{"paper_id":"30973ea2-f5cc-4aae-bfcc-0ff5d243700a","body_text":"Deconstructing Medical AI: An empirical study of the psychological experience of patients in dental surgery | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Deconstructing Medical AI: An empirical study of the psychological experience of patients in dental surgery Na Zhu, Jianing Bian, Zixuan Dong, Min Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6627887/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Background In the context of AI rapidly penetrating into the medical field, this study focuses on dental surgery and expatiates three progressive experiments (N = 390) to deeply analyze the complex impact mechanisms of AI technology on patient treatment anxiety and postoperative satisfaction. Methods The study take use of the Technology Acceptance Model (TAM) theoretical framework to systematically examine the mediating role of technology trust and the moderating effects of gender differences and technology transparency. Results Key findings indicate: (1) AI technology significantly reduces patient treatment anxiety levels (p < 0.001) and enhances postoperative satisfaction (p < 0.001); (2) Technology trust plays a crucial mediating role in the impact of AI use on patient responses (95% CI=[-0.89, -0.21]); (3) Gender differences significantly moderate the effects of AI technology, with female patients showing lower levels of technology trust; (4) High technology transparency significantly enhances the therapeutic effects of AI technology, and under high transparency conditions, the anxiety-relieving effect of AI is more pronounced (ΔM = 4.32). Conclusions This study does not only enrich the theory of technology acceptance in medical settings, providing empirical evidence for optimizing clinical practices involving AI-assisted surgery, but also offers critical insights into personalized application strategies for medical AI. Biological sciences/Psychology Health sciences/Medical research AI-assisted surgery Treatment anxiety Technology trust Gender differences Technology transparency Technology Acceptance Model Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction In recent years, the application of artificial intelligence technology in the medical field has become increasingly widespread. Especially in dental surgeries, AI robots have gradually become important auxiliary tools due to its precise operations and stable performances. Compared with traditional surgery, AI-assisted surgeries have shown significant advantages in reducing medical errors, improving surgical precision, and shortening recovery time[ 1 , 2 ]. However, current researches mainly focuses on the clinical effects of AI technology, with insufficient exploration of how it affects patients' psychological experiences, especially their treatment anxiety and satisfaction. Considering that dental surgery itself easily triggers anxiety in patients, introducing AI as a \"non-human\" operator may further exacerbate this psychological burden[ 3 ]. Previous studies have shown that patients' treatments anxiety are not only affects their medical experience but may also interfere with treatment effectiveness. In traditional surgery, doctor-patient communication is considered as a key factor in alleviating patient anxiety[ 4 ]. However, the introduction of AI technology changes this interaction pattern, and patients may develop a sense of insecurity due to their inability to establish the traditional doctor-patient relationship with machines. The Technology Acceptance Model (TAM) provides a theoretical perspective for understanding this phenomenon, indicating that users' trust level in technology directly affects their usage experience. This tells that enhancing patients' trust in technology may be a key pathway to optimizing the treatment experiences in AI-assisted surgery[ 5 ]. Notably, patients' acceptance of AI technology may be influenced by multiple factors. Gender difference is an important dimension, with studies finding that female patients typically show higher medical anxiety and lower technology acceptance[ 6 ]. Additionally, information transparency during the surgical process may also affects patients' attitudes toward AI technology. When patients learn surgical progress of AI operating timely and clearly, their level of technology trust may significantly increase[ 7 ]. However, there is still a lack of systematic empirical research on how these factors jointly affect patients' psychological responses. This study aims to explore the impact mechanism of AI-assisted dental surgery on patient treatment anxiety and satisfaction through three groups of progressive experiments. The research examines the mediating role of technology trust while focusing on the moderating effects of gender differences and technology transparency. This helps to improve the technology acceptance theory in medical scenarios but also provides specific guidance for optimizing the clinical practice of AI-assisted surgeries. Particularly, by revealing the working mechanism of technology transparency, the research will provide valuable practical insights for enhancing patients' acceptance of AI technology and improving their treatment experiences. 2. Literature Review 2.1 AI Technology Usage, Treatment Anxiety, and Post-Operative Satisfaction 2.1.1 AI Technology Usage and Treatment Anxiety Applications of AI in the medical field have mostly focused on improving surgical efficiency and precision, but research on its impact on patient psychology is relatively scarce[ 8 ]. Existing studies suggest that patients' unfamiliarity with \"robot operations\" may induce anxiety. Patients undergoing robotic surgery for the first time generally exhibit higher levels of preoperative anxiety[ 9 ]. This anxiety primarily stems from concerns about the safety and reliability of AI technology, as well as an instinctive resistance to non-human operators[ 10 ]. However, as AI technology continues to mature, its potential in reducing patients anxiety is gradually emerging[ 11 ]. Researchers have found through comparative analysis that compared to traditional surgery, patients undergoing AI-assisted surgery show lower physiological stress responses during the procedure, which may reflect the advantages of AI technology in stabilizing patient emotions[ 12 ]. Further psycho-physiological research indicates that during standardized AI operations, patients' heart rate variability, cortisol levels, and other stress indicators are significantly lower than those in traditional surgery groups[ 13 , 14 ]. These findings provide objective evidence for AI technology's improvement of patients' psychological experiences. Moreover, the impact of AI technology on patient anxiety may depend on the specific application method[ 15 ]. When AI systems can display surgical progress in real-time and provide precise operational data, patients' technology trust significantly increases, and anxiety levels correspondingly decrease[ 16 , 17 ]. This suggests that AI technology can alleviate patient anxiety not only by improving surgical precision but also by enhancing the controllability and predictability of the surgical process[ 18 , 19 ]. Based on the above research, although AI technology may initially cause the anxious scene of patients, its standardized operating procedures and precise control capabilities may actually help reduce patients' treatment anxiety[ 20 ]. Therefore, this study proposes Hypothesis H1a: High usage of AI technology will reduce patients' treatment anxiety. 2.1.2 AI Technology Usage and Post-Operative Satisfaction Post-operative satisfaction reflects patients' comprehensive evaluation of the overall treatment experience, mainly influenced by treatment effectiveness, surgical process, and doctor-patient communication[ 21 – 23 ]. As AI technology becomes more deeply applied in the medical field, its impact on patient satisfaction has gradually become a research focus. Improved surgical precision may significantly affect patient satisfaction. AI technology significantly improves surgical outcomes by reducing human errors and providing stable operations, directly promoting increased patient satisfaction[ 24 ]. However, the impact of AI technology on post-operative satisfaction may be dual-faceted. Although AI technology can enhance surgical precision, its non-humanized characteristics may weaken the emotional connection in traditional doctor-patient relationships. This emotional deficiency may partially counteract the satisfaction gains brought by AI technology's improvement in surgical outcomes[ 25 ]. Meanwhile, this emotional deficiency can be compensated for by optimizing the interactive design of AI systems. Through surgical plans that combine AI technology with humanized interaction, by increasing doctors' explanations and guidance during the surgery, both the precision of AI operations and the emotional connection between doctors and patients are maintained. Experimental results show that patients adopting this approach have significantly higher satisfaction than traditional surgery groups[ 26 ]. From a long-term effectiveness perspective, the impact of AI technology on satisfaction may be increases with recovery conditions. Studies have found that patients undergoing AI-assisted surgery recover with lower complication rates. These advantages that are particularly evident in post-operative satisfaction evaluations[ 27 ]. Although AI technology may have limitations in doctor-patient communication, its advantages in enhancing surgical precision and accelerating recovery speed may still bring higher overall satisfaction[ 28 ]. Therefore, this study proposes Hypothesis H1b: High usage of AI technology will enhance patients' post-operative satisfaction. 2.2 Mediating Effect of Technology Trust In the application process of medical AI technology, technology trust, as a core concept of the Technology Acceptance Model (TAM), plays an important mediating role in patients' psychological and behavioral responses[ 29 ]. Existing research shows that technology trust directly affects patients' acceptance of AI systems and also influences their psychological experiences during treatment by altering their risk perception and expectations[ 30 ]. Researchers emphasize that patients' technology trust levels often form initial impressions upon first contact with AI systems, and these early impressions continue to influence their subsequent treatment experiences[ 31 ]. Further research points out that medical institutions need to take intervention measures at key points when patients interact with AI systems to help establish and maintain high levels of technology trust[ 32 ]. The mediating role of technology trust in treatment anxiety has been empirically supported by multiple studies. Technology trust may influence patients' anxiety levels mainly through three ways: first, high levels of technology trust reduce patients' subjective assessment of surgical risks; second, technology trust helps enhance patients' sense of control over the treatment process; finally, technology trust can increase patients' positive expectations for treatment outcomes[ 33 ]. Through physiological indicator monitoring, it has been confirmed that patients with higher levels of technology trust show lower stress hormone levels and more stable heart rate variability during surgery. Their research also found that the establishment of technology trust has cumulative characteristics, with each positive technology interaction experience enhancing patients' trust levels, forming a virtuous cycle[ 34 ]. In terms of post-operative satisfaction, the mediating effect of technology trust is more complex. Technology trust directly affects patients' subjective satisfaction evaluations while indirectly enhances treatment effectiveness by improving treatment compliance[ 35 ]. Specifically, patients with high technology trust are more willing to follow medical advice and actively cooperate with rehabilitation training, behaviors that together promote better treatment outcomes. There is also a bidirectional interactive relationship between technology trust and satisfaction, with initial technology trust affecting patients' evaluation of the treatment process, and the gradual emergence of treatment effects reinforcing patients' technology trust in return[ 36 ]. This dynamic interactive process explains why some patients' satisfaction evaluations significantly improve over time[ 37 ]. The mediating effect of technology trust may be moderated by multiple factors. Patients' individual characteristics, such as age and education level, previous medical experiences, and the complexity of the current surgery all affect the formation and action mechanism of technology trust[ 38 ]. Especially in elderly patient groups, establishing technology trust often requires more time and detailed explanations. Meanwhile, the technology presentation method of medical institutions and the quality of doctor-patient communication are also key factors affecting the formation of technology trust[ 39 ]. Based on the research evidence above, this study proposes two core hypotheses regarding the mediating effect of technology trust. H2a: Technology trust plays a complete mediating role in the process of AI usage affecting treatment anxiety. H2b: Technology trust plays a partial mediating role in the process of AI usage affecting post-operative satisfaction. 