AI Information Literacy in Healthcare Context: Profiles and Predictors Among Healthcare Professionals

AI Information Literacy in Healthcare Context: Profiles and Predictors Among Healthcare Professionals | 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 Research Article AI Information Literacy in Healthcare Context: Profiles and Predictors Among Healthcare Professionals Lilach Alon¹, Inbar Levkovich¹ This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9101750/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract This study examines AI information literacy among healthcare professionals and identify differences across professional roles. An online survey was administered to 390 healthcare professionals including 22.3% medical doctors, 42.1% nurses, and 35.6% health professionals. AI information literacy was measured using the AI Information Literacy Scale (AILIS), which captures four dimensions: process/create, assess, retrieve, and ethics. Findings indicated that across AILIS dimensions, participants reported the greatest confidence in retrieving information with AI and the least confidence in critically assessing AI-generated outputs. Medical doctors scored significantly higher than nurses and health professionals on process/create and retrieve. Across all four regression models, self-perceived understanding of how AI tools work was the strongest predictor, and the models explained between 27.3% and 48.3% of the variance in AI information literacy dimensions. Age showed small negative associations in most models. AI information literacy among healthcare professionals appears multidimensional and uneven, with relatively lower confidence in critical assessment. GenAI integration in healthcare should therefore be approached as a literacy and training challenge, with particular attention to evaluation skills and reflective understanding of AI outputs. generative artificial intelligence AI information literacy healthcare professionals ethics technology adoption Figures Figure 1 Figure 2 Figure 3 Introduction Healthcare systems are entering a period in which generative artificial intelligence (GenAI) tools are becoming increasingly visible in professional practice [ 1 ]. Large language model (LLM) based systems are now used to summarize information, draft text, explain complex concepts, support documentation, and provide rapid access to synthesized answers [ 2 – 4 ]. In health-related contexts, these tools are being explored for clinical decision support [ 5 ], communication, education, administrative work, and information seeking [ 6 ]. As a result, healthcare professionals are beginning to work in environments where AI-generated content may shape how information is accessed, interpreted, and used [ 7 , 8 ]. This shift creates new demands for healthcare professional. In healthcare, the use of AI-generated information is a high-stakes matter that requires careful judgment, responsible use, and context-sensitive interpretation [ 9 ]. Professionals must often make judgments under time pressure [ 10 ], communicate clearly with patients and colleagues [ 11 ], protect sensitive information, and distinguish between plausible outputs and trustworthy ones [ 12 ]. In such contexts, what matters is healthcare professionals’ ability to use AI critically, responsibly, and effectively. This includes retrieving relevant information, evaluating the quality of AI-generated responses, adapting outputs for professional purposes, and recognizing ethical boundaries in the use of AI tools [ 13 ]. These demands can be understood through the concept of AI information literacy. Information literacy has traditionally been defined as a set of abilities related to locating, processing, interpreting, and critically evaluating information [ 14 ]. In AI-mediated environments, these abilities remain central, but the conditions under which they are enacted have changed. Users must formulate prompts, interpret outputs that may be fluent but unverifiable, assess transparency and trustworthiness, and distinguish between human-authored and machine-generated information [ 15 – 17 ]. Accordingly, AI information literacy refers to the ability to locate, create, process, and critically evaluate information with and through AI tools while making informed and ethical decisions about their use [ 18 ]. This construct reflects how people work with AI as an information intermediary in everyday and professional practice [ 19 , 20 ]. Understanding the integration of GenAI into healthcare through an information literacy perspective is especially important because the different dimensions of AI information literacy may not develop equally. A healthcare professional may feel relatively confident using AI to retrieve information or generate drafts, yet less confident in critically evaluating output quality or in making ethical decisions about disclosure and confidentiality. A multidimensional approach therefore helps identify uneven competency profiles rather than treating AI-related competence as a single general skill. The AI Information Literacy Scale (AILIS) was developed to address these aspects by operationalizing AI information literacy as information literacy self-efficacy in GenAI-mediated information behavior [ 18 ]. Unlike broader AI literacy measures, which often emphasize conceptual understanding, attitudes, or general awareness of AI [e.g., 21,22], AILIS focuses on concrete information practices performed with GenAI tools. Grounded in information behavior and information literacy scholarship, it was designed for everyday, educational, and professional contexts. To the best of our knowledge, AI information literacy has not yet been examined in healthcare contexts. AILIS captures four dimensions that are highly relevant to healthcare use. The first, creation and processing, refers to the ability to generate, adapt, summarize, compare, and integrate AI-generated content for specific purposes, audiences, and formats [ 23 – 25 ]. The second, assessment and critique, concerns the ability to evaluate outputs for accuracy, coherence, bias, timeliness, and possible fabrication, as well as to recognize missing evidence or problematic content [ 26 ]. The third, seeking and retrieval, reflects the ability to clarify information needs, formulate effective prompts, request sources or examples, and decide when AI is an appropriate tool for a given task [ 16 ]. The fourth, ethics, addresses transparency, responsible use, attribution, and the protection of sensitive information when interacting with AI systems [ 27 , 28 ]. Overall, these dimensions move beyond undifferentiated notions of AI readiness and allow the identification of uneven literacy profiles across types of information work. Although AILIS was validated in a general adult sample, little is known about AI information literacy among healthcare professionals. In particular, it remains unclear how healthcare professionals’ AI-related literacy background is distributed, how they score across the dimensions of AI information literacy, whether these patterns differ across professional groups, and which demographic, professional, and AI-related background variables predict stronger or weaker AI information literacy in healthcare contexts. The present study addresses this gap by applying AILIS to a sample of healthcare professional and related healthcare professionals. Specifically, it examines participants’ AI-related literacy background, their scores across the four dimensions of AI information literacy, differences across professional groups, and the demographic, professional, and AI-related variables associated with AI information literacy across dimensions. We examine the following research questions: RQ1. What are healthcare professional’s self-reported levels of AI-related literacy background, including perceived AI knowledge, frequency of AI use, and number of AI tool use, and do these differ across professional groups? RQ2. How do healthcare professionals score on the identified dimensions of AI information literacy and do these differ across professional groups? RQ3. Which demographic, professional, and AI-related background variables predict healthcare professionals’ AI information literacy across these dimensions? By examining these questions, the study provides an initial healthcare-based profile of AI information literacy that may inform future research, organizational assessment, and targeted educational efforts for safer and more effective AI use in healthcare. Methods Participants The sample in this study included 390 participants from a range of healthcare professions and training stages. A sensitivity analysis conducted in G*Power 3.1 indicated that with N = 390, α = .05, and power = .80, the study was sufficiently powered to detect small-to-medium effects in the one-way ANOVAs (f = 0.16) and small effects in the multiple regression models with five predictors (f² = 0.033). Participants were recruited from the medical workforce and completed the questionnaire online in Hebrew. The sample included 58 male participants (14.9%) and 332 female participants (85.1%), with ages ranging from 23 to 68 years ( M = 40.64, SD = 10.67). Mean seniority in the medical field was 11.13 years ( SD = 11.05). In terms of professional role, the sample included 87 participants in medical roles across training and career stages (22.3%), including 28 medical students (7.2%), 13 interns (3.3%), 16 residents (4.1%), and 30 specialists or senior physicians (7.7%). Registered nurses constituted the largest group, with 164 participants (42.1%). The remaining 139 participants (35.6%) represented other health professions, including 84 allied health professionals, such as speech therapists, physiotherapists, and occupational therapists (21.5%), 45 pharmacists or laboratory staff (11.5%), and 10 participants in other roles (2.6%). Tools Data were collected using a structured questionnaire consisting of three sections. AI Information Literacy Scale (AILIS) : AI information literacy was assessed using AILIS [ 18 ], a self-report instrument that conceptualizes AI information literacy as information literacy self-efficacy in GenAI-mediated information behavior. The scale assesses perceived ability to work with AI-generated information across four dimensions: process/create, assess, retrieve, and ethics. In its original validation, AILIS was designed to capture concrete information practices performed with GenAI tools across everyday, educational, and professional contexts. The full instrument includes 39 items distributed across four subscales: process/create (16 items, e.g., “Generate titles or summaries using an AI tool”, “Convert a text into a graph or diagram using an AI tool”), assess (13 items, e.g., “Detect hallucinations and ask the tool to correct them”, “Critically edit an output generated by an AI tool”), retrieve (8 items, e.g., “Update or refine prompt after receiving an answer to improve the result”, “Ask the AI tool for examples, sources, or citations”), and ethics (2 items, e.g., “Indicate my use of AI tools in a transparent way”). Items are rated on a 5-point confidence scale ranging from 1 (not at all) to 5 (to a very great extent). In the present sample, internal consistency was high for process/create (α = .972), assess (α = .974), and retrieve (α = .959), and acceptable for ethics (α = .794). GenAI usage patterns : To characterize participants’ AI-related background, we included four self-report indicators: perceived AI knowledge, frequency of AI use, number and type of AI tools used, and self-perceived understanding of how AI tools work. Perceived AI knowledge and frequency of AI use were rated on a 5-point Likert scale ranging from 1 (“not at all”) to 5 (“to a very great extent”). Self-perceived understanding of how AI tools work was rated on a 7-point Likert scale ranging from 1 (“not at all”) to 7 (“to a very great extent”). The