Patients' and Physicians' Perceptions of AI Integration in Prostate Cancer Diagnosis: Mixed Methods Study of Challenges to the Patient-Physician Relationship.

Background: AI is increasingly integrated into prostate cancer diagnostics, with the potential to improve accuracy and efficiency. However, it also raises important questions about the conditions and barriers that may influence its successful implementation in this clinical context. Objective: This study aimed to examine how patients and physicians perceived the integration of AI in prostate cancer diagnostics, with particular attention to its impact on the clinical relationships and the roles of patients and physicians. Methods: A sequential explanatory mixed methods design was used. Quantitative data were collected using an online questionnaire administered to patients with localized prostate cancer (N=51). Descriptive analyses focused on perceived benefits, willingness to use AI, and associated concerns. Qualitative data were collected through focus groups and semistructured interviews with patients (n=16) and physicians (n=11). Data were analyzed using an iterative, inductive thematic analysis. Results: Quantitative findings showed that despite recognizing the potential benefits of AI, patients remained divided regarding the use of such tools in their own care. Qualitative findings suggested that this hesitation cannot be explained solely in terms of perceived performance or utility. Rather than simply reducing complexity in clinical decision-making, AI appeared to reconfigure the certainties on which trust within the patient-physician relationship was established. This reconfiguration was reflected across epistemic, ethical, and role-related dimensions. Patients emphasized difficulties in understanding AI-generated knowledge, whereas physicians focused on issues of reliability, validation, and clinical relevance. Ethical concerns centered on responsibility, which was consistently attributed to physicians, while errors made by AI were perceived as less acceptable than those made by physicians. Role-related uncertainties were reflected in ambivalent patient positions: while some participants sought more information to remain involved in decision-making, others preferred to rely on physicians, reflecting variation in how patients engaged with complex clinical information. AI was generally viewed as a supportive tool rather than a replacement for clinical judgment, while its integration was associated with evolving professional roles, including increased demands for interpretation, communication, and oversight. Conclusions: The integration of AI in prostate cancer diagnostics is shaped not only by its technical performance but also by how it interacts with trust within the patient-physician relationship. Our findings suggest that AI may reshape, rather than eliminate, uncertainty related to knowledge, responsibility, and social roles. Its integration into clinical practice therefore requires careful attention to clinician oversight, communication, and the relational context in which decisions are made.