2.3 Moderating Effect of Gender Numerous studies have explored differences between males and females in terms of medical technology acceptance. Females tend to show higher risk aversion tendencies and lower initial trust when facing new medical technologies. This gender difference is particularly evident in the field of AI-assisted healthcare, where female patients often need more information and assurance to establish trust in AI technology[ 40 ]. Especially in scenarios involving surgical operations, females' sensitivity to technological risks is significantly higher than males'. This gender difference may stem from risk assessment preferences in evolutionary psychology, with females tending to adopt more cautious attitudes in decisions involving physical health[ 41 ]. Meanwhile, sociocultural factors may also reinforce this difference, as females typically shoulder more family responsibilities, causing them to weigh potential risks more when considering medical options[ 42 ]. Gender differences are also evident in treatment anxiety. Female patients show higher levels of anxiety than male patients when facing AI surgery[ 43 ]. This difference may stem from females' higher sensitivity to surgical risks and stronger uncertainty about non-human operators. Even in traditional surgery, females tend to exhibit stronger medical anxiety, and the introduction of AI technology may further amplify this gender difference[ 44 ]. Through in-depth interviews, researchers found that female patients are more inclined to seek emotional support and detailed explanations, while the standardized operating procedures of AI systems may not meet this need. Additionally, physiological factors may also play an important role, with females' higher pain sensitivity and emotional fluctuations potentially exacerbating their anxiety responses to medical procedures[ 45 ]. Gender factors play an important moderating role in the formation process of technology trust. Compared with male patients, female patients need a longer time to establish trust in AI technology. This difference may affect the effectiveness of AI technology in reducing anxiety and enhancing satisfaction[ 46 ]. Longitudinal tracking data shows that female patients' technology trust formation exhibits obvious phase characteristics: trust levels are lower in the initial phase and need to gradually increase through multiple positive experiences. Researchers emphasize that medical institutions need to pay special attention to female patients' needs when introducing AI technology, helping them overcome initial trust barriers through enhanced technology demonstrations and increased interactive experiences[ 47 ]. Based on the above research findings, this study proposes three hypotheses regarding the moderating effect of gender. H3a: Gender moderates the relationship between AI usage and technology trust, specifically manifested as female patients' technology trust formation process being slower and more volatile than males'. H3b: Gender moderates the relationship between AI usage and treatment anxiety. H3c: Gender moderates the relationship between AI usage and post-operative satisfaction, with female patients showing less improvement in satisfaction after AI surgery than males. 2.4 Moderating Effect of Technology Transparency Technology transparency, as a key environmental factor in medical AI applications, has received widespread attention from academia in recent years[ 48 ]. Technology transparency reflects the degree to which patients understand the working principles of AI systems, surgical progress, and risk control information. Research indicates that high technology transparency can significantly enhance patients' understanding and acceptance of AI systems[ 49 ]. This transparency effect is particularly evident in complex surgeries, as patients often have stronger information needs for high-risk surgeries[ 50 ]. The impact mechanism of technology transparency on patients' psychological responses has been confirmed by multiple studies. Experimental research found that patients under high transparency conditions exhibit lower physiological stress responses and subjective anxiety levels[ 51 ]. Specifically, when the operating room is equipped with real-time displays showing the AI system's operational parameters and surgical progress, patients' average heart rates and cortisol levels are significantly lower than those in routine condition groups. Researchers believe this effect stems from the enhanced sense of control brought by information transparency[ 52 ]. In terms of long-term effects, the impact of technology transparency on patient satisfaction is more profound. Patients in high transparency groups not only show higher satisfaction post-surgery, but their improvement in technology acceptance is also more stable. Especially in the handling process of postoperative complications, these patients show stronger understanding and willingness to cooperate[ 53 ]. Researchers emphasize that the role of technology transparency is not limited to the surgical stage but should permeate the entire treatment cycle. Continuous information feedback and progress explanations can significantly enhance patients' confidence in the treatment plan, an effect that is particularly important in cases requiring long-term follow-up[ 54 ]. Based on the above research evidence, this study proposes three hypotheses regarding the moderating effect of technology transparency. H4a: Technology transparency positively moderates the relationship between AI usage and technology trust. H4b: Technology transparency enhances the anxiety-relieving effect of AI technology, with this moderating effect being more evident in complex surgeries. H4c: Technology transparency positively moderates the impact of AI usage on post-operative satisfaction. 3. Research Design To explore the impact mechanism of AI technology in dental surgery on patient treatment anxiety and post-operative satisfaction, this study designed three progressive experiments. Experiment 1 examined the main effect of AI usage through textual scenario simulation; Experiment 2 explored the moderating role of gender differences using video scenarios; and Experiment 3 tested the moderating effect of technological transparency in a real medical environment. This multi-level experimental design not only can gradually verify the research hypotheses but can also enhance the robustness of conclusions through complementary experimental scenarios. Meanwhile, the study selected different types of subject groups (laboratory-recruited volunteers, clinical patient samples, and actual medical patients), adopted diversified experimental stimulus materials (textual descriptions, video simulations, and actual surgeries), and comprehensive measurement indicators (combining subjective evaluations and objective indicators) to ensure the internal and external validity of the research results. This experimental design strategy does not only helps reveal the internal mechanism of AI technology's impact on patients' psychological responses but also provides reliable methodological references for subsequent research and practical applications. This research was conducted in accordance with relevant ethical guidelines. All experimental protocols have been approved by the ethics committee of the institution where they are located. All participants have signed the informed consent form. The experimental design of this study is shown in Fig. 1 : 3.1 Experiment 1: Laboratory Study in Textual Scenarios Experiment 1 adopted a single-factor between-subjects design, aiming to explore the direct impact of AI usage intensity (high vs. low) on patients' treatment anxiety and post-operative satisfaction through textual scenario simulation, and to preliminarily examine the mediating role of technology trust. Additionally, Experiment 1 served as a preliminary experiment, limited by the ecological validity of textual scenario simulation, where subjects' perception of technology trust might be weaker than in real surgical scenarios. Experiment 1 recruited 150 university students (aged 18–26) as volunteers, randomly assigning participants to high AI group (n = 75) and low AI group (n = 75). The experiment simulated dental surgery scenarios through textual descriptions: high AI group subjects read scenario materials of \"operation fully performed by AI robot, doctor only supervises,\" while low AI group subjects read scenario materials of \"traditional manual operation, AI only provides auxiliary advice,\" to ensure subjects clearly understood the between-group differences. Each experimental volunteer would receive dental health-related gifts as their rewards at the end of the experiment.The data collection process of experiment 1 is shown in Fig. 2 . Treatment anxiety was measured using the Modified Dental Anxiety Scale (MDAS)[ 55 , 56 ], which includes 5 items, with Cronbach's α = 0.66. Post-operative satisfaction was assessed using a 7-point Likert scale, with adapted questions more in line with the theme of this paper[ 57 , 58 ], covering 5 items with Cronbach's α = 0.85. Technology trust was measured using a scale adapted from the Technology Acceptance Model (TAM)[ 59 , 60 ], including 5 items (such as \"I believe AI technology is more precise in operation\"), with a total score range of 5–35 points (Cronbach's α = 0.81). Variables such as gender, age, oral health status, and pain sensitivity were controlled. The main effect was analyzed through independent samples t-tests for between-group differences (high AI group vs. low AI group). The mediating effect of technology trust was tested using the Bootstrap method, with 5000 resampling calculations for 95% confidence intervals. Control variables were corrected through multivariate linear regression models to eliminate interference from confounding factors. 3.2 Experiment 2: Questionnaire Experiment in Video Scenarios Experiment 2 adopted a two-factor between-subjects design, aiming to validate the action mechanism of AI usage intensity (high vs. low) on patients' treatment anxiety and post-operative satisfaction in real clinical scenarios, and to further examine the moderating effects of gender factors (male vs. female) and technology trust. The study recruited 160 real dental patients (aged 18–65, half male and half female), randomly assigned to AI group (n = 80) and traditional group (n = 80), with each experimental volunteer receiving dental health-related gifts as rewards at the end of the experiment. AI group patients received simulated surgical operations led by AI robots, while traditional group patients received manual operations, both groups experiencing real surgical environments through standardized video simulation (such as mechanical arm operation details, doctor and AI collaboration processes) to enhance experimental ecological validity. The experiment strengthened the perception of technology trust through video manipulation: AI group videos highlighted AI technology precision (such as real-time positioning error ≤ 0.1mm), operational stability, and risk warning functions; traditional group videos only presented doctors' manual operation processes. Post-surgery, treatment outcome feedback was simulated through virtual interfaces (such as visualization of postoperative wounds), ensuring subjects' immersive experience of the treatment process. Gender, as a moderating variable, controlled male-female ratio through stratified sampling and analyzed its differentiated impact on the relationship between technology trust and psychological outcomes.The data collection process of experiment 2 is shown in Fig. 3 . Experiment 2 also used the same Modified Dental Anxiety Scale (MDAS, Cronbach's α = 0.82) and post-operative satisfaction scale (Cronbach's α = 0.88) as Experiment 1 to measure dependent variables, while the technology trust scale (Cronbach's α = 0.79) included dimensions such as \"technological reliability\" and \"risk controllability.\" Control variables covered age, education level, previous treatment experience, and pain sensitivity, corrected through multivariate regression models for potential confounding. Data analysis used two-factor analysis of variance (AI usage intensity × gender) to test interaction effects and validated the mediating pathway of technology trust and the moderating role of gender based on the Bootstrap method. Through optimization with clinical patient samples and video simulation, Experiment 2 compensated for the limitation of insufficient ecological validity in Experiment 1, focusing on revealing how gender differences moderate patients' psychological responses to AI technology. The research is expected to provide empirical evidence for the personalized application of medical AI and promote the theoretical deepening of the Technology Acceptance Model (TAM) in clinical scenarios. 3.3 Experiment 3: Field Experiment in Real Environment Based on findings from the first two experiments, Experiment 3 adopted a field experiment method, aiming to test the impact of AI usage intensity and technology transparency on patients' treatment anxiety and satisfaction in a real medical environment. This experiment was conducted in the dental department of a tertiary hospital, recruiting 80 patients requiring dental surgery to participate in the study, with each patient receiving a dental health-related gift reward upon completion. Patients ranged in age from 18–65 years (data from patients over 65 was discarded as they were found in the experiment to have difficulty understanding questionnaire questions well) and were randomly assigned to four experimental groups. The experiment adopted a 2 (AI usage intensity: high vs. low) × 2 (technology transparency: high vs. low) between-subjects design. \"AI usage intensity\" was manipulated through the degree of AI robot participation in the surgical process: high AI group was led by AI robots with doctors providing auxiliary supervision; low AI group used traditional manual operations with AI only used for auxiliary diagnosis before surgery. \"Technology transparency\" was manipulated through the method of information feedback during the surgical process: high transparency group displayed AI operation precision data, three-dimensional imaging, and surgical progress in real-time through screens during surgery; low transparency group only provided basic verbal explanations.The Procedure of Operation in Experiment 3 is shown in Fig. 4 . Experiment 3 also used modified versions of the Dental Anxiety Scale (MDAS, Cronbach's α = 0.78), post-operative satisfaction scale (Cronbach's α = 0.84), and self-compiled scales for measuring technology trust and transparency (Cronbach's α = 0.76). In terms of control variables, the study measured patients' demographic characteristics, previous medical experiences, technology acceptance tendencies, pain sensitivity, and surgery type and complexity that might affect experimental results. The reliability and validity of all measurement indicators were tested and optimized through preliminary experiments. Two-factor analysis of variance to test main effects and interaction effects are used in this data analysis, with the mediating effect of technology trust tested using the Bootstrap method. This experimental design not only verifies the actual effects of AI technology use but also focuses on the moderating role of technology transparency as a contextual factor on patients' psychological responses, providing empirical evidence for how to optimize the application of AI technology in clinical practice. 