Transforming Urologic Care: Physician and Patient Insights on AI Integration

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Minhaj Siddiqui This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6280598/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction The integration of artificial intelligence (AI) in urologic practice offers promising advancements in diagnostics, prognostics, and therapeutic decision-making, with applications spanning the entire field of urology. As both patients and physicians adapt to AI in urology, this study examines their understanding of AI, highlighting similarities and differences in their perspectives. Methods An IRB-approved survey was created to assess awareness and perspectives on AI within the urologic community across the United States. Percentages were used to quantify the similarities and differences and statistical tests as appropriate were applied for significance. P value was set at 0.05. Surveys were distributed via email to both patients and urologists. Results In a survey of 380 participants (199 physicians (phy), 181 patients (pts)), both groups shared a baseline unfamiliarity with AI in general (59.3% phy vs. 65.8% pts, p=0.2) and AI in healthcare (71.4% phy vs. 76.8% pts, p=0.2). A majority of each group expressed optimism about AI’s clinical utility (61.3% phy vs. 74.6% pts, p=0.006) but held mixed trust in its accuracy with physicians showing greater mistrust (49.7% phy vs. 34.2% pts, p=0.001). Ethical and privacy concerns were notable, with more physicians than patients emphasizing ethical issues (38.7% vs. 22.1%, p=0.001) and significant privacy risks (50.8% vs. 42.0%, p=0.2). Both groups favored shared accountability for AI-driven outcomes (71.3% phy vs. 63.3% pts, p=0.2) and human oversight (78.4% phy vs. 66.9% pts, p=0.01), underscoring a need for cautious AI integration in urology. Conclusions This study reveals alignment between physicians and patients on AI’s potential in urology, alongside shared concerns about its reliability, ethics, and oversight. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction In recent years, the integration of artificial intelligence (AI) into urologic practice has emerged as a promising frontier, offering innovative solutions for diagnostic, prognostic, and therapeutic decision-making. 1 , 2 The application of AI in urology has been investigated across diverse domains 3 , including pediatric urology 4 , diagnosis of bladder cancer 5 , and treatment of benign prostatic hyperplasia (BPH) 6 . Concurrently, ethical implications surrounding the use of AI in urologic practice have been examined, considering issues such as data privacy, algorithmic bias, and the impact on patient-doctor relationships. 7 , 8 However, the rapid evolution of AI applications in urology has raised significant regulatory challenges, 9 necessitating a critical examination of the current framework and integration of AI into the clinical workflow by all interested parties. This paper examines the perspectives of patients and providers on the integration of AI into urology, shedding light on both shared viewpoints and key differences. Understanding these perspectives from both the patient and physician point of view is crucial for navigating the challenges and opportunities that AI presents in urologic care. A growing body of research highlights the importance of understanding patient and provider perspectives on the integration of AI into clinical practice. A recent Pew Research Center survey found that 60% of Americans would feel uncomfortable with their healthcare providers relying heavily on AI for their own medical care, reflecting widespread hesitations about this technology. 10 Concerns cited included fears of AI making incorrect decisions, a lack of transparency, and potential disruptions to the patient-provider relationship. Similarly, studies focusing on provider perspectives reveal a mix of enthusiasm and skepticism regarding AI integration. A 2023 study found that while many healthcare professionals recognize the potential for AI to improve diagnostic accuracy and operational efficiency, they also express reservations about algorithmic bias, the erosion of clinical judgment, and liability issues in cases of AI-related errors. 11 These concerns are particularly relevant in fields like urology, where decision-making often involves complex and individualized patient care. Patient and provider perspectives also vary depending on the specific AI application. For example, a systematic review highlighted that patients tend to be more accepting of AI for administrative tasks, such as appointment scheduling, but exhibit greater discomfort regarding its use in diagnostic or treatment decisions. 12 Providers, however, are more likely to embrace AI when they perceive it as a tool to enhance, rather than replace, their clinical expertise. 11 These studies emphasize the critical role of education and transparent communication in bridging gaps in understanding between patients and providers, fostering a more collaborative approach to AI adoption in healthcare. Despite these challenges, there is growing recognition of the need to integrate patient and provider feedback into the design and implementation of AI systems. Research points to the importance of participatory approaches that involve end-users early in the development process to ensure the technology meets clinical and ethical standards. 13 By understanding and addressing the perspectives of patients and providers, the healthcare community can facilitate a more seamless and equitable integration of AI, ultimately enhancing patient outcomes and satisfaction. This study offers a novel contribution by directly comparing and contrasting patient and provider perspectives on the use of AI in healthcare. By focusing specifically on the context of urologic practice, this research provides unique insights into how these stakeholders perceive the ethical, regulatory, and clinical implications of AI technologies in a specialized medical domain. This comparative approach is crucial for understanding where alignment and divergence occur, ensuring that both patient concerns and provider needs are addressed as AI applications continue to evolve in the field of urology. Materials and Methods This study, through a survey disseminated to patients and providers, explored the application of AI in urology, with a focus on emerging frontiers, ethical considerations, and regulatory frameworks. Our study sought to identify new opportunities for AI in urology while critically examining the relevant ethical, legal, and regulatory implications. The survey was distributed through REDCap to members of the urology community to assess their awareness and understanding of the ethical and regulatory implications of AI adoption. The survey captured input from two main groups: patients, mostly prostate cancer survivors, and physicians, predominantly general