JMIR Publications

ABSTRACT

Background:

Artificial intelligence (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 examined how patients and healthcare professionals perceive the integration of AI in prostate cancer diagnostics, with particular attention to its impact on clinical relationships and the roles of patients and physicians.

Methods:

A sequential explanatory mixed-methods design was used. Quantitative data were collected through 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 semi-structured interviews with patients (n=16) and physicians (n=11). Data were analyzed using 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 suggest 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 is established. This reconfiguration was reflected across epistemic, ethical, and role-related dimensions. Patients emphasized difficulties in understanding AI-generated knowledge, whereas clinicians 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 clinicians. 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 engage with complex clinical information. AI was generally viewed as a supportive tool rather than a replacement for clinical judgement, 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 by how it reconfigures trust within the patient–physician relationship. Rather than eliminating uncertainty, AI redistributes it across knowledge, responsibility, and social roles. Ensuring that AI contributes positively to clinical practice therefore requires careful attention to clinician oversight, communication, and the relational context in which decisions are made. Clinical Trial: NCT07074405 (ClinicalTrials.gov)