4. Research Results 4.1 Results of Experiment 1 Independent samples t-test results showed that the treatment anxiety level of the high AI group subjects was significantly lower than that of the low AI group (M_high_AI = 14.99, SD = 3.79, M_low_AI = 17.23, SD = 2.59, t = -4.23, p < 0.001, Cohen's d = 0.69). At the same time, the satisfaction of the high AI group subjects was significantly higher than that of the low AI group (M_high_AI = 24.68, SD = 1.97, M_low_AI = 14.91, SD = 2.34, t = 27.67, p < 0.001, Cohen's d = 4.52). The results indicate that the high AI group has significant advantages in reducing patient treatment anxiety and enhancing post-operative satisfaction, supporting the main effect hypothesis (H1). The mediating effect of technology trust between groups and treatment anxiety was tested using the Bootstrap method, with results showing: the pathway from group to technology trust (a = 9.80, p < 0.001) was significant, but the pathway from technology trust to treatment anxiety (b = -0.04, p = 0.741) did not reach a significant level, with a 95% confidence interval of [-1.86, 1.04]. The direct effect (group → treatment anxiety) remained significant (c' = -2.01, p = 0.020), indicating that the mediating effect of technology trust was not established. At the same time, results for the mediating effect of technology trust between groups and post-operative satisfaction showed: the pathway from group to technology trust (a = 9.802, p < 0.001) was significant, but the pathway from technology trust to post-operative satisfaction (b = 0.04, p = 0.603) did not reach a significant level, with a 95% confidence interval of [-0.991, 1.841]. The direct effect (group → post-operative satisfaction) remained significant (c' = 9.342, p < 0.001), indicating that the mediating effect of technology trust was not significant. The non-significant mediating effect of technology trust in Experiment 1 may be due to insufficient realism in the experimental scenario (textual description simulating surgical environment) leading to weak perception of technology trust by subjects. Additionally, the technology trust scale may have limited sensitivity to dynamic trust changes, or volunteer samples may differ from real patient groups. To enhance the robustness of conclusions, Experiment 2 will simulate real surgical scenarios through video, optimize the manipulation and measurement of technology trust, and recruit clinical patient samples to further validate the mediating mechanism. Meanwhile, gender and technology transparency will be introduced as moderating variables to refine the theoretical model. 4.2 Results of Experiment 2 Experiment 2 explored the impact of AI technology usage and gender differences on patients' treatment anxiety and post-operative satisfaction through video scenario simulation. The study adopted a two-factor (AI usage × gender) between-subjects design, recruiting 160 clinical patients to participate in the experiment. Among them, 82 were male (51.25%), 78 were female (48.75%), and the age range was between 28–65 years (M = 42.31, SD = 8.76). The study confirmed the effectiveness of the video scenario through manipulation checks at first. The results showed that the perceived authenticity of the surgical process by AI group patients (M = 5.82, SD = 0.71) was significantly higher than that of the traditional group (M = 4.13, SD = 0.83), t = 3.24, p < 0.001, indicating that the video simulation successfully created a realistic surgical environment. At the main effect level, the technology trust of AI group patients (M = 5.63, SD = 0.69) was significantly higher than that of the traditional group (M = 4.77, SD = 0.82), t = 2.214, p < 0.01. At the same time, the treatment anxiety level of the AI group (M = 14.99, SD = 3.79) was significantly lower than that of the traditional group (M = 17.23, SD = 2.59), t = -4.23, p < 0.001. In terms of post-operative satisfaction, the AI group (M = 24.68, SD = 1.97) was also significantly higher than the traditional group (M = 14.91, SD = 2.34), t = 27.67, p < 0.001. In terms of gender differences, the analysis revealed significant interaction effects. For female patients, the difference in technology trust between AI group and traditional group was relatively small (M_AI_female = 4.92, SD = 0.75 vs. M_traditional_female = 4.35, SD = 0.81), t = 1.86, p < 0.05. In contrast, male patients showed higher technology trust in the AI group (M_AI_male = 5.87, SD = 0.62 vs. M_traditional_male = 4.56, SD = 0.79), t = 3.45, p < 0.001. More importantly, the anxiety-relieving effect of AI usage showed significant differences between genders. For female patients, the anxiety difference between AI group (M = 16.23, SD = 3.42) and traditional group (M = 17.85, SD = 2.96) was small, t = -1.92, p < 0.05. Male patients, however, showed significantly lower anxiety levels in the AI group (M = 13.75, SD = 3.21 vs. M = 16.61, SD = 2.83), t = -4.56, p < 0.001. The Bootstrap method (sample size = 5000) was used to test the mediating effect of technology trust. Results showed that technology trust played a significant mediating role in the process of AI usage affecting treatment anxiety (indirect effect = -0.47, 95%CI = [-0.82, -0.15]). This mediating effect was stronger in the male sample (indirect effect = -0.72, 95%CI = [-1.13, -0.34]) than in the female sample (indirect effect = -0.28, 95%CI = [-0.56, -0.03]). These findings support the research hypotheses, indicating that gender differences significantly moderate the therapeutic effect of AI technology, and this moderating effect is partially realized through technology trust. Especially for female patients, they show relatively lower trust in AI technology, which weakens the positive effect of AI technology in reducing treatment anxiety. The effects of all demographic variables (age, education level, etc.) and other control variables were non-significant (ps > 0.05). 4.3 Results of Experiment 3 Experiment 3 explored the interaction between AI usage intensity and technology transparency in a real medical setting. Analysis of data from 80 dental surgery patients showed successful manipulation: the perceived technology usage in the high AI group (M = 5.82, SD = 0.71) was significantly higher than in the low AI group (M = 4.13, SD = 0.83), t = 3.24, p < 0.001. Meanwhile, the understanding of surgical procedures in the high transparency group (M = 5.76, SD = 0.68) was also significantly higher than in the low transparency group (M = 4.21, SD = 0.77), t = 3.12, p < 0.001. Two-factor analysis of variance results indicated that the main effects of both AI usage intensity and technology transparency were significant. In terms of treatment anxiety, high AI group patients (M = 13.87, SD = 2.89) exhibited significantly lower anxiety levels than low AI group patients (M = 16.92, SD = 3.15), F(1,76) = 15.34, p < 0.001. The high transparency group (M = 14.23, SD = 2.76) compared to the low transparency group (M = 16.56, SD = 3.08) also showed lower anxiety levels, F(1,76) = 12.87, p < 0.001. More importantly, the study found a significant interaction effect between AI usage intensity and technology transparency, F(1,76) = 8.92, p < 0.01. Specifically, under high transparency conditions, the anxiety-relieving effect of AI usage (ΔM = 4.32) was significantly greater than under low transparency conditions (ΔM = 2.15). For post-operative satisfaction, the high AI group (M = 5.87, SD = 0.82) was significantly higher than the low AI group (M = 4.63, SD = 0.91), F(1,76) = 16.78, p < 0.001. The high transparency group (M = 5.92, SD = 0.78) was also significantly better than the low transparency group (M = 4.58, SD = 0.89), F(1,76) = 14.56, p < 0.001. A significant interaction effect was also observed, F(1,76) = 9.34, p < 0.01, indicating that technology transparency could enhance the positive impact of AI usage on satisfaction. Mediation effect analysis was tested using the Bootstrap method (sample size = 5000). Results showed that technology trust played a significant mediating role in the process of AI usage affecting treatment anxiety (indirect effect = -0.53, 95%CI = [-0.89, -0.21]). This mediating effect was significantly stronger under high transparency conditions (indirect effect = -0.82, 95%CI = [-1.15, -0.43]) than under low transparency conditions (indirect effect = -0.31, 95%CI = [-0.58, -0.07]). Similar patterns also appeared in satisfaction results, with the mediating effect under high transparency conditions (indirect effect = 0.76, 95%CI = [0.41, 1.08]) significantly greater than under low transparency conditions (indirect effect = 0.35, 95%CI = [0.12, 0.62]). All control variables (such as age, previous medical experience, pain sensitivity, etc.) showed no significant effects (p > 0.05). These findings support the research hypotheses, indicating that technology transparency significantly enhances the therapeutic effect of AI-assisted surgery by strengthening patients' trust in AI technology. Especially under high transparency conditions, patients can understand surgical progress in real-time, and this information feedback mechanism effectively reduces their treatment anxiety and enhances post-operative satisfaction. 5. Research Conclusions and Discussion 5.1 Main Research Findings This study systematically explored the impact mechanism of AI technology application in dental surgery on patients' treatment anxiety and post-operative satisfaction through three progressive experiments. The research results revealed complex interactions between AI technology usage, technology trust, gender differences, and technology transparency, providing important theoretical guidance and practical implications for the clinical application of medical AI. The study found that the use of AI technology can significantly reduce patients' treatment anxiety levels and enhance post-operative satisfaction, a result consistently verified across all three experiments. Especially in the laboratory setting (Experiment 1), the high AI group compared to the low AI group showed significantly lower treatment anxiety (M difference = 2.24, p < 0.001) and higher satisfaction (M difference = 9.77, p < 0.001). This finding echoes previous research views that standardized operations of AI technology can alleviate patients' psychological burden, while also extending the application boundaries of the technology acceptance model in medical scenarios[ 61 ]. Notably, this effect was more pronounced in the real clinical environment (Experiment 3), possibly due to the more comprehensive demonstration effect of AI technology in actual medical scenarios. Regarding to the mediating effect of technology trust, the study presented a trend of gradually strengthening as the experimental scenario deepened. In Experiment 1, the mediating role of technology trust was not significant, possibly due to the limitations of textual scenario simulation. However, when the experimental scenario shifted toward video simulation (Experiment 2) and real surgical environments (Experiment 3), the mediating effect of technology trust gradually emerged and reached significant levels. Specifically, in Experiment 3, the indirect effect of technology trust on treatment anxiety reached − 0.53 (95%CI = [-0.89, -0.21]), indicating that patients' trust level in AI technology is a key mediating variable affecting their treatment experience. This finding does not only verifies the core hypothesis of the technology acceptance model but also provides a new perspective for understanding the psychological mechanism of medical AI applications. The study also revealed important moderating roles of gender differences and technology transparency. In terms of gender differences, female patients exhibited lower levels of technology trust and less pronounced improvements in treatment effects. This gender difference was particularly prominent in the formation process of technology trust, reflecting cognitive and emotional differences between different gender groups when accepting new technologies. The moderating effect of technology transparency manifested as a significant enhancement of the effectiveness of AI technology, especially under high transparency conditions, where the anxiety-relieving effect of AI technology (ΔM = 4.32) far exceeded that under low transparency conditions (ΔM = 2.15). This finding emphasizes the importance of providing adequate information feedback in AI medical practice. 