number and type of AI tools used were included to reflect the number of participants’ GenAI tool repertoire. Perceived AI knowledge reflected participants’ subjective familiarity with AI tools, and frequency of AI use captured how often they used them. These variables were used both to describe the sample and to predict AI information literacy scores. Demographic and professional background variables Participants also completed a brief background questionnaire that included year of birth, gender, seniority in the medical field, and professional role within the healthcare professional. Procedure and Ethics The survey was administered online via the iPanel internet panel service. Data were collected in December 2025 and January 2026. iPanel is an established Israeli online research platform for administering surveys among consenting respondents and enables the recruitment of geographically dispersed samples through its panel infrastructure. Participants were invited through iPanel’s distribution system, and the questionnaire, which included the AILIS items, GenAI knowledge and use measures, and background questions, required approximately 15 minutes to complete. Responses were collected anonymously, and iPanel’s privacy and confidentiality procedures were applied throughout the data collection process. The study was conducted in accordance with institutional and national ethical guidelines for research involving human participants. The research protocol was reviewed and approved by the Tel-Hai University Institutional Review Board (Application No. 01-10-25; approval date: October 22, 2025). Participation was voluntary, and informed consent was obtained electronically, as participants indicated their agreement on the first survey screen before proceeding. Participants were informed that their responses would be used for research purposes only, that they could discontinue participation at any time, and that no identifying information would be stored with their responses. Data were analyzed and reported in aggregate form to protect participants’ privacy. Analysis Data were analyzed using IBM SPSS Statistics (Version 31). To address RQ1, descriptive statistics were computed for the AI-related literacy background variables, including perceived AI knowledge, frequency of AI use, number of AI tools used, and self-perceived understanding of how AI tools work. To address RQ2, descriptive statistics were calculated for the four dimensions of AI information literacy: process/create, assess, retrieve, and ethics. To address RQ3, a series of multiple linear regression analyses was conducted. Each AI information literacy dimension was entered as a separate dependent variable. Age, gender, seniority in the medical field, perceived AI knowledge, frequency of AI use, number of AI tools used, and self-perceived understanding of how AI tools work were entered as predictor variables to identify the variables associated with healthcare professional’s AI information literacy across dimensions. In addition, Pearson correlation analyses were conducted to examine bivariate associations among the study variables. Results AI-related literacy background across groups To characterize participants’ AI-related background, participants reported the number and type of GenAI tools they currently use, the frequency of AI use, their perceived AI knowledge, and their self-perceived understanding of how AI tools work (Fig. 1 ). The mean number of AI tools used was 1.82 (SD = 1.03). Frequency of AI use was rated on a 1 to 5 scale, with higher scores indicating more frequent use (M = 3.02, SD = 1.17). Perceived AI knowledge was also rated on a 1 to 5 scale, with higher scores indicating greater perceived knowledge (M = 3.35, SD = 1.05). Self-perceived understanding of how AI tools work was rated on a 1 to 7 scale, with higher scores indicating greater perceived understanding (M = 3.66, SD = 1.50). Note Error bars represent standard deviations. To further examine whether AI-related literacy background differed across professional groups, a one-way ANOVA was conducted to compare medical doctors, nurses, and health professionals (Fig. 2). No statistically significant group differences were found for perceived AI knowledge [ F (2, 387) = 2.75, p = .065], or for self-perceived understanding of how AI tools work [ F (2, 387) = 0.36, p = .700]. However, significant differences emerged for frequency of AI use [ F (2, 387) = 3.97, p < .05], and number of AI tools used [ F (2, 387) = 6.38, p < .01]. Bonferroni comparisons showed that medical doctors reported more frequent AI use ( M = 3.23, SD = 1.15) than health professionals ( M = 2.81, SD = 1.13), whereas nurses did not differ significantly from either group. In contrast, health professionals reported using a larger number of AI tools ( M = 2.05, SD = 1.07) than nurses ( M = 1.63, SD = 1.02), whereas medical doctors did not differ significantly from either group. Figure 2. AI-related literacy background by professional group. Note Error bars represent standard deviations. Significant Bonferroni-adjusted differences were found for frequency of AI use, with medical doctors reporting more frequent AI use than other health professionals, and for number of AI tools used, with health professionals reporting use of a larger number of AI tools than nurses. Differences Across AI Information Literacy Dimensions Overall, participants reported moderate levels of AI information literacy across the four dimensions. The highest mean score was found for retrieve ( M = 3.04, SD = 1.18), followed by process/create ( M = 2.80, SD = 1.16), ethics ( M = 2.73, SD = 1.28), and assess ( M = 2.53, SD = 1.14). This pattern suggests that participants felt relatively more confident in using AI tools to locate and retrieve information than in critically assessing AI-generated information. To further examine whether AI information literacy differed across professional groups, a one-way ANOVA was conducted to compare medical doctors, nurses, and health professionals (Fig. 3 ). Significant group differences emerged for process/create [ F (2, 387) = 4.71, p < .05], assess [ F (2, 387) = 3.30, p < .05], and retrieve [ F (2, 387) = 4.56, p < .05]. No significant group differences were found for ethics [ F (2, 387) = 1.12, p = .329]. Bonferroni post hoc comparisons showed that medical doctors scored significantly higher than nurses and health professionals on process/create. Specifically, medical doctors scored higher than nurses ( M = 3.13 vs. 2.72, p < .05) and health professionals ( M = 3.13 vs. 2.68, p < .05). A similar pattern was found for retrieve, with medical doctors scoring higher than nurses ( M = 3.36 vs. 2.98, p < .05) and health professionals ( M = 3.36 vs. 2.90, p < .05). For assess, although the overall ANOVA was significant, Bonferroni-adjusted pairwise comparisons did not reach significance. No significant pairwise differences were found for ethics. Note Error bars represent standard deviations. Significant Bonferroni-adjusted differences were found for process/create and retrieve, with medical doctors scoring higher than nurses and other health professionals. Predictors of AI Information Literacy Across Dimensions A series of multiple linear regression analyses was conducted to examine whether demographic, professional, and AI-related background variables predicted healthcare professional’s AI information literacy across the four dimensions. The initial models included age, gender, professional group, perceived AI knowledge, frequency of AI use, number of AI tools used, and self-perceived understanding of how AI tools work. Gender and professional group were not significant predictors and were therefore omitted from the final models. For process/create, the overall model was significant [ F (5, 384) = 71.86, p < .001], and explained 48.3% of the variance. Higher scores were predicted by greater perceived AI knowledge ( β = .182, p < .01), more frequent AI use ( β = .198, p < .001), use of a larger number of AI tools ( β = .148, p < .001), and greater self-perceived understanding of how AI tools work ( β = .312, p < .001). Age was a significant negative predictor ( β = − .092, p < .05). For assess, the model was also significant [ F (5, 384) = 42.11, p < .001], explaining 35.4% of the variance. Higher assess scores were predicted by greater perceived AI knowledge ( β = .160, p < .05), use of a larger number of AI tools ( β = .128, p < .01), and greater self-perceived understanding of how AI tools work ( β = .312, p < .001). Age was again a negative predictor ( β = − .086, p < .05). Frequency of AI use did not reach significance in this model ( β = .112, p = .061). For retrieve, the model was significant [ F (5, 384) = 52.07, p < .001], and explained 40.4% of the variance. Higher retrieve scores were predicted by greater perceived AI knowledge ( β = .122, p < .05), more frequent AI use ( β = .173, p < .01), use of a larger number of AI tools ( β = .160, p < .001), and greater self-perceived understanding of how AI tools work ( β = .309, p < .001). Age was a significant negative predictor ( β = − .107, p < .05). For ethics, the model was significant [ F (5, 384) = 28.91, p < .001], explaining 27.3% of the variance. Higher ethics scores were predicted by more frequent AI use ( β = .128, p < .05), use of a larger number of AI tools ( β = .137, p < .01), and greater self-perceived understanding of how AI tools work ( β = .269, p < .001). In contrast, age ( β = − .080, p = .083) and perceived AI knowledge ( β = .097, p = .145) were not significant predictors in this model. Across all four models, self-perceived understanding of how AI tools work emerged as the strongest and most consistent predictor, while age showed a small but consistent negative association with most AI information literacy dimensions. Discussion This study examined AI information literacy among healthcare professional and related healthcare professionals by applying AILIS in a healthcare context. Three main findings emerged. First, participants reported moderate AI-related literacy background overall, including moderate perceived AI knowledge, frequency of AI use, and the number of AI tool use. Second, AI information literacy was uneven across dimensions: retrieval received the highest scores, whereas assessment received the lowest. Third, AI information literacy was more strongly associated with AI-related experience and perceived understanding than with demographic or professional characteristics. Together, these findings suggest that AI integration in healthcare should be understood as depending on multiple dimensions of AI information literacy. One central contribution of the study is showing that AI information literacy in healthcare is a multidimensional capability [ 18 ]. The uneven pattern across dimensions is theoretically meaningful because it suggests that healthcare professionals engage with AI-mediated information in different ways and with different levels of confidence, as has been found in other workforce domains [ 29 , 30 ]. In particular, they may feel more confident using AI to obtain or rework information than in judging the reliability, adequacy, or ethical implications of what AI produces [ 31 ]. This suggests that AI use in healthcare is shaped by the quality of professional judgment applied to AI-generated information once it is produced. More broadly, the findings indicate that the literacy demands of AI use in healthcare are internally differentiated and are best understood through multiple dimensions of AI information literacy [ 32 , 33 ]. The identified group differences further clarify this pattern. For healthcare organizations, this highlights the importance of supporting not only AI use, but also the development of critical and responsible information practices [ 34 ]. A second contribution of the study is the indication that AI information literacy in healthcare is shaped