urologists and urologic oncologists. Patients were recruited through a network of prostate cancer survivors, with distribution via email, two newsletters (Europa Uomo and AnCan), and a blog post (The Active Surveillor). Physician participants were recruited via email outreach directed at members of the American Urological Association. The survey gathered diverse perspectives on issues such as data privacy, patient consent, liability, and regulatory oversight. It aimed to evaluate the significance and prevalence of these concerns within the field. The study was approved by the University of Maryland Institutional Review Board (HP-00109759). The survey distributed to patient and provider respondents is available in the Appendix of this article. Results Demographics and Professional Characteristics The survey collected responses from 380 participants, including 199 physicians and 181 patients, to explore their perspectives on the integration of artificial intelligence in urologic practice (Table 1). A majority of both groups self-identified as cisgender males (87.9% of physicians and 99.0% of patients) and as white (77.9% of physicians and 95.0% of patients). (Table 1). Among the physicians, nearly all respondents were attending physicians at the time of the survey (98.0%), and a significant portion had been practicing urology for over 20 years (70.4%). (Table 2). Physician respondents were evenly split between academic teaching hospitals (40.2%) private practice settings (41.7%). (Table 2). The majority of physician respondents reported having active clinical responsibilities (86.9%), with their primary research interests in general urology (41.7%) and urologic oncology (19.1%). (Table 2). Familiarity with Artificial Intelligence An analysis of AI familiarity revealed no significant differences between physicians and patients in terms of self-reported unfamiliarity with AI, both in general (59.3% vs. 65.8% expressing “somewhat” or less familiar, p=0.2) (Figure 1) and specifically in healthcare contexts (71.4% vs. 76.8%, p=0.2) (Figure 1). These findings suggest a comparable baseline of AI awareness across both groups, indicating that educational initiatives targeting AI in healthcare could be similarly structured for both physicians and patients. Perceived Utility and Trust in AI Regarding the perceived utility of AI in clinical practice, both physicians and patients demonstrated comparable levels of agreement (61.3% vs. 74.6% “moderately” useful or more, p=0.006), reflecting a shared optimism about the potential benefits of AI in urology. (Figure 1). Additionally, when asked about their trust in AI to provide accurate information, responses were mixed, with 49.7% of physicians and 34.2% of patients, “agree” or more, indicating agreement (p=0.001). (Figure 2). Concerns about the potential for AI to disseminate false information were prevalent in both groups (77.4% of physicians vs. 71.3% of patients “agree” or more, p=0.6), highlighting a cautious approach to the adoption of AI technologies, despite recognizing their potential advantages. Ethical Considerations Ethical considerations were also a prominent concern. Interestingly, physicians and patients demonstrated differing levels of concern regarding the presence of ethical issues to be addressed before implementing AI in urologic practice (38.7% vs. 22.1% “agree” or more, p=0.001). This divergence highlights the importance of establishing robust ethical frameworks that account for diverse stakeholder perspectives, ensuring the development and use of AI in healthcare align with both clinical priorities and societal values. Accountability for AI-Driven Outcomes When considering accountability for AI-driven outcomes, the majority of respondents from both groups believed that responsibility should be shared between the AI system and the end user (71.3% of physicians vs. 63.3% of patients selecting “both”, p=0.2). (Figure 4). A smaller proportion felt that the end user alone should be held accountable (33.2% of physicians vs. 25.4% of patients selecting “the user”, p=0.2). This consensus suggests a preference for a collaborative responsibility model, which could foster more cautious and responsible use of AI in clinical settings. Privacy and Data Security Concerns Concerns surrounding privacy and data security were also highlighted, with half of the physicians and a considerable portion of patients expressing apprehension about privacy risks associated with AI in healthcare (50.8% vs. 42.0%, p=0.2). (Figure 5). Furthermore, there was significant agreement that AI’s potential for inaccuracies, such as artificial hallucinations, posed a notable risk (77.4% of physicians vs. 55.2% of patients, p=0.01). Additionally, the lack of human oversight when deploying AI in clinical practice was a shared concern (78.4% of physicians vs. 66.9% of patients, p=0.01), emphasizing the need for human involvement in AI applications to maintain patient safety and trust. Impact of AI on Clinical Practice In terms of the practical impact of AI on urologic practice, many physicians reported that AI had little to no effect on their productivity, with 19.6% noting an insignificant impact and 36.2% observing no impact at all. (Figure 3). Both groups expressed uncertainty about whether patients would be likely to accept medical information provided by AI, with a substantial proportion of respondents reporting neutral opinions (40.2% of physicians vs. 49.2% of patients, p=0.4). (Figure 5). Nonetheless, a strong majority from both groups agreed that AI could offer valuable contributions to urology, with 77.4% of physicians and 82.3% of patients endorsing its potential benefits. (Figure 5). Discussion This study presents a nuanced understanding of the perspectives of physicians and patients regarding the use of artificial intelligence (AI) in urologic practice. Remarkably, both groups demonstrate a strong alignment in their views, highlighting the broader implications of AI integration into healthcare and the critical need for collaborative approaches to address shared concerns. General AI Familiarity and Perspectives in Healthcare The findings reveal that both physicians and patients exhibit a comparable level of familiarity with AI, reflecting a collective understanding of its potential and limitations. Despite advancements in AI technology, there remains a notable gap in comprehensive knowledge and confidence in its applications across both groups. This situation underscores the importance of targeted educational initiatives that can demystify AI and enhance understanding among both healthcare professionals and patients. Providing clear, accessible information about AI’s capabilities will be vital in fostering a more informed environment that embraces AI innovations in clinical practice. 