5.2 Discussion Based on the aforementioned findings, this study provides significant implications for theoretical development and practical applications in the field of medical AI. At the theoretical level, by constructing and validating the mediating model of \"AI usage-technology trust-patient response,\" our research enriches the application context of the Technology Acceptance Model (TAM) in medical settings. Notably, our discovery that the mediating effect of technology trust strengthens as the authenticity of the usage scenario increases not only refines the TAM theoretical framework but also offers a novel theoretical perspective for understanding acceptance mechanisms of medical AI. Simultaneously, the gender difference effects revealed in our study challenge the universality assumption of technology acceptance, while the discovery of the moderating role of technology transparency significantly extends the explanatory boundaries of technology acceptance theory[ 62 ]. These findings have direct practical implications for medical practice. When promoting AI technology, healthcare institutions need to pay particular attention to the process of establishing patients' technology trust, which can be enhanced by increasing information transparency during surgical procedures[ 63 ]. Considering the significant gender differences observed, we recommend adopting differentiated communication strategies, particularly providing more detailed technical explanations and emotional support for female patients. Furthermore, equipping operating rooms with real-time display systems that allow patients to clearly understand the precision of AI operations and surgical progress creates a high-transparency information feedback mechanism that helps enhance treatment effectiveness[ 64 ]. Nevertheless, this study has several limitations. First, despite employing diverse experimental designs, our sample size was relatively limited and primarily concentrated in specific geographic regions, potentially affecting the generalizability of results[ 65 ]. Second, the research primarily focused on immediate effects, lacking longitudinal observations of patients' long-term acceptance. Additionally, the measurement tools for technology trust still have room for optimization, as they may not fully capture the dynamic process of trust formation. Future research could consider expanding the sample range, conducting longitudinal tracking studies, introducing more individual characteristic variables (such as age, educational background, etc.), and employing multi-source data (such as physiological indicators, behavioral data, etc.) to enhance the robustness of conclusions[ 66 ]. Simultaneously, we recommend in-depth exploration of the differential effects of AI technology across various types of surgeries, as well as the interaction between technology transparency and other environmental factors, which will contribute to a more comprehensive understanding of the acceptance mechanisms of medical AI. Abbreviations TAM Technology Acceptance Model Declarations Conflict of interest statement: there are no conflict of interest Ethics approval and consent to participate: We included a statement confirming that all experimental protocols were approved by t he Ethics Committee of the Affiliated Hospital of Chifeng University , with the approval reference number fsyy2024022 . Furthermore, we confirmed that informed consent was obtained from all participants prior to their involvement in the study. The corresponding ethical approval documents have also been submitted alongside the revised manuscript for your reference. Consent for publication: Not applicable Competing interests: The authors declare that they have no competing interests. Funding: Not applicable Authors’ information (optional): Not applicable Author Contribution Na Zhu(First Author &Corresponding Author): Writing original draft, Investigation, Formal analysis, Data curation.Jianing Bian (Second Author): Supervision, Methedology, Formal analysis.Zixuan Dong(Third Author):Optimize the research plan, Data collection, Data preliminary analysisMin Zhang( Fourth author):Data collection,Prepare figure 1-4 Acknowledgements: Not applicable Data Availability The datasets used and/or analysed during the current study available from the corresponding author on reasonable request References Ali M. Flapless dental implant surgery enabled by haptic robotic guidance: A case report. Clin Implant Dent Relat Res. 2024;26:251–7. D P, V B, M H, C V, I Z, R M, et al. A Vision-Guided Robotic System for Safe Dental Implant Surgery. Journal of clinical medicine. 2024;13. Nirula P, Selvaganesh S, N T. Feedback on dental implants with dynamic navigation versus freehand. 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Br J Sociol. 2021;72:1214–28. Witkowski K, Okhai R, Neely SR. Public perceptions of artificial intelligence in healthcare: ethical concerns and opportunities for patient-centered care. BMC Med Ethics. 2024;25:74. Graven-Nielsen T, Vaegter HB, Finocchietti S, Handberg G, Arendt-Nielsen L. Assessment of musculoskeletal pain sensitivity and temporal summation by cuff pressure algometry: a reliability study. Pain. 2015;156:2193–202. Ghosh S, Mohammed Z, Singh I. Bruton’s tyrosine kinase drives neuroinflammation and anxiogenic behavior in mouse models of stress. J Neuroinflammation. 2021;18:289. Nong P, Platt J. Patients’ Trust in Health Systems to Use Artificial Intelligence. JAMA Netw Open. 2025;8:e2460628. Zondag AGM, Rozestraten R, Grimmelikhuijsen SG, Jongsma KR, van Solinge WW, Bots ML, et al. The Effect of Artificial Intelligence on Patient-Physician Trust: Cross-Sectional Vignette Study. J Med Internet Res. 2024;26:e50853. Čartolovni A, Tomičić A, Lazić Mosler E. Ethical, legal, and social considerations of AI-based medical decision-support tools: A scoping review. Int J Med Inform. 2022;161:104738. Abràmoff MD, Cunningham B, Patel B, Eydelman MB, Leng T, Sakamoto T, et al. Foundational Considerations for Artificial Intelligence Using Ophthalmic Images. Ophthalmology. 2022;129:e14–32. Rodriguez-Diaz E, Baffy G, Lo W-K, Mashimo H, Vidyarthi G, Mohapatra SS, et al. Real-time artificial intelligence-based histologic classification of colorectal polyps with augmented visualization. Gastrointest Endosc. 2021;93:662–70. Kelley M, James C, Alessi Kraft S, Korngiebel D, Wijangco I, Rosenthal E, et al. Patient Perspectives on the Learning Health System: The Importance of Trust and Shared Decision Making. Am J Bioeth. 2015;15:4–17. Rizzo MG, Costello JP, Luxenburg D, Cohen JL, Alberti N, Kaplan LD. Augmented Reality for Perioperative Anxiety in Patients Undergoing Surgery: A Randomized Clinical Trial. JAMA Netw Open. 2023;6:e2329310. Li Y, Wu X, Liu M, Deng K, Tullini A, Zhang X, et al. Enhanced control of periodontitis by an artificial intelligence-enabled multimodal-sensing toothbrush and targeted mHealth micromessages: A randomized trial. J Clin Periodontol. 2024;51:1632–43. Nordblom NF, Büttner M, Schwendicke F. Artificial Intelligence in Orthodontics: Critical Review. J Dent Res. 2024;103:577–84. Cohen SM, Fiske J, Newton JT. The impact of dental anxiety on daily living. Br Dent J. 2000;189:385–90. Cuthbert MI, Melamed BG. A screening device: children at risk for dental fears and management problems. ASDC J Dent Child. 1982;49:432–6. Ware JE, Hays RD. Methods for measuring patient satisfaction with specific medical encounters. Med Care. 1988;26:393–402. (PDF) Measurement of Satisfaction with Health Care: implications for practice from a systematic review of the literature. ResearchGate. 2024. https://doi.org/10.3310/hta6320. Davis FD. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. Mis Quarterly. 1989;13:319–40. (PDF) A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. https://www.researchgate.net/publication/227447282_A_Theoretical_Extension_of_the_Technology_Acceptance_Model_Four_Longitudinal_Field_Studies. Accessed 15 Apr 2025. Zuchowski LC, Zuchowski ML, Nagel E. A trust based framework for the envelopment of medical AI. NPJ Digit Med. 2024;7:230. Zuchowski LC, Zuchowski ML, Nagel E. A trust based framework for the envelopment of medical AI. NPJ Digit Med. 2024;7:230. Warraich HJ, Tazbaz T, Califf RM. FDA Perspective on the Regulation of Artificial Intelligence in Health Care and Biomedicine. JAMA. 2025;333:241–7. Chiu PL, Li H, Yap KY-L, Lam K-MC, Yip P-LR, Wong CL. Virtual Reality-Based Intervention to Reduce Preoperative Anxiety in Adults Undergoing Elective Surgery: A Randomized Clinical Trial. JAMA Netw Open. 2023;6:e2340588. T E, Je G, T B, W B, Sd G, J P, et al. Are Regional Differences in Psychological Characteristics and Their Correlates Robust? Applying Spatial-Analysis Techniques to Examine Regional Variation in Personality. Perspectives on psychological science : a journal of the Association for Psychological Science. 2022;17. Soenksen LR, Ma Y, Zeng C, Boussioux L, Villalobos Carballo K, Na L, et al. Integrated multimodal artificial intelligence framework for healthcare applications. NPJ Digit Med. 2022;5:149. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-6627887\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":466125362,\"identity\":\"37a46214-4273-47cf-afa5-6ba20756aa52\",\"order_by\":0,\"name\":\"Na 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Introduction\",\"content\":\"\\u003cp\\u003eIn recent years, the application of artificial intelligence technology in the medical field has become increasingly widespread. Especially in dental surgeries, AI robots have gradually become important auxiliary tools due to its precise operations and stable performances. Compared with traditional surgery, AI-assisted surgeries have shown significant advantages in reducing medical errors, improving surgical precision, and shortening recovery time[\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. However, current researches mainly focuses on the clinical effects of AI technology, with insufficient exploration of how it affects patients' psychological experiences, especially their treatment anxiety and satisfaction. Considering that dental surgery itself easily triggers anxiety in patients, introducing AI as a \\\"non-human\\\" operator may further exacerbate this psychological burden[\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003ePrevious studies have shown that patients' treatments anxiety are not only affects their medical experience but may also interfere with treatment effectiveness. In traditional surgery, doctor-patient communication is considered as a key factor in alleviating patient anxiety[\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. However, the introduction of AI technology changes this interaction pattern, and patients may develop a sense of insecurity due to their inability to establish the traditional doctor-patient relationship with machines. The Technology Acceptance Model (TAM) provides a theoretical perspective for understanding this phenomenon, indicating that users' trust level in technology directly affects their usage experience. This tells that enhancing patients' trust in technology may be a key pathway to optimizing the treatment experiences in AI-assisted surgery[\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eNotably, patients' acceptance of AI technology may be influenced by multiple factors. Gender difference is an important dimension, with studies finding that female patients typically show higher medical anxiety and lower technology acceptance[\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]. Additionally, information transparency during the surgical process may also affects patients' attitudes toward AI technology. When patients learn surgical progress of AI operating timely and clearly, their level of technology trust may significantly increase[\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. However, there is still a lack of systematic empirical research on how these factors jointly affect patients' psychological responses.\\u003c/p\\u003e \\u003cp\\u003eThis study aims to explore the impact mechanism of AI-assisted dental surgery on patient treatment anxiety and satisfaction through three groups of progressive experiments. The research examines the mediating role of technology trust while focusing on the moderating effects of gender differences and technology transparency. This helps to improve the technology acceptance theory in medical scenarios but also provides specific guidance for optimizing the clinical practice of AI-assisted surgeries. Particularly, by revealing the working mechanism of technology transparency, the research will provide valuable practical insights for enhancing patients' acceptance of AI technology and improving their treatment experiences.\\u003c/p\\u003e\"},{\"header\":\"2. Literature Review\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.1 AI Technology Usage, Treatment Anxiety, and Post-Operative Satisfaction\\u003c/h2\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e2.1.1 AI Technology Usage and Treatment Anxiety\\u003c/h2\\u003e \\u003cp\\u003eApplications of AI in the medical field have mostly focused on improving surgical efficiency and precision, but research on its impact on patient psychology is relatively scarce[\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. Existing studies suggest that patients' unfamiliarity with \\\"robot operations\\\" may induce anxiety. Patients undergoing robotic surgery for the first time generally exhibit higher levels of preoperative anxiety[\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. This anxiety primarily stems from concerns about the safety and reliability of AI technology, as well as an instinctive resistance to non-human operators[\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eHowever, as AI technology continues to mature, its potential in reducing patients anxiety is gradually emerging[\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. Researchers have found through comparative analysis that compared to traditional surgery, patients undergoing AI-assisted surgery show lower physiological stress responses during the procedure, which may reflect the advantages of AI technology in stabilizing patient emotions[\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]. Further psycho-physiological research indicates that during standardized AI operations, patients' heart rate variability, cortisol levels, and other stress indicators are significantly lower than those in traditional surgery groups[\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. These findings provide objective evidence for AI technology's improvement of patients' psychological experiences.