primarily by active engagement with AI [ 35 ]. Once AI-related experience and perceived understanding were taken into account, demographic and professional group variables contributed little to explaining variation across dimensions. The regression analyses further showed that AI information literacy was most strongly associated with AI-related engagement. Across all four dimensions, self-perceived understanding of how AI tools work was the strongest and most consistent predictor, while AI use and number of AI tools used also contributed to several models [ 36 ]. By contrast, gender and professional group did not remain significant predictors in the final models, and age showed only a small negative association with some dimensions. Overall, the current study suggests that AI information literacy in healthcare contexts varies across dimensions and is most strongly associated with active AI engagement and perceived understanding rather than demographic or professional background alone [ 18 , 21 ]. For healthcare systems adopting GenAI tools, this means that effective integration may depend not only on technological availability, but also on the literacy-related capacities of the professionals expected to use them. These findings have several implications for healthcare contexts. First, they suggest that workforce preparation for AI should not be framed only around positive attitudes or willingness to adopt innovation. What appears more central is the development of specific competencies related to retrieving, processing, evaluating, and ethically using AI-generated information. Second, the relatively lower scores on assessment point to a particularly important area for intervention. If healthcare professionals are more confident retrieving or generating information than evaluating its validity, there is a risk of overreliance on fluent but insufficiently scrutinized output. Third, the strong role of perceived understanding suggests that educational efforts may need to combine practical use with reflective understanding of how GenAI tools generate responses, where errors may arise, and how outputs should be checked in professional settings. In this sense, AI information literacy may offer a more actionable framework for healthcare training than broader and more diffuse notions of AI readiness. The study also contributes conceptually by extending AI information literacy into a healthcare setting. To the best of our knowledge, this is one of the first applications of an explicit AI information literacy framework among healthcare professional. In a field where discussions of AI often emphasize implementation, clinical performance, or attitudes, the present study highlights the value of focusing on information practices themselves. Understanding how medical professionals seek, interpret, evaluate, adapt, and ethically use AI-generated information may therefore be essential for responsible integration. Several limitations should be noted. The study relied on self-report measures, which capture perceived competence rather than observed performance. The cross-sectional design does not allow causal inference, and the findings reflect one national context and one language of administration. In addition, although the sample included a range of healthcare professions, the professional groups were uneven in size, with nurses forming the largest subgroup. Future research should therefore examine AI information literacy using performance-based tasks, longitudinal designs, and more balanced professional samples. It would also be valuable to test whether AI information literacy predicts actual practices such as verification behavior, disclosure decisions, or the quality of AI-supported professional work. Conclusion This study provides an initial profile of AI information literacy among healthcare professional and related healthcare professionals. Participants reported moderate AI-related background and moderate AI information literacy overall, with stronger scores in retrieval than in assessment. Across dimensions, self-perceived understanding of how AI tools work emerged as the most consistent predictor, followed by AI use and the number of AI tools used. These findings suggest that AI integration in healthcare should be approached as a literacy issue as well as a technological one. A multidimensional AI information literacy perspective may help healthcare systems identify uneven competency profiles and design more targeted support for responsible and effective AI use. Declarations Fundings No funding was received for conducting this study. Conflict of interest The authors declare that they have no competing interests (financial or non-financial). Research involving human participants This study was reviewed and approved by the Tel-Hai University Institutional Review Board (Application No. 01-10-25; approval date: October 22, 2025). Informed consent Informed consent was obtained electronically from all individual participants included in the study. Human Ethics and Consent to Participate declarations Applicable. Clinical trial number Not applicable. Author Contribution Statement L.A. and I.L. jointly contributed to the conception and design of the study, data collection, data analysis and interpretation, and manuscript writing. L.A. and I.L. both contributed equally to the development of the theoretical framing, revision of the manuscript, and preparation of the final submitted version. All authors reviewed and approved the final manuscript. Data availability statement The data supporting the findings of this study are not publicly available due to the conditions of the IRB approval and the need to protect participant confidentiality. The data are therefore not available for public sharing. All information necessary to interpret the findings is provided within the paper. References Zhang Q, Zuo J, Yang S. Research on the impact of generative artificial intelligence (GenAI) on enterprise innovation performance: a knowledge management perspective. J Knowl Manage. 2025;29(7):2238–57. https://doi.org/10.1108/JKM-10-2024-1198 . Amber Q, Sumbal MS, Tsang YP, Papa A, Nauman S. Change is always permanent: the role of ChatGPT on knowledge workers’ productivity and innovative behavior. J Knowl Manage. 2025. https://doi.org/10.1108/JKM-01-2025-0091 . Hong SJ. What drives AI-based risk information-seeking intent? Insufficiency of risk information versus (Un) certainty of AI chatbots. Comput Hum Behav. 2025;162:108460. https://doi.org/10.1016/j.chb.2024.108460 . Huvila I, Gorichanaz T. Trends in information behavior research, 2016–2022: An Annual Review of Information Science and Technology (ARIST) paper. J Assoc Inf Sci Technol. 2025;76(1):216–37. https://doi.org/10.1002/asi.24943 . Levkovich I, Omar M. Evaluating of BERT-based and large language mod for suicide detection, prevention, and risk assessment: A systematic review. J Med Syst. 2024;48(1):113. https://doi.org/10.1007/s10916-024-02134-3 . Shereefdeen H, MacKay M. It's like dynamite—It can do a lot of good. It could do a lot of harm: A qualitative study on the uses, benefits, and risks of genAI in public health. Digit Health. 2026;12:20552076261416713. https://doi.org/10.1177/20552076261416713 . Roberts LJ, Jayasena R, Khanna S, Arnott L, Lane P, Bain C. Challenges for implementing generative artificial intelligence (GenAI) into clinical healthcare. Intern Med J. 2025;55(7):1063–9. https://doi.org/10.1111/imj.70035 . Tischendorf T, Hinsche L, Hasseler M, Schaal T. GenAI in nursing and clinical practice: a rapid review of applications and challenges. J Public Health. 2025:1–1. https://doi.org/10.1007/s10389-025-02523-z Partap A, Mubeen US, Mohan NC, Chikkanna Y, Iyengar A. The Role of Generative AI in Transforming Healthcare Services. In 2025 25th International Conference on Software Quality, Reliability, and Security Companion (QRS-C). 2025. (pp. 33–39). IEEE. https://doi.org/10.1109/QRS-C65679.2025.00015 Fraile Navarro D, Kocaballi AB, Berkovsky S. Understanding Clinician Perceptions of GenAI: A Mixed Methods Analysis of Clinical Documentation Tasks. J Med Syst. 2025;49(1):101. https://doi.org/10.1007/s10916-025-02234-8 . Bignami EG, Russo M, Bellini V. Reclaiming Patient-Centered Care: How Intelligent Time is Redefining Healthcare Priorities. J Med Syst. 2025;49(1):30. https://doi.org/10.1007/s10916-025-02163-6 . Capella S. How does Generative AI Affect Patients' Rights? A Focus on Privacy, Autonomy, and Justice. Voices Bioeth. 2025;11. https://doi.org/10.52214/vib.v11i.14212 . MacKay M, Kukan A, McWhirter JE. The double-edged algorithm: A rapid review exploring the trustworthy and responsible use of generative AI in public health. Digit Health. 2025;11:20552076251393302. https://doi.org/10.1177/20552076251393302 . Trixa J, Kaspar K. Information literacy in the digital age: information sources, evaluation strategies, and perceived teaching competences of pre-service teachers. Front Psychol. 2024;15:1336436. https://doi.org/10.3389/fpsyg.2024.1336436 . Carroll AJ, Borycz J. Integrating large language models and generative artificial intelligence tools into information literacy instruction. The Journal of Academic Librarianship . 2024 1;50(4):102899. https://doi.org/10.1016/j.acalib.2024.102899 Johnson JG, Peralta M, Kaur M, Huang RS, Zhao S, Guan R, Rajaram S, Nebeling M. Exploring collaborative GenAI agents in synchronous group settings: eliciting team perceptions and design considerations for the future of work. Proceedings of the ACM on Human-Computer Interaction . 2025;9(7):1–33. https://doi.org/10.1145/3757595 Liu Y, Du Y. The effect of generative AI ethics on users’ continuous usage intentions: A PLS-SEM and fsQCA approach. Int J Human–Computer Interact. 2025;41(20):12831–42. https://doi.org/10.1080/10447318.2025.2465861 . Alon L, Levkovich I. Information literacy in the age of generative tools: Development and validation of the AI Information Literacy Scale (AILIS). Comput Hum Behav. 2026;1:7:100254. https://doi.org/10.1016/j.chbah.2026.100254 . Carolus A, Koch MJ, Straka S, Latoschik ME, Wienrich C. MAILS-Meta AI literacy scale: Development and testing of an AI literacy questionnaire based on well-founded competency models and psychological change-and meta-competencies. Comput Hum Behav. 2023;1(2):100014. https://doi.org/10.1016/j.chbah.2023.100014 . Koch MJ, Wienrich C, Straka S, Latoschik ME, Carolus A. Overview and confirmatory and exploratory factor analysis of AI literacy scale. Computers Education: Artif Intell. 2024;7:100310. https://doi.org/10.1016/j.caeai.2024.100310 . Hou C, Zhu G, Sudarshan V, Lim FS, Ong YS. Measuring undergraduate students' reliance on Generative AI during problem-solving: Scale development and validation. Comput Educ. 2025;234:105329. https://doi.org/10.1016/j.compedu.2025.105329 . Jorolan J, Cabillo F, Batucan RR, Camansi CM, Etoquilla A, Gapo J, Hallarte DK, Milano ML, Gonzales R, Gonzales G. Development and validation of moral ascendancy and dependency in AI integration (MAD-AI) scale for teachers. Comput Educ. 2025;235:105346. https://doi.org/10.1016/j.compedu.2025.105346 . Cai Y, Tian S. Student translators’ web-based vs. GenAI-based information-seeking behavior in translation process: A comparative study. Educ Inf Technol. 2025;30(13):18997–9025. https://doi.org/10.1007/s10639-025-13523-7 . Ooi KB, Tan GW, Al-Emran M, Al-Sharafi MA, Capatina A, Chakraborty A, Dwivedi YK, Huang TL, Kar AK, Lee VH, Loh XM. The potential of generative artificial intelligence across disciplines: Perspectives and future directions. J Comput Inform Syst. 2025;65(1):76–107. https://doi.org/10.1080/08874417.2023.2261010 . Shen L, Li H, Wang Y, Xie X, Qu H. Prompting generative AI with interaction-augmented instructions. InProceedings of the extended abstracts of the CHI conference on human factors in computing systems. 