14,15 Perceived Utility and Trust in AI The optimism surrounding AI’s utility in clinical practice is palpable among both physicians and patients, illustrating a shared recognition of its potential to enhance diagnostic accuracy and streamline workflows. This optimism is tempered, however, by a collective apprehension regarding the reliability of AI-generated information. Trust in AI remains a pivotal barrier to its widespread adoption, with concerns about the accuracy and transparency of AI outputs dominating the conversation. Addressing these trust issues will require robust validation processes, transparency in AI algorithms, and consistent communication about the limitations and capabilities of AI technologies. Engaging both clinicians and patients in this dialogue can enhance confidence and foster a culture of trust. 16,17,18 Ethical Concerns and Regulatory Considerations Ethical considerations surrounding AI in healthcare emerged as a significant concern for both groups, emphasizing the need for comprehensive frameworks to guide AI deployment. There is a shared recognition that ethical challenges—including privacy, data security, and algorithmic bias—must be addressed to ensure responsible integration into clinical practice. 19,20 This shared perspective highlights the necessity of involving diverse stakeholders in the development of ethical guidelines that reflect the values and priorities of both healthcare providers and patients. Establishing clear regulatory frameworks will be crucial in mitigating risks associated with AI while ensuring that the technology is leveraged to benefit patient care. The preference for a shared responsibility model regarding AI outcomes indicates a mature understanding of the complexities involved in AI integration. Both physicians and patients recognize that while AI can significantly enhance clinical decision-making, accountability must remain a collaborative endeavor. This approach fosters a culture of safety and encourages a continuous dialogue around the ethical implications of AI technologies in healthcare. 21,22 Future Directions and Implications The strong consensus regarding AI’s potential benefits in urology, informed by interviews with key stakeholders—including urologists, patient advocates, and regulators—underscores a promising landscape for the continued development and integration of AI-driven innovations. These stakeholders expressed shared enthusiasm, which provides a solid foundation for expanding AI applications tailored to the specific needs of the urology field. However, to fully realize the transformative potential of AI, insights gathered from these interviews emphasize the need for a concerted focus on transparency, ethics, and inclusive engagement with all involved parties. An adaptable and comprehensive regulatory framework, as highlighted by both clinicians and regulators in the interviews, is crucial to ensure the responsible and safe deployment of AI technologies in urology. The framework should prioritize transparency in algorithmic decision-making, allowing healthcare providers and patients to understand how AI tools reach their conclusions. The interviews revealed that many urologists and patient advocates harbor concerns about the opaque nature of AI decision-making. Transparent systems, where data flows and decision pathways are clearly explained, would help alleviate this skepticism and encourage greater trust and adoption in clinical practice. Moreover, stakeholders underscored the critical importance of patient data privacy. Given AI’s reliance on extensive datasets for training and improvement, concerns about the security and proper handling of sensitive patient information were frequently raised in interviews. Regulatory frameworks must address these concerns by implementing robust encryption methods, anonymization protocols, and stringent data access controls. By doing so, regulators and policymakers can foster an environment of trust, which is vital for the ethical and sustained integration of AI into urological care. Another theme that emerged from the interviews with stakeholders is the pressing need to avoid bias in AI algorithms. Urologists and patient advocates alike voiced concerns that biases in AI training data could lead to inaccurate diagnoses or skewed treatment recommendations, disproportionately affecting underrepresented populations. The future direction of AI in urology must involve concerted efforts to ensure that training datasets are diverse and representative of various demographic, socioeconomic, and geographic factors. This will be critical to preventing health disparities and ensuring that AI systems promote equitable healthcare outcomes. Regulatory oversight, as emphasized by the interviewed regulators, will need to enforce rigorous validation processes that detect and correct biases in AI systems before they are widely deployed. While the potential for AI is vast, its successful integration hinges on developing a regulatory framework that is adaptable to technological advancements while ensuring transparency, protecting patient data, and eliminating bias. These principles, combined with ongoing stakeholder engagement, will be crucial to unlocking AI’s full potential and ensuring its ethical and effective use in urology. This study has some limitations. The use of voluntary physician and patient surveys may bias the findings of the study related the ability to sample a broad range of respondents. Our access to patients in particular was skewed heavily towards males as we relied on relationships with urologic cancer advocacy groups to disseminate amongst urology patients. Conclusion In summary, this study illuminates a significant alignment between physicians and patients regarding their views on AI in urology. Both groups recognize the transformative potential of AI while expressing legitimate concerns about its reliability, ethical implications, and regulatory oversight. To harness the full potential of AI in urology and beyond, it is imperative that future initiatives focus on education, ethical considerations, and collaborative governance, ultimately ensuring that AI is integrated safely and effectively into clinical practice. Declarations Source of funding This research was supported in part by the Program for Research Initiated by Students and Mentors (PRISM), University of Maryland School of Medicine Office of Student Research. Disclosures The authors have no relevant financial or non-financial disclosures to declare in relation to this manuscript. IRB Statement The study was approved by the University of Maryland Institutional Review Board (HP-00109759). Author Contribution P.E. and M.S. conceptualized the study and contributed to manuscript drafting and revisions. A.K. and K.X. assisted with data analysis and contribtued to revising the manuscript. H.W. contributed to data collection. All authors reviewed and approved the final manuscript. References Anderer S, Hswen Y. "Scalable Privilege"-How AI Could Turn Data From the Best Medical Systems Into Better Care for All. JAMA. 2024 Feb 13;331(6):459-462. doi: 10.1001/jama.2023.21719. PMID: 38265824. Eppler M, Ganjavi C, Ramacciotti LS, et al. Awareness and Use of ChatGPT and Large Language Models: A Prospective Cross-sectional Global Survey in Urology. Eur Urol. 2024 Feb;85(2):146-153. doi: 10.1016/j.eururo.2023.10.014. Epub 2023 Nov 4. PMID: 37926642. Oh JK, Lee JY, Eun SJ, Park JM. New Trends in Innovative Technologies Applying Artificial Intelligence to Urinary Diseases. 