Artificial Intelligence Applications in Urology: Reporting Standards to Achieve Fluency for Urologists

Area of Science:

• Urology
• Medical Artificial Intelligence
• Machine Learning

Background:

• Artificial intelligence (AI) adoption is increasing in urology, yielding significant advancements.
• A critical need exists to establish AI literacy within the healthcare workforce.
• Current research reporting in urology AI lacks standardization, impacting knowledge synthesis.

Purpose of the Study:

• To conduct a narrative review of AI and machine learning literature in urology.
• To propose a checklist of reporting standards for AI in urology research.
• To evaluate the current state of reporting and improve research generalizability.

Main Methods:

• A comprehensive narrative review of existing literature on artificial intelligence and machine learning in urology.
• Development of a proposed checklist for reporting standards in urology AI research.
• Analysis of reporting heterogeneity in the reviewed studies.

Main Results:

• The literature review revealed significant heterogeneity in the reporting of methodologies and outcomes.
• Inconsistent reporting practices limit the generalizability and reproducibility of AI research findings in urology.
• Current standards for reporting AI in urology are insufficient for robust evaluation.

Conclusions:

• Standardizing reporting in urology AI research is crucial for advancing the field.
• The proposed checklist aims to enhance the readability and evaluability of medical AI research.
• This review provides a foundation for future assessments of AI in urology literature.