2025. (pp. 1–9). https://doi.org/10.1145/3706599.3720080 Li J, Yuan Q. The impact of AI-generated knowledge on user knowledge avoidance in OKCs: the moderating role of AI literacy and platform trust. J Knowl Manage. 2026:1–25. https://doi.org/10.1108/JKM-02-2025-0231 Radanliev P. AI ethics: Integrating transparency, fairness, and privacy in AI development. Appl Artif Intell. 2025;39(1):2463722. https://doi.org/10.1080/08839514.2025.2463722 . Singhal A, Neveditsin N, Tanveer H, Mago V. Toward fairness, accountability, transparency, and ethics in AI for social media and health care: scoping review. JMIR Med Inf. 2024;12(1):e50048. https://doi.org/10.2196/50048 . Kim B, Kim D. Understanding the Role of Trust, Perceived Risk, and Habit in Organization Members' Generative AI Use. Sage Open. 2026;16(1):21582440251410620. https://doi.org/10.1177/21582440251410620 . Maben SK, Lambiase J, Vasquez RA. Self-starters and lone rangers: Municipal government and nonprofit PR practitioners’ approaches to AI training, ethics, and policy-making. Public Relations Rev. 2026;52(1):102664. https://doi.org/10.1016/j.pubrev.2025.102664 . Esmaeilzadeh P. Patient safety and quality implications of large language model use in healthcare: a risk-stratified assessment of artificial intelligence-assisted medical consultations. Int J Qual Health Care. 2026;38(1):mzaf134. https://doi.org/10.1093/intqhc/mzaf134 . Dehnavieh R, Inayatullah S, Yousefi F, Nadali M. Artificial Intelligence (AI) and the future of Iran’s Primary Health Care (PHC) system. BMC Prim Care. 2025;26(1):75. https://doi.org/10.1186/s12875-025-02773-6 . Wardle C, Urbani S, Wang E. Evolving Health Information–Seeking Behavior in the Context of Google AI Overviews, ChatGPT, and Alexa: Interview Study Using the Think-Aloud Protocol. J Med Internet Res. 2025;27:e79961. https://doi.org/10.2196/79961 . Alon L, Malinoff T. Understanding Barriers to AI Use Among Public Sector Knowledge Workers. Proceedings of the Association for Information Science and Technology . 2025;62(1):1346-8. https://doi.org/10.1002/pra2.1399 Amjad AI, Sheikh BS. Trusting the Machine: How AI Assessment Feedback and AI Literacy Shape Students' Idea Implementation Skills. Eur J Educ. 2026;61(1):e70457. https://doi.org/10.1111/ejed.70457 . Levkovich I, Alon L. AI knowledge to AI literacy: A serial mediation model of generative AI literacy among knowledge workers. International Journal of Human–Computer Interaction . 2026, in press. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 15 May, 2026 Reviews received at journal 03 May, 2026 Reviews received at journal 28 Apr, 2026 Reviewers agreed at journal 24 Apr, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviews received at journal 15 Apr, 2026 Reviewers agreed at journal 15 Apr, 2026 Reviewers invited by journal 15 Apr, 2026 Editor invited by journal 20 Mar, 2026 Editor assigned by journal 13 Mar, 2026 Submission checks completed at journal 13 Mar, 2026 First submitted to journal 12 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9101750","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":627742532,"identity":"f998ede8-44e1-4633-8a8e-3958332b9ae3","order_by":0,"name":"Lilach Alon¹","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYDACdiB+YGAhx8DA3MbAYAASYmw8gFcLMxAnGEgY8zAwwrU0EKGFQSKxB6wFCvBq4W9mPvYgoUAifT97Y9uDHwUM8vwNzPhtkTjMlm4AdFhuD8/BdsMeAwbDGQcIOewwj5kEWItEYpsEjwED4wZCfpGHaknnkX/YJvnHgMGeoBYDqJYEHgnGNmmgLYkEtRgeZksDaTHsOZPYJi1jIJE84zABLXLHm49JfPhjI8/efviY5Js/Nrb97e0PH+DTgg4kIDE1CkbBKBgFo4AyAABpQUJRDlqB4QAAAABJRU5ErkJggg==","orcid":"","institution":"Tel-Hai University of Kiryat Shmona in the Galilee","correspondingAuthor":true,"prefix":"","firstName":"Lilach","middleName":"","lastName":"Alon¹","suffix":""},{"id":627742533,"identity":"1999d015-c780-44c9-a642-b2290c402ac0","order_by":1,"name":"Inbar Levkovich¹","email":"","orcid":"","institution":"Tel-Hai University of Kiryat Shmona in the Galilee","correspondingAuthor":false,"prefix":"","firstName":"Inbar","middleName":"","lastName":"Levkovich¹","suffix":""}],"badges":[],"createdAt":"2026-03-12 07:53:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9101750/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9101750/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107636207,"identity":"49200b87-806c-4690-bfe9-16681c3cf267","added_by":"auto","created_at":"2026-04-23 12:37:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":62680,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAI-related literacy background among healthcare professionals\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: Error bars represent standard deviations.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9101750/v1/0cab98d7d05f2e8bb15de89a.png"},{"id":107636208,"identity":"1a172f48-595f-4df4-b22a-12166ad022dc","added_by":"auto","created_at":"2026-04-23 12:37:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":62750,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAI-related literacy background by professional group.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: Error bars represent standard deviations. Significant Bonferroni-adjusted differences were found for frequency of AI use, with medical doctors reporting more frequent AI use than other health professionals, and for number of AI tools used, with health professionals reporting use of a larger number of AI tools than nurses.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9101750/v1/dd52f76bfef2fefa43eb1254.png"},{"id":107636209,"identity":"ccafe455-02d8-460b-a7c0-9ae11541b114","added_by":"auto","created_at":"2026-04-23 12:37:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":60791,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAI information literacy dimensions by professional group.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: Error bars represent standard deviations. Significant Bonferroni-adjusted differences were found for process/create and retrieve, with medical doctors scoring higher than nurses and other health professionals.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9101750/v1/168b9ef8a4bffcd31e304ced.png"},{"id":107636217,"identity":"ba039cc2-926e-454c-a064-3d078e88c47f","added_by":"auto","created_at":"2026-04-23 12:37:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":437760,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9101750/v1/74fa6035-bf11-4727-8483-d7c871b8782d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"AI Information Literacy in Healthcare Context: Profiles and Predictors Among Healthcare Professionals","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHealthcare systems are entering a period in which generative artificial intelligence (GenAI) tools are becoming increasingly visible in professional practice [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Large language model (LLM) based systems are now used to summarize information, draft text, explain complex concepts, support documentation, and provide rapid access to synthesized answers [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In health-related contexts, these tools are being explored for clinical decision support [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], communication, education, administrative work, and information seeking [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. As a result, healthcare professionals are beginning to work in environments where AI-generated content may shape how information is accessed, interpreted, and used [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis shift creates new demands for healthcare professional. In healthcare, the use of AI-generated information is a high-stakes matter that requires careful judgment, responsible use, and context-sensitive interpretation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Professionals must often make judgments under time pressure [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], communicate clearly with patients and colleagues [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], protect sensitive information, and distinguish between plausible outputs and trustworthy ones [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In such contexts, what matters is healthcare professionals\u0026rsquo; ability to use AI critically, responsibly, and effectively. This includes retrieving relevant information, evaluating the quality of AI-generated responses, adapting outputs for professional purposes, and recognizing ethical boundaries in the use of AI tools [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese demands can be understood through the concept of AI information literacy. Information literacy has traditionally been defined as a set of abilities related to locating, processing, interpreting, and critically evaluating information [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In AI-mediated environments, these abilities remain central, but the conditions under which they are enacted have changed. Users must formulate prompts, interpret outputs that may be fluent but unverifiable, assess transparency and trustworthiness, and distinguish between human-authored and machine-generated information [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Accordingly, AI information literacy refers to the ability to locate, create, process, and critically evaluate information with and through AI tools while making informed and ethical decisions about their use [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This construct reflects how people work with AI as an information intermediary in everyday and professional practice [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUnderstanding the integration of GenAI into healthcare through an information literacy perspective is especially important because the different dimensions of AI information literacy may not develop equally. A healthcare professional may feel relatively confident using AI to retrieve information or generate drafts, yet less confident in critically evaluating output quality or in making ethical decisions about disclosure and confidentiality. A multidimensional approach therefore helps identify uneven competency profiles rather than treating AI-related competence as a single general skill.\u003c/p\u003e \u003cp\u003eThe AI Information Literacy Scale (AILIS) was developed to address these aspects by operationalizing AI information literacy as information literacy self-efficacy in GenAI-mediated information behavior [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Unlike broader AI literacy measures, which often emphasize conceptual understanding, attitudes, or general awareness of AI [e.g., 21,22], AILIS focuses on concrete information practices performed with GenAI tools. Grounded in information behavior and information literacy scholarship, it was designed for everyday, educational, and professional contexts. To the best of our knowledge, AI information literacy has not yet been examined in healthcare contexts.\u003c/p\u003e \u003cp\u003eAILIS captures four dimensions that are highly relevant to healthcare use. The first, creation and processing, refers to the ability to generate, adapt, summarize, compare, and integrate AI-generated content for specific purposes, audiences, and formats [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The second, assessment and critique, concerns the ability to evaluate outputs for accuracy, coherence, bias, timeliness, and possible fabrication, as well as to recognize missing evidence or problematic content [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The third, seeking and retrieval, reflects the ability to clarify information needs, formulate effective prompts, request sources or examples, and decide when AI is an appropriate tool for a given task [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The fourth, ethics, addresses transparency, responsible use, attribution, and the protection of sensitive information when interacting with AI systems [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Overall, these dimensions move beyond undifferentiated notions of AI readiness and allow the identification of uneven literacy profiles across types of information work.