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Nat Mach Intell 1, 389–399 (2019). https://doi.org/10.1038/s42256-019-0088-2 Tables Table 2 – Physician Respondent Demographics Practice Location (AUA Sections) New England (CT, ME, MA, NH, RI, VT) 9 (4.5) New York (NY) 13 (6.5) Mid-Atlantic (DE, PA, MD, NJ, VA, WV) 34 (17.1) North Central (IL, IN, IA, MI, MN, ND, OH, SD, WI) 36 (18.1) Southeastern (AL, FL, GA, KT, LA, MS, NC, SC, TN) 39 (19.6) South Central (AR, CO, KS, MO, NE, NW, OK, TX) 22 (11.1) Western (AK, AZ, CA, HI, ID, MO, NV, OR, UT, WA, WY) 36 (18.1) No Response 10 (5.0) Years in Practice 20 years 140 (70.4) No response 1 (0.5) Position Title Attending 195 (98.0) Resident/Fellow 2 (1.0) No response 2 (1.0) Work Environment Teaching Hospital/ Academic Institution 80 (40.2) Nonacademic Public Hospital 26 (13.1) Private Practice 83 (41.7) Mix of Public and Private 8 (4.0) No response 2 (1.0) Job Responsibilities Active Clinical 173 (86.9) Active Research 0 (0) Retired from Clinical 22 (11.1) Temporary Break from Clinical 4 (2.0) Never been in Clinical 0 (0) No response 0 (0) Research Interests Pediatric Urology 10 (5.0) Female Urology 13 (6.5) Andrology 4 (2.0) Neurourology 1 (0.5) Urologic Oncology 38 (19.1) Reconstructive Urology 4 (2.0) Renal Transplant 1 (0.5) Endourology 16 (8.1) General Urology 83 (41.7) Other 28 (14.1) No response 1 (0.5) Additional Declarations No competing interests reported. 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Minhaj Siddiqui","email":"","orcid":"","institution":"University of Maryland School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"M.","middleName":"Minhaj","lastName":"Siddiqui","suffix":""}],"badges":[],"createdAt":"2025-03-21 23:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6280598/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6280598/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82053482,"identity":"6607d06d-53f2-476b-af1b-7d8ec647dc69","added_by":"auto","created_at":"2025-05-06 10:12:57","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":419351,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6280598/v1/2c6a5d80fda6b6002e53122c.jpeg"},{"id":82051747,"identity":"55509844-3936-4a98-9162-3dad98b7d593","added_by":"auto","created_at":"2025-05-06 10:04:57","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":377943,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6280598/v1/95c9f4c7e6fc460c755acc8d.jpeg"},{"id":82053481,"identity":"6abd4306-f97d-455b-bb8b-5343db12a5df","added_by":"auto","created_at":"2025-05-06 10:12:57","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":245347,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6280598/v1/ac3b41deb7a705a7f984263d.jpeg"},{"id":82051757,"identity":"de6b0370-69a6-48ef-a741-dcfe3573b809","added_by":"auto","created_at":"2025-05-06 10:04:57","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":204666,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6280598/v1/9e2704765a8721388a40a62d.jpeg"},{"id":82053484,"identity":"894510f7-8f41-4fb3-ac05-8b8ecced5cad","added_by":"auto","created_at":"2025-05-06 10:12:57","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":421042,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6280598/v1/1cbb7d92f8136ee9e9f0792a.jpeg"},{"id":82056500,"identity":"fc4ae770-b05f-4e3d-b5c1-15eec248f0df","added_by":"auto","created_at":"2025-05-06 10:37:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2185594,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6280598/v1/3c3aee1c-46d0-4f69-b87a-0b5c6ef3b651.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transforming Urologic Care: Physician and Patient Insights on AI Integration","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn recent years, the integration of artificial intelligence (AI) into urologic practice has emerged as a promising frontier, offering innovative solutions for diagnostic, prognostic, and therapeutic decision-making.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e The application of AI in urology has been investigated across diverse domains\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, including pediatric urology\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, diagnosis of bladder cancer\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, and treatment of benign prostatic hyperplasia (BPH)\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Concurrently, ethical implications surrounding the use of AI in urologic practice have been examined, considering issues such as data privacy, algorithmic bias, and the impact on patient-doctor relationships.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e However, the rapid evolution of AI applications in urology has raised significant regulatory challenges,\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e necessitating a critical examination of the current framework and integration of AI into the clinical workflow by all interested parties.\u003c/p\u003e \u003cp\u003eThis paper examines the perspectives of patients and providers on the integration of AI into urology, shedding light on both shared viewpoints and key differences. Understanding these perspectives from both the patient and physician point of view is crucial for navigating the challenges and opportunities that AI presents in urologic care.\u003c/p\u003e \u003cp\u003eA growing body of research highlights the importance of understanding patient and provider perspectives on the integration of AI into clinical practice. A recent Pew Research Center survey found that 60% of Americans would feel uncomfortable with their healthcare providers relying heavily on AI for their own medical care, reflecting widespread hesitations about this technology.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Concerns cited included fears of AI making incorrect decisions, a lack of transparency, and potential disruptions to the patient-provider relationship.\u003c/p\u003e \u003cp\u003eSimilarly, studies focusing on provider perspectives reveal a mix of enthusiasm and skepticism regarding AI integration. A 2023 study found that while many healthcare professionals recognize the potential for AI to improve diagnostic accuracy and operational efficiency, they also express reservations about algorithmic bias, the erosion of clinical judgment, and liability issues in cases of AI-related errors.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e These concerns are particularly relevant in fields like urology, where decision-making often involves complex and individualized patient care.