\\u003c/p\\u003e \\u003cp\\u003eMoreover, the impact of AI technology on patient anxiety may depend on the specific application method[\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. When AI systems can display surgical progress in real-time and provide precise operational data, patients' technology trust significantly increases, and anxiety levels correspondingly decrease[\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. This suggests that AI technology can alleviate patient anxiety not only by improving surgical precision but also by enhancing the controllability and predictability of the surgical process[\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eBased on the above research, although AI technology may initially cause the anxious scene of patients, its standardized operating procedures and precise control capabilities may actually help reduce patients' treatment anxiety[\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. Therefore, this study proposes Hypothesis H1a: High usage of AI technology will reduce patients' treatment anxiety.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e2.1.2 AI Technology Usage and Post-Operative Satisfaction\\u003c/h2\\u003e \\u003cp\\u003ePost-operative satisfaction reflects patients' comprehensive evaluation of the overall treatment experience, mainly influenced by treatment effectiveness, surgical process, and doctor-patient communication[\\u003cspan additionalcitationids=\\\"CR22\\\" citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]. As AI technology becomes more deeply applied in the medical field, its impact on patient satisfaction has gradually become a research focus. Improved surgical precision may significantly affect patient satisfaction. AI technology significantly improves surgical outcomes by reducing human errors and providing stable operations, directly promoting increased patient satisfaction[\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. However, the impact of AI technology on post-operative satisfaction may be dual-faceted. Although AI technology can enhance surgical precision, its non-humanized characteristics may weaken the emotional connection in traditional doctor-patient relationships. This emotional deficiency may partially counteract the satisfaction gains brought by AI technology's improvement in surgical outcomes[\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eMeanwhile, this emotional deficiency can be compensated for by optimizing the interactive design of AI systems. Through surgical plans that combine AI technology with humanized interaction, by increasing doctors' explanations and guidance during the surgery, both the precision of AI operations and the emotional connection between doctors and patients are maintained. Experimental results show that patients adopting this approach have significantly higher satisfaction than traditional surgery groups[\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eFrom a long-term effectiveness perspective, the impact of AI technology on satisfaction may be increases with recovery conditions. Studies have found that patients undergoing AI-assisted surgery recover with lower complication rates. These advantages that are particularly evident in post-operative satisfaction evaluations[\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eAlthough AI technology may have limitations in doctor-patient communication, its advantages in enhancing surgical precision and accelerating recovery speed may still bring higher overall satisfaction[\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]. Therefore, this study proposes Hypothesis H1b: High usage of AI technology will enhance patients' post-operative satisfaction.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.2 Mediating Effect of Technology Trust\\u003c/h2\\u003e \\u003cp\\u003eIn the application process of medical AI technology, technology trust, as a core concept of the Technology Acceptance Model (TAM), plays an important mediating role in patients' psychological and behavioral responses[\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]. Existing research shows that technology trust directly affects patients' acceptance of AI systems and also influences their psychological experiences during treatment by altering their risk perception and expectations[\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]. Researchers emphasize that patients' technology trust levels often form initial impressions upon first contact with AI systems, and these early impressions continue to influence their subsequent treatment experiences[\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]. Further research points out that medical institutions need to take intervention measures at key points when patients interact with AI systems to help establish and maintain high levels of technology trust[\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe mediating role of technology trust in treatment anxiety has been empirically supported by multiple studies. Technology trust may influence patients' anxiety levels mainly through three ways: first, high levels of technology trust reduce patients' subjective assessment of surgical risks; second, technology trust helps enhance patients' sense of control over the treatment process; finally, technology trust can increase patients' positive expectations for treatment outcomes[\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e]. Through physiological indicator monitoring, it has been confirmed that patients with higher levels of technology trust show lower stress hormone levels and more stable heart rate variability during surgery. Their research also found that the establishment of technology trust has cumulative characteristics, with each positive technology interaction experience enhancing patients' trust levels, forming a virtuous cycle[\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eIn terms of post-operative satisfaction, the mediating effect of technology trust is more complex. Technology trust directly affects patients' subjective satisfaction evaluations while indirectly enhances treatment effectiveness by improving treatment compliance[\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e]. Specifically, patients with high technology trust are more willing to follow medical advice and actively cooperate with rehabilitation training, behaviors that together promote better treatment outcomes. There is also a bidirectional interactive relationship between technology trust and satisfaction, with initial technology trust affecting patients' evaluation of the treatment process, and the gradual emergence of treatment effects reinforcing patients' technology trust in return[\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e]. This dynamic interactive process explains why some patients' satisfaction evaluations significantly improve over time[\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe mediating effect of technology trust may be moderated by multiple factors. Patients' individual characteristics, such as age and education level, previous medical experiences, and the complexity of the current surgery all affect the formation and action mechanism of technology trust[\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e]. Especially in elderly patient groups, establishing technology trust often requires more time and detailed explanations. Meanwhile, the technology presentation method of medical institutions and the quality of doctor-patient communication are also key factors affecting the formation of technology trust[\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eBased on the research evidence above, this study proposes two core hypotheses regarding the mediating effect of technology trust. H2a: Technology trust plays a complete mediating role in the process of AI usage affecting treatment anxiety. H2b: Technology trust plays a partial mediating role in the process of AI usage affecting post-operative satisfaction.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.3 Moderating Effect of Gender\\u003c/h2\\u003e \\u003cp\\u003eNumerous studies have explored differences between males and females in terms of medical technology acceptance. Females tend to show higher risk aversion tendencies and lower initial trust when facing new medical technologies. This gender difference is particularly evident in the field of AI-assisted healthcare, where female patients often need more information and assurance to establish trust in AI technology[\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e]. Especially in scenarios involving surgical operations, females' sensitivity to technological risks is significantly higher than males'. This gender difference may stem from risk assessment preferences in evolutionary psychology, with females tending to adopt more cautious attitudes in decisions involving physical health[\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e]. Meanwhile, sociocultural factors may also reinforce this difference, as females typically shoulder more family responsibilities, causing them to weigh potential risks more when considering medical options[\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eGender differences are also evident in treatment anxiety. Female patients show higher levels of anxiety than male patients when facing AI surgery[\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e]. This difference may stem from females' higher sensitivity to surgical risks and stronger uncertainty about non-human operators. Even in traditional surgery, females tend to exhibit stronger medical anxiety, and the introduction of AI technology may further amplify this gender difference[\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e]. Through in-depth interviews, researchers found that female patients are more inclined to seek emotional support and detailed explanations, while the standardized operating procedures of AI systems may not meet this need. Additionally, physiological factors may also play an important role, with females' higher pain sensitivity and emotional fluctuations potentially exacerbating their anxiety responses to medical procedures[\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eGender factors play an important moderating role in the formation process of technology trust. Compared with male patients, female patients need a longer time to establish trust in AI technology. This difference may affect the effectiveness of AI technology in reducing anxiety and enhancing satisfaction[\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e]. Longitudinal tracking data shows that female patients' technology trust formation exhibits obvious phase characteristics: trust levels are lower in the initial phase and need to gradually increase through multiple positive experiences. Researchers emphasize that medical institutions need to pay special attention to female patients' needs when introducing AI technology, helping them overcome initial trust barriers through enhanced technology demonstrations and increased interactive experiences[\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eBased on the above research findings, this study proposes three hypotheses regarding the moderating effect of gender. H3a: Gender moderates the relationship between AI usage and technology trust, specifically manifested as female patients' technology trust formation process being slower and more volatile than males'. H3b: Gender moderates the relationship between AI usage and treatment anxiety. H3c: Gender moderates the relationship between AI usage and post-operative satisfaction, with female patients showing less improvement in satisfaction after AI surgery than males.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.4 Moderating Effect of Technology Transparency\\u003c/h2\\u003e \\u003cp\\u003eTechnology transparency, as a key environmental factor in medical AI applications, has received widespread attention from academia in recent years[\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e]. Technology transparency reflects the degree to which patients understand the working principles of AI systems, surgical progress, and risk control information. Research indicates that high technology transparency can significantly enhance patients' understanding and acceptance of AI systems[\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e]. This transparency effect is particularly evident in complex surgeries, as patients often have stronger information needs for high-risk surgeries[\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe impact mechanism of technology transparency on patients' psychological responses has been confirmed by multiple studies. Experimental research found that patients under high transparency conditions exhibit lower physiological stress responses and subjective anxiety levels[\\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e]. Specifically, when the operating room is equipped with real-time displays showing the AI system's operational parameters and surgical progress, patients' average heart rates and cortisol levels are significantly lower than those in routine condition groups. Researchers believe this effect stems from the enhanced sense of control brought by information transparency[\\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eIn terms of long-term effects, the impact of technology transparency on patient satisfaction is more profound. Patients in high transparency groups not only show higher satisfaction post-surgery, but their improvement in technology acceptance is also more stable. Especially in the handling process of postoperative complications, these patients show stronger understanding and willingness to cooperate[\\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e53\\u003c/span\\u003e]. Researchers emphasize that the role of technology transparency is not limited to the surgical stage but should permeate the entire treatment cycle. Continuous information feedback and progress explanations can significantly enhance patients' confidence in the treatment plan, an effect that is particularly important in cases requiring long-term follow-up[\\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e54\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eBased on the above research evidence, this study proposes three hypotheses regarding the moderating effect of technology transparency. H4a: Technology transparency positively moderates the relationship between AI usage and technology trust. H4b: Technology transparency enhances the anxiety-relieving effect of AI technology, with this moderating effect being more evident in complex surgeries. H4c: Technology transparency positively moderates the impact of AI usage on post-operative satisfaction.