Purpose: To assess whether an artificial intelligence (AI)-based reassurance-call can inform patients about potential complications and provides reassurance following a prostate biopsy.

Materials And Methods: From October 2022 to December 2023, 42 patients aged 40 to 70 years undergoing their first prostate biopsy were recruited. The 'Reassurance-call' service was utilized to inform and monitor patients for complications. Participants were randomized into three groups: AI-assisted Reassurance-call service (Group 1), human-assisted Reassurance-call service (Group 2), and no call (Group 3). The primary outcome measured was patient satisfaction with the Reassurance-call service, assessed using a Likert scale. Secondary outcomes included satisfaction with complication management and anxiety levels, evaluated using the Likert scale, visual analog scale (VAS), and the state-trait anxiety inventory (STAI).

Results: Satisfaction with Reassurance-call averaged 4.20 (standard deviation [SD] 0.56) for Group 1 and 4.71 (SD 0.47) for Group 2, showing a statistically significant difference. Satisfaction regarding complication management using Likert scale was 4.13 (SD 0.52) for Group 1, 4.43 (SD 0.76) for Group 2, and 3.79 (SD 0.80) for Group 3 with no statistically significant differences. Satisfaction regarding complication management using VAS averaged 8.33 (SD 1.23) for Group 1, 8.57 (SD 1.45) for Group 2, and 7.07 (SD 1.86) for Group 3, indicating significant differences. Anxiety levels using STAI averaged 40.00 (SD 10.54) for Group 1, 39.14 (SD 8.29) for Group 2, and 35.00 (SD 9.46) for Group 3, showing no significant differences. Anxiety levels using VAS averaged 5.07 (SD 2.79) for Group 1, 2.21 (SD 2.64) for Group 2, and 3.50 (SD 2.28) for Group 3, with significant differences observed.

Conclusions: AI demonstrated potential in enhancing patient reassurance and managing complications post-prostate biopsy, although human interaction proved superior in certain aspects. Further studies with larger cohorts are necessary to verify the effectiveness of AI-based tools.

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Source
http://dx.doi.org/10.5534/wjmh.240191DOI Listing

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