\u003c/p\u003e \u003cp\u003ePatient and provider perspectives also vary depending on the specific AI application. For example, a systematic review highlighted that patients tend to be more accepting of AI for administrative tasks, such as appointment scheduling, but exhibit greater discomfort regarding its use in diagnostic or treatment decisions.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e Providers, however, are more likely to embrace AI when they perceive it as a tool to enhance, rather than replace, their clinical expertise.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e These studies emphasize the critical role of education and transparent communication in bridging gaps in understanding between patients and providers, fostering a more collaborative approach to AI adoption in healthcare.\u003c/p\u003e \u003cp\u003eDespite these challenges, there is growing recognition of the need to integrate patient and provider feedback into the design and implementation of AI systems. Research points to the importance of participatory approaches that involve end-users early in the development process to ensure the technology meets clinical and ethical standards.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e By understanding and addressing the perspectives of patients and providers, the healthcare community can facilitate a more seamless and equitable integration of AI, ultimately enhancing patient outcomes and satisfaction.\u003c/p\u003e \u003cp\u003eThis study offers a novel contribution by directly comparing and contrasting patient and provider perspectives on the use of AI in healthcare. By focusing specifically on the context of urologic practice, this research provides unique insights into how these stakeholders perceive the ethical, regulatory, and clinical implications of AI technologies in a specialized medical domain. This comparative approach is crucial for understanding where alignment and divergence occur, ensuring that both patient concerns and provider needs are addressed as AI applications continue to evolve in the field of urology.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e This study, through a survey disseminated to patients and providers, explored the application of AI in urology, with a focus on emerging frontiers, ethical considerations, and regulatory frameworks. Our study sought to identify new opportunities for AI in urology while critically examining the relevant ethical, legal, and regulatory implications.\u003c/p\u003e \u003cp\u003eThe survey was distributed through REDCap to members of the urology community to assess their awareness and understanding of the ethical and regulatory implications of AI adoption. The survey captured input from two main groups: patients, mostly prostate cancer survivors, and physicians, predominantly general urologists and urologic oncologists. Patients were recruited through a network of prostate cancer survivors, with distribution via email, two newsletters (Europa Uomo and AnCan), and a blog post (The Active Surveillor). Physician participants were recruited via email outreach directed at members of the American Urological Association. The survey gathered diverse perspectives on issues such as data privacy, patient consent, liability, and regulatory oversight. It aimed to evaluate the significance and prevalence of these concerns within the field. The study was approved by the University of Maryland Institutional Review Board (HP-00109759).\u003c/p\u003e \u003cp\u003eThe survey distributed to patient and provider respondents is available in the Appendix of this article.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eDemographics and Professional Characteristics\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe survey collected responses from 380 participants, including 199 physicians and 181 patients, to explore their perspectives on the integration of artificial intelligence in urologic practice (Table 1). A majority of both groups self-identified as cisgender males (87.9% of physicians and 99.0% of patients) and as white (77.9% of physicians and 95.0% of patients). (Table 1). Among the physicians, nearly all respondents were attending physicians at the time of the survey (98.0%), and a significant portion had been practicing urology for over 20 years (70.4%). (Table 2). Physician respondents were evenly split between academic teaching hospitals (40.2%) \u0026nbsp;private practice settings (41.7%). (Table 2). The majority of physician respondents reported having active clinical responsibilities (86.9%), with their primary research interests in general urology (41.7%) and urologic oncology (19.1%). (Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFamiliarity with Artificial Intelligence\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAn analysis of AI familiarity revealed no significant differences between physicians and patients in terms of self-reported unfamiliarity with AI, both in general (59.3% vs. 65.8% expressing \u0026ldquo;somewhat\u0026rdquo; or less familiar, p=0.2) (Figure 1) and specifically in healthcare contexts (71.4% vs. 76.8%, p=0.2) (Figure 1). These findings suggest a comparable baseline of AI awareness across both groups, indicating that educational initiatives targeting AI in healthcare could be similarly structured for both physicians and patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePerceived Utility and Trust in AI\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eRegarding the perceived utility of AI in clinical practice, both physicians and patients demonstrated comparable levels of agreement (61.3% vs. 74.6% \u0026ldquo;moderately\u0026rdquo; useful or more, p=0.006), reflecting a shared optimism about the potential benefits of AI in urology. (Figure 1). Additionally, when asked about their trust in AI to provide accurate information, responses were mixed, with 49.7% of physicians and 34.2% of patients, \u0026ldquo;agree\u0026rdquo; or more, indicating agreement (p=0.001). (Figure 2). Concerns about the potential for AI to disseminate false information were prevalent in both groups (77.4% of physicians vs. 71.3% of patients \u0026ldquo;agree\u0026rdquo; or more, p=0.6), highlighting a cautious approach to the adoption of AI technologies, despite recognizing their potential advantages.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthical Considerations\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eEthical considerations were also a prominent concern. Interestingly, physicians and patients demonstrated differing levels of concern regarding the presence of ethical issues to be addressed before implementing AI in urologic practice (38.7% vs. 22.1% \u0026ldquo;agree\u0026rdquo; or more, p=0.001). This divergence highlights the importance of establishing robust ethical frameworks that account for diverse stakeholder perspectives, ensuring the development and use of AI in healthcare align with both clinical priorities and societal values.