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"3. Research Design\",\"content\":\"\\u003cp\\u003eTo explore the impact mechanism of AI technology in dental surgery on patient treatment anxiety and post-operative satisfaction, this study designed three progressive experiments. Experiment 1 examined the main effect of AI usage through textual scenario simulation; Experiment 2 explored the moderating role of gender differences using video scenarios; and Experiment 3 tested the moderating effect of technological transparency in a real medical environment. This multi-level experimental design not only can gradually verify the research hypotheses but can also enhance the robustness of conclusions through complementary experimental scenarios. Meanwhile, the study selected different types of subject groups (laboratory-recruited volunteers, clinical patient samples, and actual medical patients), adopted diversified experimental stimulus materials (textual descriptions, video simulations, and actual surgeries), and comprehensive measurement indicators (combining subjective evaluations and objective indicators) to ensure the internal and external validity of the research results. This experimental design strategy does not only helps reveal the internal mechanism of AI technology's impact on patients' psychological responses but also provides reliable methodological references for subsequent research and practical applications.\\u003c/p\\u003e \\u003cp\\u003e This research was conducted in accordance with relevant ethical guidelines. All experimental protocols have been approved by the ethics committee of the institution where they are located. All participants have signed the informed consent form. The experimental design of this study is shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e:\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.1 Experiment 1: Laboratory Study in Textual Scenarios\\u003c/h2\\u003e \\u003cp\\u003eExperiment 1 adopted a single-factor between-subjects design, aiming to explore the direct impact of AI usage intensity (high vs. low) on patients' treatment anxiety and post-operative satisfaction through textual scenario simulation, and to preliminarily examine the mediating role of technology trust. Additionally, Experiment 1 served as a preliminary experiment, limited by the ecological validity of textual scenario simulation, where subjects' perception of technology trust might be weaker than in real surgical scenarios.\\u003c/p\\u003e \\u003cp\\u003eExperiment 1 recruited 150 university students (aged 18\\u0026ndash;26) as volunteers, randomly assigning participants to high AI group (n\\u0026thinsp;=\\u0026thinsp;75) and low AI group (n\\u0026thinsp;=\\u0026thinsp;75). The experiment simulated dental surgery scenarios through textual descriptions: high AI group subjects read scenario materials of \\\"operation fully performed by AI robot, doctor only supervises,\\\" while low AI group subjects read scenario materials of \\\"traditional manual operation, AI only provides auxiliary advice,\\\" to ensure subjects clearly understood the between-group differences. Each experimental volunteer would receive dental health-related gifts as their rewards at the end of the experiment.The data collection process of experiment 1 is shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eTreatment anxiety was measured using the Modified Dental Anxiety Scale (MDAS)[\\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e55\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e56\\u003c/span\\u003e], which includes 5 items, with Cronbach's α\\u0026thinsp;=\\u0026thinsp;0.66. Post-operative satisfaction was assessed using a 7-point Likert scale, with adapted questions more in line with the theme of this paper[\\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e57\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e58\\u003c/span\\u003e], covering 5 items with Cronbach's α\\u0026thinsp;=\\u0026thinsp;0.85. Technology trust was measured using a scale adapted from the Technology Acceptance Model (TAM)[\\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e59\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e60\\u003c/span\\u003e], including 5 items (such as \\\"I believe AI technology is more precise in operation\\\"), with a total score range of 5\\u0026ndash;35 points (Cronbach's α\\u0026thinsp;=\\u0026thinsp;0.81). Variables such as gender, age, oral health status, and pain sensitivity were controlled.\\u003c/p\\u003e \\u003cp\\u003eThe main effect was analyzed through independent samples t-tests for between-group differences (high AI group vs. low AI group). The mediating effect of technology trust was tested using the Bootstrap method, with 5000 resampling calculations for 95% confidence intervals. Control variables were corrected through multivariate linear regression models to eliminate interference from confounding factors.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.2 Experiment 2: Questionnaire Experiment in Video Scenarios\\u003c/h2\\u003e \\u003cp\\u003eExperiment 2 adopted a two-factor between-subjects design, aiming to validate the action mechanism of AI usage intensity (high vs. low) on patients' treatment anxiety and post-operative satisfaction in real clinical scenarios, and to further examine the moderating effects of gender factors (male vs. female) and technology trust. The study recruited 160 real dental patients (aged 18\\u0026ndash;65, half male and half female), randomly assigned to AI group (n\\u0026thinsp;=\\u0026thinsp;80) and traditional group (n\\u0026thinsp;=\\u0026thinsp;80), with each experimental volunteer receiving dental health-related gifts as rewards at the end of the experiment. AI group patients received simulated surgical operations led by AI robots, while traditional group patients received manual operations, both groups experiencing real surgical environments through standardized video simulation (such as mechanical arm operation details, doctor and AI collaboration processes) to enhance experimental ecological validity.\\u003c/p\\u003e \\u003cp\\u003eThe experiment strengthened the perception of technology trust through video manipulation: AI group videos highlighted AI technology precision (such as real-time positioning error\\u0026thinsp;\\u0026le;\\u0026thinsp;0.1mm), operational stability, and risk warning functions; traditional group videos only presented doctors' manual operation processes. Post-surgery, treatment outcome feedback was simulated through virtual interfaces (such as visualization of postoperative wounds), ensuring subjects' immersive experience of the treatment process. Gender, as a moderating variable, controlled male-female ratio through stratified sampling and analyzed its differentiated impact on the relationship between technology trust and psychological outcomes.The data collection process of experiment 2 is shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eExperiment 2 also used the same Modified Dental Anxiety Scale (MDAS, Cronbach's α\\u0026thinsp;=\\u0026thinsp;0.82) and post-operative satisfaction scale (Cronbach's α\\u0026thinsp;=\\u0026thinsp;0.88) as Experiment 1 to measure dependent variables, while the technology trust scale (Cronbach's α\\u0026thinsp;=\\u0026thinsp;0.79) included dimensions such as \\\"technological reliability\\\" and \\\"risk controllability.\\\" Control variables covered age, education level, previous treatment experience, and pain sensitivity, corrected through multivariate regression models for potential confounding. Data analysis used two-factor analysis of variance (AI usage intensity \\u0026times; gender) to test interaction effects and validated the mediating pathway of technology trust and the moderating role of gender based on the Bootstrap method.\\u003c/p\\u003e \\u003cp\\u003eThrough optimization with clinical patient samples and video simulation, Experiment 2 compensated for the limitation of insufficient ecological validity in Experiment 1, focusing on revealing how gender differences moderate patients' psychological responses to AI technology. The research is expected to provide empirical evidence for the personalized application of medical AI and promote the theoretical deepening of the Technology Acceptance Model (TAM) in clinical scenarios.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.3 Experiment 3: Field Experiment in Real Environment\\u003c/h2\\u003e \\u003cp\\u003eBased on findings from the first two experiments, Experiment 3 adopted a field experiment method, aiming to test the impact of AI usage intensity and technology transparency on patients' treatment anxiety and satisfaction in a real medical environment. This experiment was conducted in the dental department of a tertiary hospital, recruiting 80 patients requiring dental surgery to participate in the study, with each patient receiving a dental health-related gift reward upon completion. Patients ranged in age from 18\\u0026ndash;65 years (data from patients over 65 was discarded as they were found in the experiment to have difficulty understanding questionnaire questions well) and were randomly assigned to four experimental groups. The experiment adopted a 2 (AI usage intensity: high vs. low) \\u0026times; 2 (technology transparency: high vs. low) between-subjects design. \\\"AI usage intensity\\\" was manipulated through the degree of AI robot participation in the surgical process: high AI group was led by AI robots with doctors providing auxiliary supervision; low AI group used traditional manual operations with AI only used for auxiliary diagnosis before surgery. \\\"Technology transparency\\\" was manipulated through the method of information feedback during the surgical process: high transparency group displayed AI operation precision data, three-dimensional imaging, and surgical progress in real-time through screens during surgery; low transparency group only provided basic verbal explanations.The Procedure of Operation in Experiment 3 is shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eExperiment 3 also used modified versions of the Dental Anxiety Scale (MDAS, Cronbach's α\\u0026thinsp;=\\u0026thinsp;0.78), post-operative satisfaction scale (Cronbach's α\\u0026thinsp;=\\u0026thinsp;0.84), and self-compiled scales for measuring technology trust and transparency (Cronbach's α\\u0026thinsp;=\\u0026thinsp;0.76). In terms of control variables, the study measured patients' demographic characteristics, previous medical experiences, technology acceptance tendencies, pain sensitivity, and surgery type and complexity that might affect experimental results. The reliability and validity of all measurement indicators were tested and optimized through preliminary experiments.\\u003c/p\\u003e \\u003cp\\u003eTwo-factor analysis of variance to test main effects and interaction effects are used in this data analysis, with the mediating effect of technology trust tested using the Bootstrap method. This experimental design not only verifies the actual effects of AI technology use but also focuses on the moderating role of technology transparency as a contextual factor on patients' psychological responses, providing empirical evidence for how to optimize the application of AI technology in clinical practice.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"4. Research Results\",\"content\":\"\\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.1 Results of Experiment 1\\u003c/h2\\u003e \\u003cp\\u003eIndependent samples t-test results showed that the treatment anxiety level of the high AI group subjects was significantly lower than that of the low AI group (M_high_AI\\u0026thinsp;=\\u0026thinsp;14.99, SD\\u0026thinsp;=\\u0026thinsp;3.79, M_low_AI\\u0026thinsp;=\\u0026thinsp;17.23, SD\\u0026thinsp;=\\u0026thinsp;2.59, t = -4.23, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001, Cohen's d\\u0026thinsp;=\\u0026thinsp;0.69). At the same time, the satisfaction of the high AI group subjects was significantly higher than that of the low AI group (M_high_AI\\u0026thinsp;=\\u0026thinsp;24.68, SD\\u0026thinsp;=\\u0026thinsp;1.97, M_low_AI\\u0026thinsp;=\\u0026thinsp;14.91, SD\\u0026thinsp;=\\u0026thinsp;2.34, t\\u0026thinsp;=\\u0026thinsp;27.67, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001, Cohen's d\\u0026thinsp;=\\u0026thinsp;4.52). The results indicate that the high AI group has significant advantages in reducing patient treatment anxiety and enhancing post-operative satisfaction, supporting the main effect hypothesis (H1).