\u003c/p\u003e \u003cp\u003eAlthough AILIS was validated in a general adult sample, little is known about AI information literacy among healthcare professionals. In particular, it remains unclear how healthcare professionals\u0026rsquo; AI-related literacy background is distributed, how they score across the dimensions of AI information literacy, whether these patterns differ across professional groups, and which demographic, professional, and AI-related background variables predict stronger or weaker AI information literacy in healthcare contexts. The present study addresses this gap by applying AILIS to a sample of healthcare professional and related healthcare professionals. Specifically, it examines participants\u0026rsquo; AI-related literacy background, their scores across the four dimensions of AI information literacy, differences across professional groups, and the demographic, professional, and AI-related variables associated with AI information literacy across dimensions. We examine the following research questions:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003eRQ1.\u003c/em\u003e What are healthcare professional\u0026rsquo;s self-reported levels of AI-related literacy background, including perceived AI knowledge, frequency of AI use, and number of AI tool use, and do these differ across professional groups?\u003c/p\u003e\u003cp\u003e \u003cem\u003eRQ2.\u003c/em\u003e How do healthcare professionals score on the identified dimensions of AI information literacy and do these differ across professional groups?\u003c/p\u003e\u003cp\u003e \u003cem\u003eRQ3.\u003c/em\u003e Which demographic, professional, and AI-related background variables predict healthcare professionals\u0026rsquo; AI information literacy across these dimensions?\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eBy examining these questions, the study provides an initial healthcare-based profile of AI information literacy that may inform future research, organizational assessment, and targeted educational efforts for safer and more effective AI use in healthcare.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThe sample in this study included 390 participants from a range of healthcare professions and training stages. A sensitivity analysis conducted in G*Power 3.1 indicated that with N\u0026thinsp;=\u0026thinsp;390, α\u0026thinsp;=\u0026thinsp;.05, and power = .80, the study was sufficiently powered to detect small-to-medium effects in the one-way ANOVAs (f\u0026thinsp;=\u0026thinsp;0.16) and small effects in the multiple regression models with five predictors (f\u0026sup2; = 0.033).\u003c/p\u003e \u003cp\u003eParticipants were recruited from the medical workforce and completed the questionnaire online in Hebrew. The sample included 58 male participants (14.9%) and 332 female participants (85.1%), with ages ranging from 23 to 68 years (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;40.64, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10.67). Mean seniority in the medical field was 11.13 years (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;11.05). In terms of professional role, the sample included 87 participants in medical roles across training and career stages (22.3%), including 28 medical students (7.2%), 13 interns (3.3%), 16 residents (4.1%), and 30 specialists or senior physicians (7.7%). Registered nurses constituted the largest group, with 164 participants (42.1%). The remaining 139 participants (35.6%) represented other health professions, including 84 allied health professionals, such as speech therapists, physiotherapists, and occupational therapists (21.5%), 45 pharmacists or laboratory staff (11.5%), and 10 participants in other roles (2.6%).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTools\u003c/h3\u003e\n\u003cp\u003eData were collected using a structured questionnaire consisting of three sections.\u003c/p\u003e \u003cp\u003e \u003cem\u003eAI Information Literacy Scale (AILIS)\u003c/em\u003e: AI information literacy was assessed using AILIS [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], a self-report instrument that conceptualizes AI information literacy as information literacy self-efficacy in GenAI-mediated information behavior. The scale assesses perceived ability to work with AI-generated information across four dimensions: process/create, assess, retrieve, and ethics. In its original validation, AILIS was designed to capture concrete information practices performed with GenAI tools across everyday, educational, and professional contexts. The full instrument includes 39 items distributed across four subscales: process/create (16 items, e.g., \u0026ldquo;Generate titles or summaries using an AI tool\u0026rdquo;, \u0026ldquo;Convert a text into a graph or diagram using an AI tool\u0026rdquo;), assess (13 items, e.g., \u0026ldquo;Detect hallucinations and ask the tool to correct them\u0026rdquo;, \u0026ldquo;Critically edit an output generated by an AI tool\u0026rdquo;), retrieve (8 items, e.g., \u0026ldquo;Update or refine prompt after receiving an answer to improve the result\u0026rdquo;, \u0026ldquo;Ask the AI tool for examples, sources, or citations\u0026rdquo;), and ethics (2 items, e.g., \u0026ldquo;Indicate my use of AI tools in a transparent way\u0026rdquo;). Items are rated on a 5-point confidence scale ranging from 1 (not at all) to 5 (to a very great extent). In the present sample, internal consistency was high for process/create (α\u0026thinsp;=\u0026thinsp;.972), assess (α\u0026thinsp;=\u0026thinsp;.974), and retrieve (α\u0026thinsp;=\u0026thinsp;.959), and acceptable for ethics (α\u0026thinsp;=\u0026thinsp;.794).\u003c/p\u003e \u003cp\u003e \u003cem\u003eGenAI usage patterns\u003c/em\u003e: To characterize participants\u0026rsquo; AI-related background, we included four self-report indicators: perceived AI knowledge, frequency of AI use, number and type of AI tools used, and self-perceived understanding of how AI tools work. Perceived AI knowledge and frequency of AI use were rated on a 5-point Likert scale ranging from 1 (\u0026ldquo;not at all\u0026rdquo;) to 5 (\u0026ldquo;to a very great extent\u0026rdquo;). Self-perceived understanding of how AI tools work was rated on a 7-point Likert scale ranging from 1 (\u0026ldquo;not at all\u0026rdquo;) to 7 (\u0026ldquo;to a very great extent\u0026rdquo;). The number and type of AI tools used were included to reflect the number of participants\u0026rsquo; GenAI tool repertoire. Perceived AI knowledge reflected participants\u0026rsquo; subjective familiarity with AI tools, and frequency of AI use captured how often they used them. These variables were used both to describe the sample and to predict AI information literacy scores.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDemographic and professional background variables\u003c/strong\u003e \u003cp\u003eParticipants also completed a brief background questionnaire that included year of birth, gender, seniority in the medical field, and professional role within the healthcare professional.\u003c/p\u003e \u003c/p\u003e\n\u003ch3\u003eProcedure and Ethics\u003c/h3\u003e\n\u003cp\u003eThe survey was administered online via the iPanel internet panel service. Data were collected in December 2025 and January 2026. iPanel is an established Israeli online research platform for administering surveys among consenting respondents and enables the recruitment of geographically dispersed samples through its panel infrastructure. Participants were invited through iPanel\u0026rsquo;s distribution system, and the questionnaire, which included the AILIS items, GenAI knowledge and use measures, and background questions, required approximately 15 minutes to complete.\u003c/p\u003e \u003cp\u003eResponses were collected anonymously, and iPanel\u0026rsquo;s privacy and confidentiality procedures were applied throughout the data collection process. The study was conducted in accordance with institutional and national ethical guidelines for research involving human participants. The research protocol was reviewed and approved by the Tel-Hai University Institutional Review Board (Application No. 01-10-25; approval date: October 22, 2025). Participation was voluntary, and informed consent was obtained electronically, as participants indicated their agreement on the first survey screen before proceeding. Participants were informed that their responses would be used for research purposes only, that they could discontinue participation at any time, and that no identifying information would be stored with their responses. Data were analyzed and reported in aggregate form to protect participants\u0026rsquo; privacy.\u003c/p\u003e\n\u003ch3\u003eAnalysis\u003c/h3\u003e\n\u003cp\u003eData were analyzed using IBM SPSS Statistics (Version 31). To address RQ1, descriptive statistics were computed for the AI-related literacy background variables, including perceived AI knowledge, frequency of AI use, number of AI tools used, and self-perceived understanding of how AI tools work. To address RQ2, descriptive statistics were calculated for the four dimensions of AI information literacy: process/create, assess, retrieve, and ethics. To address RQ3, a series of multiple linear regression analyses was conducted. Each AI information literacy dimension was entered as a separate dependent variable. Age, gender, seniority in the medical field, perceived AI knowledge, frequency of AI use, number of AI tools used, and self-perceived understanding of how AI tools work were entered as predictor variables to identify the variables associated with healthcare professional\u0026rsquo;s AI information literacy across dimensions. In addition, Pearson correlation analyses were conducted to examine bivariate associations among the study variables.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAI-related literacy background across groups\u003c/h2\u003e \u003cp\u003eTo characterize participants\u0026rsquo; AI-related background, participants reported the number and type of GenAI tools they currently use, the frequency of AI use, their perceived AI knowledge, and their self-perceived understanding of how AI tools work (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The mean number of AI tools used was 1.82 (SD\u0026thinsp;=\u0026thinsp;1.03). Frequency of AI use was rated on a 1 to 5 scale, with higher scores indicating more frequent use (M\u0026thinsp;=\u0026thinsp;3.02, SD\u0026thinsp;=\u0026thinsp;1.17). Perceived AI knowledge was also rated on a 1 to 5 scale, with higher scores indicating greater perceived knowledge (M\u0026thinsp;=\u0026thinsp;3.35, SD\u0026thinsp;=\u0026thinsp;1.05). Self-perceived understanding of how AI tools work was rated on a 1 to 7 scale, with higher scores indicating greater perceived understanding (M\u0026thinsp;=\u0026thinsp;3.66, SD\u0026thinsp;=\u0026thinsp;1.50).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eError bars represent standard deviations.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eTo further examine whether AI-related literacy background differed across professional groups, a one-way ANOVA was conducted to compare medical doctors, nurses, and health professionals (Fig.