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAccountability for AI-Driven Outcomes\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWhen considering accountability for AI-driven outcomes, the majority of respondents from both groups believed that responsibility should be shared between the AI system and the end user (71.3% of physicians vs. 63.3% of patients selecting \u0026ldquo;both\u0026rdquo;, p=0.2). (Figure 4). A smaller proportion felt that the end user alone should be held accountable (33.2% of physicians vs. 25.4% of patients selecting \u0026ldquo;the user\u0026rdquo;, p=0.2). This consensus suggests a preference for a collaborative responsibility model, which could foster more cautious and responsible use of AI in clinical settings.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePrivacy and Data Security Concerns\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eConcerns surrounding privacy and data security were also highlighted, with half of the physicians and a considerable portion of patients expressing apprehension about privacy risks associated with AI in healthcare (50.8% vs. 42.0%, p=0.2). (Figure 5). Furthermore, there was significant agreement that AI\u0026rsquo;s potential for inaccuracies, such as artificial hallucinations, posed a notable risk (77.4% of physicians vs. 55.2% of patients, p=0.01). Additionally, the lack of human oversight when deploying AI in clinical practice was a shared concern (78.4% of physicians vs. 66.9% of patients, p=0.01), emphasizing the need for human involvement in AI applications to maintain patient safety and trust.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eImpact of AI on Clinical Practice\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn terms of the practical impact of AI on urologic practice, many physicians reported that AI had little to no effect on their productivity, with 19.6% noting an insignificant impact and 36.2% observing no impact at all. (Figure 3). Both groups expressed uncertainty about whether patients would be likely to accept medical information provided by AI, with a substantial proportion of respondents reporting neutral opinions (40.2% of physicians vs. 49.2% of patients, p=0.4). (Figure 5). Nonetheless, a strong majority from both groups agreed that AI could offer valuable contributions to urology, with 77.4% of physicians and 82.3% of patients endorsing its potential benefits. (Figure 5).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study presents a nuanced understanding of the perspectives of physicians and patients regarding the use of artificial intelligence (AI) in urologic practice. Remarkably, both groups demonstrate a strong alignment in their views, highlighting the broader implications of AI integration into healthcare and the critical need for collaborative approaches to address shared concerns.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGeneral AI Familiarity and Perspectives in Healthcare\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe findings reveal that both physicians and patients exhibit a comparable level of familiarity with AI, reflecting a collective understanding of its potential and limitations. Despite advancements in AI technology, there remains a notable gap in comprehensive knowledge and confidence in its applications across both groups. This situation underscores the importance of targeted educational initiatives that can demystify AI and enhance understanding among both healthcare professionals and patients. Providing clear, accessible information about AI\u0026rsquo;s capabilities will be vital in fostering a more informed environment that embraces AI innovations in clinical practice.\u003csup\u003e14,15\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePerceived Utility and Trust in AI\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe optimism surrounding AI\u0026rsquo;s utility in clinical practice is palpable among both physicians and patients, illustrating a shared recognition of its potential to enhance diagnostic accuracy and streamline workflows. This optimism is tempered, however, by a collective apprehension regarding the reliability of AI-generated information. Trust in AI remains a pivotal barrier to its widespread adoption, with concerns about the accuracy and transparency of AI outputs dominating the conversation. Addressing these trust issues will require robust validation processes, transparency in AI algorithms, and consistent communication about the limitations and capabilities of AI technologies. Engaging both clinicians and patients in this dialogue can enhance confidence and foster a culture of trust.\u003csup\u003e16,17,18\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthical Concerns and Regulatory Considerations\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthical considerations surrounding AI in healthcare emerged as a significant concern for both groups, emphasizing the need for comprehensive frameworks to guide AI deployment. There is a shared recognition that ethical challenges\u0026mdash;including privacy, data security, and algorithmic bias\u0026mdash;must be addressed to ensure responsible integration into clinical practice.\u003csup\u003e19,20\u003c/sup\u003e This shared perspective highlights the necessity of involving diverse stakeholders in the development of ethical guidelines that reflect the values and priorities of both healthcare providers and patients. Establishing clear regulatory frameworks will be crucial in mitigating risks associated with AI while ensuring that the technology is leveraged to benefit patient care.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe preference for a shared responsibility model regarding AI outcomes indicates a mature understanding of the complexities involved in AI integration. Both physicians and patients recognize that while AI can significantly enhance clinical decision-making, accountability must remain a collaborative endeavor. This approach fosters a culture of safety and encourages a continuous dialogue around the ethical implications of AI technologies in healthcare.\u003csup\u003e21,22\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFuture Directions and Implications\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe strong consensus regarding AI\u0026rsquo;s potential benefits in urology, informed by interviews with key stakeholders\u0026mdash;including urologists, patient advocates, and regulators\u0026mdash;underscores a promising landscape for the continued development and integration of AI-driven innovations. These stakeholders expressed shared enthusiasm, which provides a solid foundation for expanding AI applications tailored to the specific needs of the urology field. However, to fully realize the transformative potential of AI, insights gathered from these interviews emphasize the need for a concerted focus on transparency, ethics, and inclusive engagement with all involved parties.