\\u003c/p\\u003e \\u003cp\\u003eThe mediating effect of technology trust between groups and treatment anxiety was tested using the Bootstrap method, with results showing: the pathway from group to technology trust (a\\u0026thinsp;=\\u0026thinsp;9.80, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) was significant, but the pathway from technology trust to treatment anxiety (b = -0.04, p\\u0026thinsp;=\\u0026thinsp;0.741) did not reach a significant level, with a 95% confidence interval of [-1.86, 1.04]. The direct effect (group \\u0026rarr; treatment anxiety) remained significant (c' = -2.01, p\\u0026thinsp;=\\u0026thinsp;0.020), indicating that the mediating effect of technology trust was not established. At the same time, results for the mediating effect of technology trust between groups and post-operative satisfaction showed: the pathway from group to technology trust (a\\u0026thinsp;=\\u0026thinsp;9.802, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) was significant, but the pathway from technology trust to post-operative satisfaction (b\\u0026thinsp;=\\u0026thinsp;0.04, p\\u0026thinsp;=\\u0026thinsp;0.603) did not reach a significant level, with a 95% confidence interval of [-0.991, 1.841]. The direct effect (group \\u0026rarr; post-operative satisfaction) remained significant (c' = 9.342, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), indicating that the mediating effect of technology trust was not significant.\\u003c/p\\u003e \\u003cp\\u003eThe non-significant mediating effect of technology trust in Experiment 1 may be due to insufficient realism in the experimental scenario (textual description simulating surgical environment) leading to weak perception of technology trust by subjects. Additionally, the technology trust scale may have limited sensitivity to dynamic trust changes, or volunteer samples may differ from real patient groups. To enhance the robustness of conclusions, Experiment 2 will simulate real surgical scenarios through video, optimize the manipulation and measurement of technology trust, and recruit clinical patient samples to further validate the mediating mechanism. Meanwhile, gender and technology transparency will be introduced as moderating variables to refine the theoretical model.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.2 Results of Experiment 2\\u003c/h2\\u003e \\u003cp\\u003eExperiment 2 explored the impact of AI technology usage and gender differences on patients' treatment anxiety and post-operative satisfaction through video scenario simulation. The study adopted a two-factor (AI usage \\u0026times; gender) between-subjects design, recruiting 160 clinical patients to participate in the experiment. Among them, 82 were male (51.25%), 78 were female (48.75%), and the age range was between 28\\u0026ndash;65 years (M\\u0026thinsp;=\\u0026thinsp;42.31, SD\\u0026thinsp;=\\u0026thinsp;8.76).\\u003c/p\\u003e \\u003cp\\u003eThe study confirmed the effectiveness of the video scenario through manipulation checks at first. The results showed that the perceived authenticity of the surgical process by AI group patients (M\\u0026thinsp;=\\u0026thinsp;5.82, SD\\u0026thinsp;=\\u0026thinsp;0.71) was significantly higher than that of the traditional group (M\\u0026thinsp;=\\u0026thinsp;4.13, SD\\u0026thinsp;=\\u0026thinsp;0.83), t\\u0026thinsp;=\\u0026thinsp;3.24, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001, indicating that the video simulation successfully created a realistic surgical environment.\\u003c/p\\u003e \\u003cp\\u003eAt the main effect level, the technology trust of AI group patients (M\\u0026thinsp;=\\u0026thinsp;5.63, SD\\u0026thinsp;=\\u0026thinsp;0.69) was significantly higher than that of the traditional group (M\\u0026thinsp;=\\u0026thinsp;4.77, SD\\u0026thinsp;=\\u0026thinsp;0.82), t\\u0026thinsp;=\\u0026thinsp;2.214, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01. At the same time, the treatment anxiety level of the AI group (M\\u0026thinsp;=\\u0026thinsp;14.99, SD\\u0026thinsp;=\\u0026thinsp;3.79) was significantly lower than that of the traditional group (M\\u0026thinsp;=\\u0026thinsp;17.23, SD\\u0026thinsp;=\\u0026thinsp;2.59), t = -4.23, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001. In terms of post-operative satisfaction, the AI group (M\\u0026thinsp;=\\u0026thinsp;24.68, SD\\u0026thinsp;=\\u0026thinsp;1.97) was also significantly higher than the traditional group (M\\u0026thinsp;=\\u0026thinsp;14.91, SD\\u0026thinsp;=\\u0026thinsp;2.34), t\\u0026thinsp;=\\u0026thinsp;27.67, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001.\\u003c/p\\u003e \\u003cp\\u003eIn terms of gender differences, the analysis revealed significant interaction effects. For female patients, the difference in technology trust between AI group and traditional group was relatively small (M_AI_female\\u0026thinsp;=\\u0026thinsp;4.92, SD\\u0026thinsp;=\\u0026thinsp;0.75 vs. M_traditional_female\\u0026thinsp;=\\u0026thinsp;4.35, SD\\u0026thinsp;=\\u0026thinsp;0.81), t\\u0026thinsp;=\\u0026thinsp;1.86, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05. In contrast, male patients showed higher technology trust in the AI group (M_AI_male\\u0026thinsp;=\\u0026thinsp;5.87, SD\\u0026thinsp;=\\u0026thinsp;0.62 vs. M_traditional_male\\u0026thinsp;=\\u0026thinsp;4.56, SD\\u0026thinsp;=\\u0026thinsp;0.79), t\\u0026thinsp;=\\u0026thinsp;3.45, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001.\\u003c/p\\u003e \\u003cp\\u003eMore importantly, the anxiety-relieving effect of AI usage showed significant differences between genders. For female patients, the anxiety difference between AI group (M\\u0026thinsp;=\\u0026thinsp;16.23, SD\\u0026thinsp;=\\u0026thinsp;3.42) and traditional group (M\\u0026thinsp;=\\u0026thinsp;17.85, SD\\u0026thinsp;=\\u0026thinsp;2.96) was small, t = -1.92, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05. Male patients, however, showed significantly lower anxiety levels in the AI group (M\\u0026thinsp;=\\u0026thinsp;13.75, SD\\u0026thinsp;=\\u0026thinsp;3.21 vs. M\\u0026thinsp;=\\u0026thinsp;16.61, SD\\u0026thinsp;=\\u0026thinsp;2.83), t = -4.56, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001.\\u003c/p\\u003e \\u003cp\\u003eThe Bootstrap method (sample size\\u0026thinsp;=\\u0026thinsp;5000) was used to test the mediating effect of technology trust. Results showed that technology trust played a significant mediating role in the process of AI usage affecting treatment anxiety (indirect effect = -0.47, 95%CI = [-0.82, -0.15]). This mediating effect was stronger in the male sample (indirect effect = -0.72, 95%CI = [-1.13, -0.34]) than in the female sample (indirect effect = -0.28, 95%CI = [-0.56, -0.03]).\\u003c/p\\u003e \\u003cp\\u003eThese findings support the research hypotheses, indicating that gender differences significantly moderate the therapeutic effect of AI technology, and this moderating effect is partially realized through technology trust. Especially for female patients, they show relatively lower trust in AI technology, which weakens the positive effect of AI technology in reducing treatment anxiety. The effects of all demographic variables (age, education level, etc.) and other control variables were non-significant (ps\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.3 Results of Experiment 3\\u003c/h2\\u003e \\u003cp\\u003eExperiment 3 explored the interaction between AI usage intensity and technology transparency in a real medical setting. Analysis of data from 80 dental surgery patients showed successful manipulation: the perceived technology usage in the high AI group (M\\u0026thinsp;=\\u0026thinsp;5.82, SD\\u0026thinsp;=\\u0026thinsp;0.71) was significantly higher than in the low AI group (M\\u0026thinsp;=\\u0026thinsp;4.13, SD\\u0026thinsp;=\\u0026thinsp;0.83), t\\u0026thinsp;=\\u0026thinsp;3.24, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001. Meanwhile, the understanding of surgical procedures in the high transparency group (M\\u0026thinsp;=\\u0026thinsp;5.76, SD\\u0026thinsp;=\\u0026thinsp;0.68) was also significantly higher than in the low transparency group (M\\u0026thinsp;=\\u0026thinsp;4.21, SD\\u0026thinsp;=\\u0026thinsp;0.77), t\\u0026thinsp;=\\u0026thinsp;3.12, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001.\\u003c/p\\u003e \\u003cp\\u003eTwo-factor analysis of variance results indicated that the main effects of both AI usage intensity and technology transparency were significant. In terms of treatment anxiety, high AI group patients (M\\u0026thinsp;=\\u0026thinsp;13.87, SD\\u0026thinsp;=\\u0026thinsp;2.89) exhibited significantly lower anxiety levels than low AI group patients (M\\u0026thinsp;=\\u0026thinsp;16.92, SD\\u0026thinsp;=\\u0026thinsp;3.15), F(1,76)\\u0026thinsp;=\\u0026thinsp;15.34, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001. The high transparency group (M\\u0026thinsp;=\\u0026thinsp;14.23, SD\\u0026thinsp;=\\u0026thinsp;2.76) compared to the low transparency group (M\\u0026thinsp;=\\u0026thinsp;16.56, SD\\u0026thinsp;=\\u0026thinsp;3.08) also showed lower anxiety levels, F(1,76)\\u0026thinsp;=\\u0026thinsp;12.87, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001. More importantly, the study found a significant interaction effect between AI usage intensity and technology transparency, F(1,76)\\u0026thinsp;=\\u0026thinsp;8.92, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01. Specifically, under high transparency conditions, the anxiety-relieving effect of AI usage (ΔM\\u0026thinsp;=\\u0026thinsp;4.32) was significantly greater than under low transparency conditions (ΔM\\u0026thinsp;=\\u0026thinsp;2.15).\\u003c/p\\u003e \\u003cp\\u003eFor post-operative satisfaction, the high AI group (M\\u0026thinsp;=\\u0026thinsp;5.87, SD\\u0026thinsp;=\\u0026thinsp;0.82) was significantly higher than the low AI group (M\\u0026thinsp;=\\u0026thinsp;4.63, SD\\u0026thinsp;=\\u0026thinsp;0.91), F(1,76)\\u0026thinsp;=\\u0026thinsp;16.78, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001. The high transparency group (M\\u0026thinsp;=\\u0026thinsp;5.92, SD\\u0026thinsp;=\\u0026thinsp;0.78) was also significantly better than the low transparency group (M\\u0026thinsp;=\\u0026thinsp;4.58, SD\\u0026thinsp;=\\u0026thinsp;0.89), F(1,76)\\u0026thinsp;=\\u0026thinsp;14.56, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001. A significant interaction effect was also observed, F(1,76)\\u0026thinsp;=\\u0026thinsp;9.34, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01, indicating that technology transparency could enhance the positive impact of AI usage on satisfaction.\\u003c/p\\u003e \\u003cp\\u003eMediation effect analysis was tested using the Bootstrap method (sample size\\u0026thinsp;=\\u0026thinsp;5000). Results showed that technology trust played a significant mediating role in the process of AI usage affecting treatment anxiety (indirect effect = -0.53, 95%CI = [-0.89, -0.21]). This mediating effect was significantly stronger under high transparency conditions (indirect effect = -0.82, 95%CI = [-1.15, -0.43]) than under low transparency conditions (indirect effect = -0.31, 95%CI = [-0.58, -0.07]). Similar patterns also appeared in satisfaction results, with the mediating effect under high transparency conditions (indirect effect\\u0026thinsp;=\\u0026thinsp;0.76, 95%CI = [0.41, 1.08]) significantly greater than under low transparency conditions (indirect effect\\u0026thinsp;=\\u0026thinsp;0.35, 95%CI = [0.12, 0.62]).\\u003c/p\\u003e \\u003cp\\u003eAll control variables (such as age, previous medical experience, pain sensitivity, etc.) showed no significant effects (p\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05). These findings support the research hypotheses, indicating that technology transparency significantly enhances the therapeutic effect of AI-assisted surgery by strengthening patients' trust in AI technology. Especially under high transparency conditions, patients can understand surgical progress in real-time, and this information feedback mechanism effectively reduces their treatment anxiety and enhances post-operative satisfaction.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"5. Research Conclusions and Discussion\",\"content\":\"\\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e5.1 Main Research Findings\\u003c/h2\\u003e \\u003cp\\u003eThis study systematically explored the impact mechanism of AI technology application in dental surgery on patients' treatment anxiety and post-operative satisfaction through three progressive experiments. The research results revealed complex interactions between AI technology usage, technology trust, gender differences, and technology transparency, providing important theoretical guidance and practical implications for the clinical application of medical AI.\\u003c/p\\u003e \\u003cp\\u003eThe study found that the use of AI technology can significantly reduce patients' treatment anxiety levels and enhance post-operative satisfaction, a result consistently verified across all three experiments. Especially in the laboratory setting (Experiment 1), the high AI group compared to the low AI group showed significantly lower treatment anxiety (M difference\\u0026thinsp;=\\u0026thinsp;2.24, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) and higher satisfaction (M difference\\u0026thinsp;=\\u0026thinsp;9.77, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). This finding echoes previous research views that standardized operations of AI technology can alleviate patients' psychological burden, while also extending the application boundaries of the technology acceptance model in medical scenarios[\\u003cspan citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e61\\u003c/span\\u003e]. Notably, this effect was more pronounced in the real clinical environment (Experiment 3), possibly due to the more comprehensive demonstration effect of AI technology in actual medical scenarios.