Journal Article

The future of AI clinicians: assessing the modern standard of chatbots and their approach to diagnostic uncertainty

Huang, Ryan S., Kemppainen, Joel, Leung, Fok-Han, Benour, Ali

2024

Overview

Background Artificial intelligence (AI) chatbots have demonstrated proficiency in structured knowledge assessments; however, there is limited research on their performance in scenarios involving diagnostic uncertainty, which requires careful interpretation and complex decision-making. This study aims to evaluate the efficacy of AI chatbots, GPT-4o and Claude-3, in addressing medical scenarios characterized by diagnostic uncertainty relative to Family Medicine residents. Methods Questions with diagnostic uncertainty were extracted from the Progress Tests administered by the Department of Family and Community Medicine at the University of Toronto between 2022 and 2023. Diagnostic uncertainty questions were defined as those presenting clinical scenarios where symptoms, clinical findings, and patient histories do not converge on a definitive diagnosis, necessitating nuanced diagnostic reasoning and differential diagnosis. These questions were administered to a cohort of 320 Family Medicine residents in their first (PGY-1) and second (PGY-2) postgraduate years and inputted into GPT-4o and Claude-3. Errors were categorized into statistical, information, and logical errors. Statistical analyses were conducted using a binomial generalized estimating equation model, paired t-tests, and chi-squared tests. Results Compared to the residents, both chatbots scored lower on diagnostic uncertainty questions ( p  < 0.01). PGY-1 residents achieved a correctness rate of 61.1% (95% CI: 58.4–63.7), and PGY-2 residents achieved 63.3% (95% CI: 60.7–66.1). In contrast, Claude-3 correctly answered 57.7% ( n  = 52/90) of questions, and GPT-4o correctly answered 53.3% ( n  = 48/90). Claude-3 had a longer mean response time (24.0 s, 95% CI: 21.0-32.5 vs. 12.4 s, 95% CI: 9.3–15.3; p  < 0.01) and produced longer answers (2001 characters, 95% CI: 1845–2212 vs. 1596 characters, 95% CI: 1395–1705; p  < 0.01) compared to GPT-4o. Most errors by GPT-4o were logical errors (62.5%). Conclusions While AI chatbots like GPT-4o and Claude-3 demonstrate potential in handling structured medical knowledge, their performance in scenarios involving diagnostic uncertainty remains suboptimal compared to human residents.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Artificial Intelligence / Artificial intelligence in clinical reasoning education / Chat rooms / Chatbots / Clinical Competence / Decision making / Diagnostic uncertainty / Education / Educational Measurement - methods / Ethics / Family medicine / Family Practice - education / Feedback (Response) / Health aspects / Humans / Internship and Residency / Lifelong Learning / Medical diagnosis / Medical Education / Medical Evaluation / Medical practices / Medicine / Multiple Choice Tests / Patient assessment / Patient Education / Reaction Time / Response time / Statistical Analysis / Statistical Significance / Technology application / Theory of Medicine/Bioethics / Uncertainty