\u0026nbsp;2). No statistically significant group differences were found for perceived AI knowledge [\u003cem\u003eF\u003c/em\u003e(2, 387)\u0026thinsp;=\u0026thinsp;2.75, \u003cem\u003ep\u003c/em\u003e = .065], or for self-perceived understanding of how AI tools work [\u003cem\u003eF\u003c/em\u003e(2, 387)\u0026thinsp;=\u0026thinsp;0.36, \u003cem\u003ep\u003c/em\u003e = .700]. However, significant differences emerged for frequency of AI use [\u003cem\u003eF\u003c/em\u003e(2, 387)\u0026thinsp;=\u0026thinsp;3.97, \u003cem\u003ep\u003c/em\u003e \u0026lt; .05], and number of AI tools used [\u003cem\u003eF\u003c/em\u003e(2, 387)\u0026thinsp;=\u0026thinsp;6.38, \u003cem\u003ep\u003c/em\u003e \u0026lt; .01]. Bonferroni comparisons showed that medical doctors reported more frequent AI use (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.23, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.15) than health professionals (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.81, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.13), whereas nurses did not differ significantly from either group. In contrast, health professionals reported using a larger number of AI tools (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.05, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.07) than nurses (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.63, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.02), whereas medical doctors did not differ significantly from either group.\u003c/p\u003e \u003cp\u003e \u003cem\u003eFigure 2. AI-related literacy background by professional group.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003e\u003cdiv description=\"\" class=\"Drawing\" id=\"554999338\" name=\"Picture 3\"\u003e\u003c/div\u003eNote\u003c/strong\u003e \u003cp\u003eError bars represent standard deviations. Significant Bonferroni-adjusted differences were found for frequency of AI use, with medical doctors reporting more frequent AI use than other health professionals, and for number of AI tools used, with health professionals reporting use of a larger number of AI tools than nurses.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDifferences Across AI Information Literacy Dimensions\u003c/h3\u003e\n\u003cp\u003eOverall, participants reported moderate levels of AI information literacy across the four dimensions. The highest mean score was found for retrieve (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.04, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.18), followed by process/create (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.80, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.16), ethics (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.73, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.28), and assess (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.53, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.14). This pattern suggests that participants felt relatively more confident in using AI tools to locate and retrieve information than in critically assessing AI-generated information.\u003c/p\u003e \u003cp\u003eTo further examine whether AI information literacy differed across professional groups, a one-way ANOVA was conducted to compare medical doctors, nurses, and health professionals (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Significant group differences emerged for process/create [\u003cem\u003eF\u003c/em\u003e(2, 387)\u0026thinsp;=\u0026thinsp;4.71, \u003cem\u003ep\u003c/em\u003e \u0026lt; .05], assess [\u003cem\u003eF\u003c/em\u003e(2, 387)\u0026thinsp;=\u0026thinsp;3.30, \u003cem\u003ep\u003c/em\u003e \u0026lt; .05], and retrieve [\u003cem\u003eF\u003c/em\u003e(2, 387)\u0026thinsp;=\u0026thinsp;4.56, \u003cem\u003ep\u003c/em\u003e \u0026lt; .05]. No significant group differences were found for ethics [\u003cem\u003eF\u003c/em\u003e(2, 387)\u0026thinsp;=\u0026thinsp;1.12, \u003cem\u003ep\u003c/em\u003e = .329].\u003c/p\u003e \u003cp\u003eBonferroni post hoc comparisons showed that medical doctors scored significantly higher than nurses and health professionals on process/create. Specifically, medical doctors scored higher than nurses (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.13 vs. 2.72, \u003cem\u003ep\u003c/em\u003e \u0026lt; .05) and health professionals (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.13 vs. 2.68, \u003cem\u003ep\u003c/em\u003e \u0026lt; .05). A similar pattern was found for retrieve, with medical doctors scoring higher than nurses (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.36 vs. 2.98, \u003cem\u003ep\u003c/em\u003e \u0026lt; .05) and health professionals (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.36 vs. 2.90, \u003cem\u003ep\u003c/em\u003e \u0026lt; .05). For assess, although the overall ANOVA was significant, Bonferroni-adjusted pairwise comparisons did not reach significance. No significant pairwise differences were found for ethics.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eError bars represent standard deviations. Significant Bonferroni-adjusted differences were found for process/create and retrieve, with medical doctors scoring higher than nurses and other health professionals.\u003c/p\u003e \u003c/p\u003e\n\u003ch3\u003ePredictors of AI Information Literacy Across Dimensions\u003c/h3\u003e\n\u003cp\u003eA series of multiple linear regression analyses was conducted to examine whether demographic, professional, and AI-related background variables predicted healthcare professional\u0026rsquo;s AI information literacy across the four dimensions. The initial models included age, gender, professional group, perceived AI knowledge, frequency of AI use, number of AI tools used, and self-perceived understanding of how AI tools work. Gender and professional group were not significant predictors and were therefore omitted from the final models.\u003c/p\u003e \u003cp\u003eFor process/create, the overall model was significant [\u003cem\u003eF\u003c/em\u003e(5, 384)\u0026thinsp;=\u0026thinsp;71.86, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001], and explained 48.3% of the variance. Higher scores were predicted by greater perceived AI knowledge (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.182, \u003cem\u003ep\u003c/em\u003e \u0026lt; .01), more frequent AI use (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.198, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), use of a larger number of AI tools (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.148, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), and greater self-perceived understanding of how AI tools work (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.312, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). Age was a significant negative predictor (\u003cem\u003eβ\u003c/em\u003e = \u0026minus;\u0026thinsp;.092, \u003cem\u003ep\u003c/em\u003e \u0026lt; .05).\u003c/p\u003e \u003cp\u003eFor assess, the model was also significant [\u003cem\u003eF\u003c/em\u003e(5, 384)\u0026thinsp;=\u0026thinsp;42.11, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001], explaining 35.4% of the variance. Higher assess scores were predicted by greater perceived AI knowledge (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.160, \u003cem\u003ep\u003c/em\u003e \u0026lt; .05), use of a larger number of AI tools (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.128, \u003cem\u003ep\u003c/em\u003e \u0026lt; .01), and greater self-perceived understanding of how AI tools work (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.312, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). Age was again a negative predictor (\u003cem\u003eβ\u003c/em\u003e = \u0026minus;\u0026thinsp;.086, \u003cem\u003ep\u003c/em\u003e \u0026lt; .05). Frequency of AI use did not reach significance in this model (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.112, \u003cem\u003ep\u003c/em\u003e = .061).\u003c/p\u003e \u003cp\u003eFor retrieve, the model was significant [\u003cem\u003eF\u003c/em\u003e(5, 384)\u0026thinsp;=\u0026thinsp;52.07, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001], and explained 40.4% of the variance. Higher retrieve scores were predicted by greater perceived AI knowledge (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.122, \u003cem\u003ep\u003c/em\u003e \u0026lt; .05), more frequent AI use (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.173, \u003cem\u003ep\u003c/em\u003e \u0026lt; .01), use of a larger number of AI tools (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.160, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), and greater self-perceived understanding of how AI tools work (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.309, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). Age was a significant negative predictor (\u003cem\u003eβ\u003c/em\u003e = \u0026minus;\u0026thinsp;.107, \u003cem\u003ep\u003c/em\u003e \u0026lt; .05).\u003c/p\u003e \u003cp\u003eFor ethics, the model was significant [\u003cem\u003eF\u003c/em\u003e(5, 384)\u0026thinsp;=\u0026thinsp;28.91, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001], explaining 27.3% of the variance. Higher ethics scores were predicted by more frequent AI use (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.128, \u003cem\u003ep\u003c/em\u003e \u0026lt; .05), use of a larger number of AI tools (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.137, \u003cem\u003ep\u003c/em\u003e \u0026lt; .01), and greater self-perceived understanding of how AI tools work (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.269, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). In contrast, age (\u003cem\u003eβ\u003c/em\u003e = \u0026minus;\u0026thinsp;.080, \u003cem\u003ep\u003c/em\u003e = .083) and perceived AI knowledge (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.097, \u003cem\u003ep\u003c/em\u003e = .145) were not significant predictors in this model.\u003c/p\u003e \u003cp\u003eAcross all four models, self-perceived understanding of how AI tools work emerged as the strongest and most consistent predictor, while age showed a small but consistent negative association with most AI information literacy dimensions.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined AI information literacy among healthcare professional and related healthcare professionals by applying AILIS in a healthcare context. Three main findings emerged. First, participants reported moderate AI-related literacy background overall, including moderate perceived AI knowledge, frequency of AI use, and the number of AI tool use. Second, AI information literacy was uneven across dimensions: retrieval received the highest scores, whereas assessment received the lowest. Third, AI information literacy was more strongly associated with AI-related experience and perceived understanding than with demographic or professional characteristics. Together, these findings suggest that AI integration in healthcare should be understood as depending on multiple dimensions of AI information literacy.\u003c/p\u003e \u003cp\u003eOne central contribution of the study is showing that AI information literacy in healthcare is a multidimensional capability [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The uneven pattern across dimensions is theoretically meaningful because it suggests that healthcare professionals engage with AI-mediated information in different ways and with different levels of confidence, as has been found in other workforce domains [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In particular, they may feel more confident using AI to obtain or rework information than in judging the reliability, adequacy, or ethical implications of what AI produces [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. This suggests that AI use in healthcare is shaped by the quality of professional judgment applied to AI-generated information once it is produced. More broadly, the findings indicate that the literacy demands of AI use in healthcare are internally differentiated and are best understood through multiple dimensions of AI information literacy [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The identified group differences further clarify this pattern. For healthcare organizations, this highlights the importance of supporting not only AI use, but also the development of critical and responsible information practices [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA second contribution of the study is the indication that AI information literacy in healthcare is shaped primarily by active engagement with AI [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Once AI-related experience and perceived understanding were taken into account, demographic and professional group variables contributed little to explaining variation across dimensions. The regression analyses further showed that AI information literacy was most strongly associated with AI-related engagement. Across all four dimensions, self-perceived understanding of how AI tools work was the strongest and most consistent predictor, while AI use and number of AI tools used also contributed to several models [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. By contrast, gender and professional group did not remain significant predictors in the final models, and age showed only a small negative association with some dimensions.