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAn adaptable and comprehensive regulatory framework, as highlighted by both clinicians and regulators in the interviews, is crucial to ensure the responsible and safe deployment of AI technologies in urology. The framework should prioritize transparency in algorithmic decision-making, allowing healthcare providers and patients to understand how AI tools reach their conclusions. The interviews revealed that many urologists and patient advocates harbor concerns about the opaque nature of AI decision-making. Transparent systems, where data flows and decision pathways are clearly explained, would help alleviate this skepticism and encourage greater trust and adoption in clinical practice.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMoreover, stakeholders underscored the critical importance of patient data privacy. Given AI\u0026rsquo;s reliance on extensive datasets for training and improvement, concerns about the security and proper handling of sensitive patient information were frequently raised in interviews. Regulatory frameworks must address these concerns by implementing robust encryption methods, anonymization protocols, and stringent data access controls. By doing so, regulators and policymakers can foster an environment of trust, which is vital for the ethical and sustained integration of AI into urological care.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnother theme that emerged from the interviews with stakeholders is the pressing need to avoid bias in AI algorithms. Urologists and patient advocates alike voiced concerns that biases in AI training data could lead to inaccurate diagnoses or skewed treatment recommendations, disproportionately affecting underrepresented populations. The future direction of AI in urology must involve concerted efforts to ensure that training datasets are diverse and representative of various demographic, socioeconomic, and geographic factors. This will be critical to preventing health disparities and ensuring that AI systems promote equitable healthcare outcomes. Regulatory oversight, as emphasized by the interviewed regulators, will need to enforce rigorous validation processes that detect and correct biases in AI systems before they are widely deployed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile the potential for AI is vast, its successful integration hinges on developing a regulatory framework that is adaptable to technological advancements while ensuring transparency, protecting patient data, and eliminating bias. These principles, combined with ongoing stakeholder engagement, will be crucial to unlocking AI\u0026rsquo;s full potential and ensuring its ethical and effective use in urology.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study has some limitations. The use of voluntary physician and patient surveys may bias the findings of the study related the ability to sample a broad range of respondents. Our access to patients in particular was skewed heavily towards males as we relied on relationships with urologic cancer advocacy groups to disseminate amongst urology patients.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, this study illuminates a significant alignment between physicians and patients regarding their views on AI in urology. Both groups recognize the transformative potential of AI while expressing legitimate concerns about its reliability, ethical implications, and regulatory oversight. To harness the full potential of AI in urology and beyond, it is imperative that future initiatives focus on education, ethical considerations, and collaborative governance, ultimately ensuring that AI is integrated safely and effectively into clinical practice.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSource of funding\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported in part by the Program for Research Initiated by Students and Mentors (PRISM), University of Maryland School of Medicine Office of Student Research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosures\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial disclosures to declare in relation to this manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIRB Statement\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study was approved by the University of Maryland Institutional Review Board (HP-00109759).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eP.E. and M.S. conceptualized the study and contributed to manuscript drafting and revisions. A.K. and K.X. assisted with data analysis and contribtued to revising the manuscript. H.W. contributed to data collection. All authors reviewed and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAnderer S, Hswen Y. \u0026quot;Scalable Privilege\u0026quot;-How AI Could Turn Data From the Best Medical Systems Into Better Care for All. JAMA. 2024 Feb 13;331(6):459-462. doi: 10.1001/jama.2023.21719. PMID: 38265824.\u003c/li\u003e\n \u003cli\u003eEppler M, Ganjavi C, Ramacciotti LS, et al.\u0026nbsp;Awareness and Use of ChatGPT and Large Language Models: A Prospective Cross-sectional Global Survey in Urology. Eur Urol. 2024 Feb;85(2):146-153. doi: 10.1016/j.eururo.2023.10.014. Epub 2023 Nov 4. PMID: 37926642.\u003c/li\u003e\n \u003cli\u003eOh JK, Lee JY, Eun SJ, Park JM. New Trends in Innovative Technologies Applying Artificial Intelligence to Urinary Diseases. 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The global landscape of AI ethics guidelines. \u003cem\u003eNat Mach Intell\u003c/em\u003e 1, 389\u0026ndash;399 (2019). https://doi.org/10.1038/s42256-019-0088-2\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cimg 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\" width=\"975\" height=\"654\"\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"623\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003eTable 2 \u0026ndash; Physician Respondent Demographics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003ePractice Location (AUA Sections)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eNew England (CT, ME, MA, NH, RI, VT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e9 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eNew York (NY)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e13 (6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eMid-Atlantic (DE, PA, MD, NJ, VA, WV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e34 (17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eNorth Central (IL, IN, IA, MI, MN, ND, OH, SD, WI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e36 (18.