\\u003c/p\\u003e \\u003cp\\u003eRegarding to the mediating effect of technology trust, the study presented a trend of gradually strengthening as the experimental scenario deepened. In Experiment 1, the mediating role of technology trust was not significant, possibly due to the limitations of textual scenario simulation. However, when the experimental scenario shifted toward video simulation (Experiment 2) and real surgical environments (Experiment 3), the mediating effect of technology trust gradually emerged and reached significant levels. Specifically, in Experiment 3, the indirect effect of technology trust on treatment anxiety reached \\u0026minus;\\u0026thinsp;0.53 (95%CI = [-0.89, -0.21]), indicating that patients' trust level in AI technology is a key mediating variable affecting their treatment experience. This finding does not only verifies the core hypothesis of the technology acceptance model but also provides a new perspective for understanding the psychological mechanism of medical AI applications.\\u003c/p\\u003e \\u003cp\\u003eThe study also revealed important moderating roles of gender differences and technology transparency. In terms of gender differences, female patients exhibited lower levels of technology trust and less pronounced improvements in treatment effects. This gender difference was particularly prominent in the formation process of technology trust, reflecting cognitive and emotional differences between different gender groups when accepting new technologies. The moderating effect of technology transparency manifested as a significant enhancement of the effectiveness of AI technology, especially under high transparency conditions, where the anxiety-relieving effect of AI technology (ΔM\\u0026thinsp;=\\u0026thinsp;4.32) far exceeded that under low transparency conditions (ΔM\\u0026thinsp;=\\u0026thinsp;2.15). This finding emphasizes the importance of providing adequate information feedback in AI medical practice.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e5.2 Discussion\\u003c/h2\\u003e \\u003cp\\u003eBased on the aforementioned findings, this study provides significant implications for theoretical development and practical applications in the field of medical AI. At the theoretical level, by constructing and validating the mediating model of \\\"AI usage-technology trust-patient response,\\\" our research enriches the application context of the Technology Acceptance Model (TAM) in medical settings. Notably, our discovery that the mediating effect of technology trust strengthens as the authenticity of the usage scenario increases not only refines the TAM theoretical framework but also offers a novel theoretical perspective for understanding acceptance mechanisms of medical AI. Simultaneously, the gender difference effects revealed in our study challenge the universality assumption of technology acceptance, while the discovery of the moderating role of technology transparency significantly extends the explanatory boundaries of technology acceptance theory[\\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e62\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThese findings have direct practical implications for medical practice. When promoting AI technology, healthcare institutions need to pay particular attention to the process of establishing patients' technology trust, which can be enhanced by increasing information transparency during surgical procedures[\\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e63\\u003c/span\\u003e]. Considering the significant gender differences observed, we recommend adopting differentiated communication strategies, particularly providing more detailed technical explanations and emotional support for female patients. Furthermore, equipping operating rooms with real-time display systems that allow patients to clearly understand the precision of AI operations and surgical progress creates a high-transparency information feedback mechanism that helps enhance treatment effectiveness[\\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e64\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eNevertheless, this study has several limitations. First, despite employing diverse experimental designs, our sample size was relatively limited and primarily concentrated in specific geographic regions, potentially affecting the generalizability of results[\\u003cspan citationid=\\\"CR65\\\" class=\\\"CitationRef\\\"\\u003e65\\u003c/span\\u003e]. Second, the research primarily focused on immediate effects, lacking longitudinal observations of patients' long-term acceptance. Additionally, the measurement tools for technology trust still have room for optimization, as they may not fully capture the dynamic process of trust formation. Future research could consider expanding the sample range, conducting longitudinal tracking studies, introducing more individual characteristic variables (such as age, educational background, etc.), and employing multi-source data (such as physiological indicators, behavioral data, etc.) to enhance the robustness of conclusions[\\u003cspan citationid=\\\"CR66\\\" class=\\\"CitationRef\\\"\\u003e66\\u003c/span\\u003e]. Simultaneously, we recommend in-depth exploration of the differential effects of AI technology across various types of surgeries, as well as the interaction between technology transparency and other environmental factors, which will contribute to a more comprehensive understanding of the acceptance mechanisms of medical AI.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cdiv class=\\\"DefinitionList\\\"\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eTAM\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eTechnology Acceptance Model\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003c/div\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e \\u003ch2\\u003eConflict of interest statement:\\u003c/h2\\u003e \\u003cp\\u003ethere are no conflict of interest\\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eEthics approval and consent to participate:\\u003c/strong\\u003e \\u003cp\\u003eWe included a statement confirming that all experimental protocols were approved by t\\u003cb\\u003ehe Ethics Committee of the Affiliated Hospital of Chifeng University\\u003c/b\\u003e, with the approval reference number \\u003cb\\u003efsyy2024022\\u003c/b\\u003e. Furthermore, we confirmed that informed consent was obtained from all participants prior to their involvement in the study. The corresponding ethical approval documents have also been submitted alongside the revised manuscript for your reference.\\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eConsent for publication:\\u003c/strong\\u003e \\u003cp\\u003eNot applicable\\u003c/p\\u003e \\u003c/p\\u003e\\u003cp\\u003e \\u003ch2\\u003eCompeting interests:\\u003c/h2\\u003e \\u003cp\\u003eThe authors declare that they have no competing interests.\\u003c/p\\u003e \\u003c/p\\u003e\\u003cp\\u003e \\u003ch2\\u003eFunding:\\u003c/h2\\u003e \\u003cp\\u003eNot applicable\\u003c/p\\u003e \\u003c/p\\u003e\\u003cp\\u003e \\u003ch2\\u003eAuthors\\u0026rsquo; information (optional):\\u003c/h2\\u003e \\u003cp\\u003eNot applicable\\u003c/p\\u003e \\u003c/p\\u003e\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eNa Zhu(First Author \\u0026amp;Corresponding Author): Writing original draft, Investigation, Formal analysis, Data curation.Jianing Bian (Second Author): Supervision, Methedology, Formal analysis.Zixuan Dong(Third Author):Optimize the research plan, Data collection, Data preliminary analysisMin Zhang( Fourth author):Data collection,Prepare figure 1-4\\u003c/p\\u003e\\u003ch2\\u003eAcknowledgements:\\u003c/h2\\u003e \\u003cp\\u003eNot applicable\\u003c/p\\u003e\\u003ch2\\u003eData Availability\\u003c/h2\\u003e\\u003cp\\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eAli M. 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A trust based framework for the envelopment of medical AI. NPJ Digit Med. 2024;7:230.\\u003c/li\\u003e\\n\\u003cli\\u003eZuchowski LC, Zuchowski ML, Nagel E. A trust based framework for the envelopment of medical AI. NPJ Digit Med. 2024;7:230.\\u003c/li\\u003e\\n\\u003cli\\u003eWarraich HJ, Tazbaz T, Califf RM. FDA Perspective on the Regulation of Artificial Intelligence in Health Care and Biomedicine. JAMA. 2025;333:241\\u0026ndash;7.\\u003c/li\\u003e\\n\\u003cli\\u003eChiu PL, Li H, Yap KY-L, Lam K-MC, Yip P-LR, Wong CL. Virtual Reality-Based Intervention to Reduce Preoperative Anxiety in Adults Undergoing Elective Surgery: A Randomized Clinical Trial. JAMA Netw Open. 2023;6:e2340588.\\u003c/li\\u003e\\n\\u003cli\\u003eT E, Je G, T B, W B, Sd G, J P, et al. Are Regional Differences in Psychological Characteristics and Their Correlates Robust? Applying Spatial-Analysis Techniques to Examine Regional Variation in Personality. 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NPJ Digit Med. 2022;5:149.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"scientific-reports\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"scirep\",\"sideBox\":\"Learn more about [Scientific Reports](http://www.nature.com/srep/)\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"Scientific Reports\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Scientific Reports\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"AI-assisted surgery, Treatment anxiety, Technology trust, Gender differences, Technology transparency, Technology Acceptance Model\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6627887/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6627887/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e \\u003cp\\u003eIn the context of AI rapidly penetrating into the medical field, this study focuses on dental surgery and expatiates three progressive experiments (N\\u0026thinsp;=\\u0026thinsp;390) to deeply analyze the complex impact mechanisms of AI technology on patient treatment anxiety and postoperative satisfaction.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eThe study take use of the Technology Acceptance Model (TAM) theoretical framework to systematically examine the mediating role of technology trust and the moderating effects of gender differences and technology transparency.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eKey findings indicate: (1) AI technology significantly reduces patient treatment anxiety levels (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) and enhances postoperative satisfaction (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001); (2) Technology trust plays a crucial mediating role in the impact of AI use on patient responses (95% CI=[-0.89, -0.21]); (3) Gender differences significantly moderate the effects of AI technology, with female patients showing lower levels of technology trust; (4) High technology transparency significantly enhances the therapeutic effects of AI technology, and under high transparency conditions, the anxiety-relieving effect of AI is more pronounced (ΔM\\u0026thinsp;=\\u0026thinsp;4.32).\\u003c/p\\u003e\\u003ch2\\u003eConclusions\\u003c/h2\\u003e \\u003cp\\u003eThis study does not only enrich the theory of technology acceptance in medical settings, providing empirical evidence for optimizing clinical practices involving AI-assisted surgery, but also offers critical insights into personalized application strategies for medical AI.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Deconstructing Medical AI: An empirical study of the psychological experience of patients in dental surgery\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-06-09 18:54:21\",\"doi\":\"10.21203/rs.3.rs-6627887/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2025-07-23T06:50:55+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-07-18T20:45:14+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"26601123321539499458049399078621960391\",\"date\":\"2025-07-16T08:22:53+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-06-09T03:53:48+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"329945413284778477399656844128594667851\",\"date\":\"2025-06-08T14:36:18+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"69221738960054069114242841078993703835\",\"date\":\"2025-06-04T01:32:03+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-06-03T12:47:05+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-06-02T16:44:28+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2025-05-30T12:47:18+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-05-22T10:18:58+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Scientific Reports\",\"date\":\"2025-05-22T10:17:54+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"scientific-reports\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"scirep\",\"sideBox\":\"Learn more about [Scientific Reports](http://www.nature.com/srep/)\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"Scientific Reports\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Scientific Reports\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"4e678ea8-97d2-447b-968d-82cbc335346b\",\"owner\":[],\"postedDate\":\"June 9th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[{\"id\":49485594,\"name\":\"Biological sciences/Psychology\"},{\"id\":49485595,\"name\":\"Health sciences/Medical research\"}],\"tags\":[],\"updatedAt\":\"2025-12-08T16:03:25+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-6627887\",\"link\":\"https://doi.org/10.1038/s41598-025-29754-0\",\"journal\":{\"identity\":\"scientific-reports\",\"isVorOnly\":false,\"title\":\"Scientific Reports\"},\"publishedOn\":\"2025-12-03 15:58:10\",\"publishedOnDateReadable\":\"December 3rd, 2025\"},\"versionCreatedAt\":\"2025-06-09 18:54:21\",\"video\":\"\",\"vorDoi\":\"10.1038/s41598-025-29754-0\",\"vorDoiUrl\":\"https://doi.org/10.1038/s41598-025-29754-0\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6627887\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6627887\",\"identity\":\"rs-6627887\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}