\u003c/p\u003e \u003cp\u003eOverall, the current study suggests that AI information literacy in healthcare contexts varies across dimensions and is most strongly associated with active AI engagement and perceived understanding rather than demographic or professional background alone [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. For healthcare systems adopting GenAI tools, this means that effective integration may depend not only on technological availability, but also on the literacy-related capacities of the professionals expected to use them.\u003c/p\u003e \u003cp\u003eThese findings have several implications for healthcare contexts. First, they suggest that workforce preparation for AI should not be framed only around positive attitudes or willingness to adopt innovation. What appears more central is the development of specific competencies related to retrieving, processing, evaluating, and ethically using AI-generated information. Second, the relatively lower scores on assessment point to a particularly important area for intervention. If healthcare professionals are more confident retrieving or generating information than evaluating its validity, there is a risk of overreliance on fluent but insufficiently scrutinized output. Third, the strong role of perceived understanding suggests that educational efforts may need to combine practical use with reflective understanding of how GenAI tools generate responses, where errors may arise, and how outputs should be checked in professional settings. In this sense, AI information literacy may offer a more actionable framework for healthcare training than broader and more diffuse notions of AI readiness.\u003c/p\u003e \u003cp\u003eThe study also contributes conceptually by extending AI information literacy into a healthcare setting. To the best of our knowledge, this is one of the first applications of an explicit AI information literacy framework among healthcare professional. In a field where discussions of AI often emphasize implementation, clinical performance, or attitudes, the present study highlights the value of focusing on information practices themselves. Understanding how medical professionals seek, interpret, evaluate, adapt, and ethically use AI-generated information may therefore be essential for responsible integration.\u003c/p\u003e \u003cp\u003eSeveral limitations should be noted. The study relied on self-report measures, which capture perceived competence rather than observed performance. The cross-sectional design does not allow causal inference, and the findings reflect one national context and one language of administration. In addition, although the sample included a range of healthcare professions, the professional groups were uneven in size, with nurses forming the largest subgroup. Future research should therefore examine AI information literacy using performance-based tasks, longitudinal designs, and more balanced professional samples. It would also be valuable to test whether AI information literacy predicts actual practices such as verification behavior, disclosure decisions, or the quality of AI-supported professional work.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides an initial profile of AI information literacy among healthcare professional and related healthcare professionals. Participants reported moderate AI-related background and moderate AI information literacy overall, with stronger scores in retrieval than in assessment. Across dimensions, self-perceived understanding of how AI tools work emerged as the most consistent predictor, followed by AI use and the number of AI tools used. These findings suggest that AI integration in healthcare should be approached as a literacy issue as well as a technological one. A multidimensional AI information literacy perspective may help healthcare systems identify uneven competency profiles and design more targeted support for responsible and effective AI use.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eFundings\u003c/p\u003e\n\u003cp\u003eNo funding was received for conducting this study.\u003c/p\u003e\n\u003cp\u003eConflict of interest\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests (financial or non-financial).\u003c/p\u003e\n\u003cp\u003eResearch involving human participants\u003c/p\u003e\n\u003cp\u003eThis study was reviewed and approved by the Tel-Hai University Institutional Review Board (Application No. 01-10-25; approval date: October 22, 2025).\u003c/p\u003e\n\u003cp\u003eInformed consent\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained electronically from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003eHuman Ethics and Consent to Participate declarations\u003c/p\u003e\n\u003cp\u003eApplicable.\u003c/p\u003e\n\u003cp\u003eClinical trial number\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAuthor Contribution Statement\u003c/p\u003e\n\u003cp\u003eL.A. and I.L. jointly contributed to the conception and design of the study, data collection, data analysis and interpretation, and manuscript writing. L.A. and I.L. both contributed equally to the development of the theoretical framing, revision of the manuscript, and preparation of the final submitted version. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eData availability statement\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are not publicly available due to the conditions of the IRB approval and the need to protect participant confidentiality. The data are therefore not available for public sharing. All information necessary to interpret the findings is provided within the paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZhang Q, Zuo J, Yang S. Research on the impact of generative artificial intelligence (GenAI) on enterprise innovation performance: a knowledge management perspective. J Knowl Manage. 2025;29(7):2238\u0026ndash;57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/JKM-10-2024-1198\u003c/span\u003e\u003cspan address=\"10.1108/JKM-10-2024-1198\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmber Q, Sumbal MS, Tsang YP, Papa A, Nauman S. Change is always permanent: the role of ChatGPT on knowledge workers\u0026rsquo; productivity and innovative behavior. J Knowl Manage. 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/JKM-01-2025-0091\u003c/span\u003e\u003cspan address=\"10.1108/JKM-01-2025-0091\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHong SJ. What drives AI-based risk information-seeking intent? Insufficiency of risk information versus (Un) certainty of AI chatbots. Comput Hum Behav. 2025;162:108460. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.chb.2024.108460\u003c/span\u003e\u003cspan address=\"10.1016/j.chb.2024.108460\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuvila I, Gorichanaz T. Trends in information behavior research, 2016\u0026ndash;2022: An Annual Review of Information Science and Technology (ARIST) paper. J Assoc Inf Sci Technol. 2025;76(1):216\u0026ndash;37. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/asi.24943\u003c/span\u003e\u003cspan address=\"10.1002/asi.24943\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevkovich I, Omar M. Evaluating of BERT-based and large language mod for suicide detection, prevention, and risk assessment: A systematic review. J Med Syst. 2024;48(1):113. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10916-024-02134-3\u003c/span\u003e\u003cspan address=\"10.1007/s10916-024-02134-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShereefdeen H, MacKay M. It's like dynamite\u0026mdash;It can do a lot of good. It could do a lot of harm: A qualitative study on the uses, benefits, and risks of genAI in public health. Digit Health. 2026;12:20552076261416713. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/20552076261416713\u003c/span\u003e\u003cspan address=\"10.1177/20552076261416713\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoberts LJ, Jayasena R, Khanna S, Arnott L, Lane P, Bain C. Challenges for implementing generative artificial intelligence (GenAI) into clinical healthcare. Intern Med J. 2025;55(7):1063\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/imj.70035\u003c/span\u003e\u003cspan address=\"10.1111/imj.70035\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTischendorf T, Hinsche L, Hasseler M, Schaal T. GenAI in nursing and clinical practice: a rapid review of applications and challenges. J Public Health. 2025:1\u0026ndash;1. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10389-025-02523-z\u003c/span\u003e\u003cspan address=\"10.1007/s10389-025-02523-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePartap A, Mubeen US, Mohan NC, Chikkanna Y, Iyengar A. The Role of Generative AI in Transforming Healthcare Services. \u003cem\u003eIn 2025 25th International Conference on Software Quality, Reliability, and Security Companion (QRS-C).\u003c/em\u003e 2025. (pp. 33\u0026ndash;39). IEEE. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/QRS-C65679.2025.00015\u003c/span\u003e\u003cspan address=\"10.1109/QRS-C65679.2025.00015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFraile Navarro D, Kocaballi AB, Berkovsky S. Understanding Clinician Perceptions of GenAI: A Mixed Methods Analysis of Clinical Documentation Tasks. J Med Syst. 2025;49(1):101. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10916-025-02234-8\u003c/span\u003e\u003cspan address=\"10.1007/s10916-025-02234-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBignami EG, Russo M, Bellini V. Reclaiming Patient-Centered Care: How Intelligent Time is Redefining Healthcare Priorities. J Med Syst. 2025;49(1):30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10916-025-02163-6\u003c/span\u003e\u003cspan address=\"10.1007/s10916-025-02163-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCapella S. How does Generative AI Affect Patients' Rights? A Focus on Privacy, Autonomy, and Justice. Voices Bioeth. 2025;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.52214/vib.v11i.14212\u003c/span\u003e\u003cspan address=\"10.52214/vib.v11i.14212\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacKay M, Kukan A, McWhirter JE. The double-edged algorithm: A rapid review exploring the trustworthy and responsible use of generative AI in public health. Digit Health. 2025;11:20552076251393302. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/20552076251393302\u003c/span\u003e\u003cspan address=\"10.1177/20552076251393302\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrixa J, Kaspar K. Information literacy in the digital age: information sources, evaluation strategies, and perceived teaching competences of pre-service teachers. Front Psychol. 2024;15:1336436. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpsyg.2024.1336436\u003c/span\u003e\u003cspan address=\"10.3389/fpsyg.2024.1336436\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarroll AJ, Borycz J. Integrating large language models and generative artificial intelligence tools into information literacy instruction. \u003cem\u003eThe Journal of Academic Librarianship\u003c/em\u003e. 2024 1;50(4):102899. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.acalib.2024.102899\u003c/span\u003e\u003cspan address=\"10.1016/j.acalib.2024.102899\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnson JG, Peralta M, Kaur M, Huang RS, Zhao S, Guan R, Rajaram S, Nebeling M. Exploring collaborative GenAI agents in synchronous group settings: eliciting team perceptions and design considerations for the future of work. \u003cem\u003eProceedings of the ACM on Human-Computer Interaction\u003c/em\u003e. 2025;9(7):1\u0026ndash;33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1145/3757595\u003c/span\u003e\u003cspan address=\"10.1145/3757595\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Y, Du Y. The effect of generative AI ethics on users\u0026rsquo; continuous usage intentions: A PLS-SEM and fsQCA approach. Int J Human\u0026ndash;Computer Interact. 2025;41(20):12831\u0026ndash;42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/10447318.2025.2465861\u003c/span\u003e\u003cspan address=\"10.1080/10447318.2025.2465861\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlon L, Levkovich I. Information literacy in the age of generative tools: Development and validation of the AI Information Literacy Scale (AILIS). Comput Hum Behav. 2026;1:7:100254. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.chbah.2026.100254\u003c/span\u003e\u003cspan address=\"10.1016/j.chbah.2026.100254\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarolus A, Koch MJ, Straka S, Latoschik ME, Wienrich C. MAILS-Meta AI literacy scale: Development and testing of an AI literacy questionnaire based on well-founded competency models and psychological change-and meta-competencies. Comput Hum Behav. 2023;1(2):100014. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.chbah.2023.100014\u003c/span\u003e\u003cspan address=\"10.1016/j.chbah.2023.100014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoch MJ, Wienrich C, Straka S, Latoschik ME, Carolus A. Overview and confirmatory and exploratory factor analysis of AI literacy scale. Computers Education: Artif Intell. 2024;7:100310. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.caeai.2024.100310\u003c/span\u003e\u003cspan address=\"10.1016/j.caeai.2024.100310\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHou C, Zhu G, Sudarshan V, Lim FS, Ong YS. Measuring undergraduate students' reliance on Generative AI during problem-solving: Scale development and validation. Comput Educ. 2025;234:105329. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.compedu.2025.105329\u003c/span\u003e\u003cspan address=\"10.1016/j.compedu.2025.105329\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJorolan J, Cabillo F, Batucan RR, Camansi CM, Etoquilla A, Gapo J, Hallarte DK, Milano ML, Gonzales R, Gonzales G. Development and validation of moral ascendancy and dependency in AI integration (MAD-AI) scale for teachers. Comput Educ. 2025;235:105346. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.compedu.2025.105346\u003c/span\u003e\u003cspan address=\"10.1016/j.compedu.2025.105346\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCai Y, Tian S. Student translators\u0026rsquo; web-based vs. GenAI-based information-seeking behavior in translation process: A comparative study. Educ Inf Technol. 2025;30(13):18997\u0026ndash;9025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10639-025-13523-7\u003c/span\u003e\u003cspan address=\"10.1007/s10639-025-13523-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOoi KB, Tan GW, Al-Emran M, Al-Sharafi MA, Capatina A, Chakraborty A, Dwivedi YK, Huang TL, Kar AK, Lee VH, Loh XM. The potential of generative artificial intelligence across disciplines: Perspectives and future directions. J Comput Inform Syst. 2025;65(1):76\u0026ndash;107. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/08874417.2023.2261010\u003c/span\u003e\u003cspan address=\"10.1080/08874417.2023.2261010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen L, Li H, Wang Y, Xie X, Qu H. Prompting generative AI with interaction-augmented instructions. \u003cem\u003eInProceedings of the extended abstracts of the CHI conference on human factors in computing systems.\u003c/em\u003e 2025. (pp. 1\u0026ndash;9). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1145/3706599.3720080\u003c/span\u003e\u003cspan address=\"10.1145/3706599.3720080\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi J, Yuan Q. The impact of AI-generated knowledge on user knowledge avoidance in OKCs: the moderating role of AI literacy and platform trust. J Knowl Manage. 2026:1\u0026ndash;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/JKM-02-2025-0231\u003c/span\u003e\u003cspan address=\"10.1108/JKM-02-2025-0231\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRadanliev P. AI ethics: Integrating transparency, fairness, and privacy in AI development. Appl Artif Intell. 2025;39(1):2463722. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/08839514.2025.2463722\u003c/span\u003e\u003cspan address=\"10.1080/08839514.2025.2463722\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSinghal A, Neveditsin N, Tanveer H, Mago V. Toward fairness, accountability, transparency, and ethics in AI for social media and health care: scoping review. JMIR Med Inf. 2024;12(1):e50048. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2196/50048\u003c/span\u003e\u003cspan address=\"10.2196/50048\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim B, Kim D. Understanding the Role of Trust, Perceived Risk, and Habit in Organization Members' Generative AI Use. Sage Open. 2026;16(1):21582440251410620. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/21582440251410620\u003c/span\u003e\u003cspan address=\"10.1177/21582440251410620\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaben SK, Lambiase J, Vasquez RA. Self-starters and lone rangers: Municipal government and nonprofit PR practitioners\u0026rsquo; approaches to AI training, ethics, and policy-making. Public Relations Rev. 2026;52(1):102664. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.pubrev.2025.102664\u003c/span\u003e\u003cspan address=\"10.1016/j.pubrev.2025.102664\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEsmaeilzadeh P. Patient safety and quality implications of large language model use in healthcare: a risk-stratified assessment of artificial intelligence-assisted medical consultations. Int J Qual Health Care. 2026;38(1):mzaf134. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/intqhc/mzaf134\u003c/span\u003e\u003cspan address=\"10.1093/intqhc/mzaf134\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDehnavieh R, Inayatullah S, Yousefi F, Nadali M. Artificial Intelligence (AI) and the future of Iran\u0026rsquo;s Primary Health Care (PHC) system. BMC Prim Care. 2025;26(1):75. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12875-025-02773-6\u003c/span\u003e\u003cspan address=\"10.1186/s12875-025-02773-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWardle C, Urbani S, Wang E. Evolving Health Information\u0026ndash;Seeking Behavior in the Context of Google AI Overviews, ChatGPT, and Alexa: Interview Study Using the Think-Aloud Protocol. J Med Internet Res. 2025;27:e79961. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2196/79961\u003c/span\u003e\u003cspan address=\"10.2196/79961\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlon L, Malinoff T. Understanding Barriers to AI Use Among Public Sector Knowledge Workers. \u003cem\u003eProceedings of the Association for Information Science and Technology\u003c/em\u003e. 2025;62(1):1346-8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/pra2.1399\u003c/span\u003e\u003cspan address=\"10.1002/pra2.1399\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmjad AI, Sheikh BS. Trusting the Machine: How AI Assessment Feedback and AI Literacy Shape Students' Idea Implementation Skills. Eur J Educ. 2026;61(1):e70457. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/ejed.70457\u003c/span\u003e\u003cspan address=\"10.1111/ejed.70457\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevkovich I, Alon L. AI knowledge to AI literacy: A serial mediation model of generative AI literacy among knowledge workers. \u003cem\u003eInternational Journal of Human\u0026ndash;Computer Interaction\u003c/em\u003e. 2026, in press.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"generative artificial intelligence, AI information literacy, healthcare professionals, ethics, technology adoption","lastPublishedDoi":"10.21203/rs.3.rs-9101750/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9101750/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines AI information literacy among healthcare professionals and identify differences across professional roles. An online survey was administered to 390 healthcare professionals including 22.3% medical doctors, 42.1% nurses, and 35.6% health professionals. AI information literacy was measured using the AI Information Literacy Scale (AILIS), which captures four dimensions: process/create, assess, retrieve, and ethics. Findings indicated that across AILIS dimensions, participants reported the greatest confidence in retrieving information with AI and the least confidence in critically assessing AI-generated outputs. Medical doctors scored significantly higher than nurses and health professionals on process/create and retrieve. Across all four regression models, self-perceived understanding of how AI tools work was the strongest predictor, and the models explained between 27.3% and 48.3% of the variance in AI information literacy dimensions. Age showed small negative associations in most models. AI information literacy among healthcare professionals appears multidimensional and uneven, with relatively lower confidence in critical assessment. GenAI integration in healthcare should therefore be approached as a literacy and training challenge, with particular attention to evaluation skills and reflective understanding of AI outputs.\u003c/p\u003e","manuscriptTitle":"AI Information Literacy in Healthcare Context: Profiles and Predictors Among Healthcare Professionals","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-23 12:37:42","doi":"10.21203/rs.3.rs-9101750/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-15T09:08:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T03:24:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-28T19:34:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"67710634403240862252014254006902300418","date":"2026-04-24T11:49:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"84308327899097660386105613508896735190","date":"2026-04-23T02:57:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-16T00:45:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"157831602867892394194096690188009403047","date":"2026-04-16T00:13:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-15T15:11:19+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-20T09:47:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-13T06:46:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-13T06:46:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2026-03-12T07:38:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"622a90c6-e713-43f8-8c3a-6e414de92d78","owner":[],"postedDate":"April 23rd, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-15T09:08:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T03:24:19+00:00","index":65,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T09:26:40+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-23 12:37:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9101750","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9101750","identity":"rs-9101750","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}