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eSoutheastern (AL, FL, GA, KT, LA, MS, NC, SC, TN)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e39 (19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eSouth Central (AR, CO, KS, MO, NE, NW, OK, TX)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e22 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eWestern (AK, AZ, CA, HI, ID, MO, NV, OR, UT, WA, WY)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e36 (18.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eNo Response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e10 (5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003eYears in Practice\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u0026lt;10 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e5 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e10\u0026ndash;20 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e53 (26.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u0026gt;20 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e140 (70.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eNo response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e1 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003ePosition Title\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eAttending\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e195 (98.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eResident/Fellow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e2 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eNo response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e2 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003eWork Environment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eTeaching Hospital/ Academic Institution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e80 (40.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eNonacademic Public Hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e26 (13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003ePrivate Practice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e83 (41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eMix of Public and Private\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e8 (4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eNo response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e2 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003eJob Responsibilities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eActive Clinical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e173 (86.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eActive Research\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eRetired from Clinical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e22 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eTemporary Break from Clinical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e4 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eNever been in Clinical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eNo response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003eResearch Interests\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003ePediatric Urology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e10 (5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eFemale Urology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e13 (6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eAndrology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e4 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eNeurourology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e1 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eUrologic Oncology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e38 (19.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eReconstructive Urology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e4 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eRenal Transplant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e1 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eEndourology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e16 (8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGeneral Urology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e83 (41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e28 (14.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eNo response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e1 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6280598/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6280598/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe integration of artificial intelligence (AI) in urologic practice offers promising advancements in diagnostics, prognostics, and therapeutic decision-making, with applications spanning the entire field of urology. As both patients and physicians adapt to AI in urology, this study examines their understanding of AI, highlighting similarities and differences in their perspectives.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn IRB-approved survey was created to assess awareness and perspectives on AI within the urologic community across the United States. Percentages were used to quantify the similarities and differences and statistical tests as appropriate were applied for significance. P value was set at 0.05. Surveys were distributed via email to both patients and urologists.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn a survey of 380 participants (199 physicians (phy), 181 patients (pts)), both groups shared a baseline unfamiliarity with AI in general (59.3% phy vs. 65.8% pts, p=0.2) and AI in healthcare (71.4% phy vs. 76.8% pts, p=0.2). A majority of each group expressed optimism about AI’s clinical utility (61.3% phy vs. 74.6% pts, p=0.006) but held mixed trust in its accuracy with physicians showing greater mistrust (49.7% phy vs. 34.2% pts, p=0.001). Ethical and privacy concerns were notable, with more physicians than patients emphasizing ethical issues (38.7% vs. 22.1%, p=0.001) and significant privacy risks (50.8% vs. 42.0%, p=0.2). Both groups favored shared accountability for AI-driven outcomes (71.3% phy vs. 63.3% pts, p=0.2) and human oversight (78.4% phy vs. 66.9% pts, p=0.01), underscoring a need for cautious AI integration in urology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study reveals alignment between physicians and patients on AI’s potential in urology, alongside shared concerns about its reliability, ethics, and oversight.\u003c/p\u003e","manuscriptTitle":"Transforming Urologic Care: Physician and Patient Insights on AI Integration","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-06 10:04:52","doi":"10.21203/rs.3.rs-6280598/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"53c69747-ed15-478f-8311-eab61f4c25f2","owner":[],"postedDate":"May 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-06T10:04:55+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-06 10:04:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6280598","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6280598","identity":"rs-6280598","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}