Patients' Trust in Artificial Intelligence-based Decision-making for Localized Prostate Cancer: Results from a Prospective Trial

Severin Rodler; Rega Kopliku; Daniel Ulrich; Annika Kaltenhauser; Jozefina Casuscelli; Lennert Eismann; Raphaela Waidelich; Alexander Buchner; Andreas Butz; Giovanni E Cacciamani; Christian G Stief; Thilo Westhofen

doi:10.1016/j.euf.2023.10.020

Patients' Trust in Artificial Intelligence-based Decision-making for Localized Prostate Cancer: Results from a Prospective Trial

Eur Urol Focus. 2023 Nov 1:S2405-4569(23)00237-7. doi: 10.1016/j.euf.2023.10.020. Online ahead of print.

Affiliations

¹ Department of Urology, LMU University Hospital, Munich, Germany; USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. Electronic address: severin.rodler@med.uni-muenchen.de.
² Department of Urology, LMU University Hospital, Munich, Germany.
³ Department of Informatics, Ludwig-Maximilian-Universität München, Munich, Germany.
⁴ USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.

PMID: 37923632
DOI: 10.1016/j.euf.2023.10.020

Abstract

Background: Artificial intelligence (AI) has the potential to enhance diagnostic accuracy and improve treatment outcomes. However, AI integration into clinical workflows and patient perspectives remain unclear.

Objective: To determine patients' trust in AI and their perception of urologists relying on AI, and future diagnostic and therapeutic AI applications for patients.

Design, setting, and participants: A prospective trial was conducted involving patients who received diagnostic or therapeutic interventions for prostate cancer (PC).

Intervention: Patients were asked to complete a survey before magnetic resonance imaging, prostate biopsy, or radical prostatectomy.

Outcome measurements and statistical analysis: The primary outcome was patient trust in AI. Secondary outcomes were the choice of AI in treatment settings and traits attributed to AI and urologists.

Results and limitations: Data for 466 patients were analyzed. The cumulative affinity for technology was positively correlated with trust in AI (correlation coefficient 0.094; p = 0.04), whereas patient age, level of education, and subjective perception of illness were not (p > 0.05). The mean score (± standard deviation) for trust in capability was higher for physicians than for AI for responding in an individualized way when communicating a diagnosis (4.51 ± 0.76 vs 3.38 ± 1.07; mean difference [MD] 1.130, 95% confidence interval [CI] 1.010-1.250; t₉₂₄ = 18.52, p < 0.001; Cohen's d = 1.040) and for explaining information in an understandable way (4.57 ± vs 3.18 ± 1.09; MD 1.392, 95% CI 1.275-1.509; t₉₂₁ = 27.27, p < 0.001; Cohen's d = 1.216). Patients stated that they had higher trust in a diagnosis made by AI controlled by a physician versus AI not controlled by a physician (4.31 ± 0.88 vs 1.75 ± 0.93; MD 2.561, 95% CI 2.444-2.678; t₉₂₅ = 42.89, p < 0.001; Cohen's d = 2.818). AI-assisted physicians (66.74%) were preferred over physicians alone (29.61%), physicians controlled by AI (2.36%), and AI alone (0.64%) for treatment in the current clinical scenario.

Conclusions: Trust in future diagnostic and therapeutic AI-based treatment relies on optimal integration with urologists as the human-machine interface to leverage human and AI capabilities.

Patient summary: Artificial intelligence (AI) will play a role in diagnostic decisions in prostate cancer in the future. At present, patients prefer AI-assisted urologists over urologists alone, AI alone, and AI-controlled urologists. Specific traits of AI and urologists could be used to optimize diagnosis and treatment for patients with prostate cancer.

Keywords: Artificial intelligence; Human-machine interaction; Prostate cancer; Real-world application; Trust.