LLMs for clinical risk prediction
This work addresses the problem of clinical risk prediction for healthcare providers, highlighting that LLMs like GPT-4 are currently unsuitable for independent decision-making, making it an incremental study that reinforces known limitations.
This study compared GPT-4 and clinalytix Medical AI for predicting delirium risk, finding that GPT-4 had significant deficiencies in identifying positive cases and providing reliable probability estimates, while clinalytix Medical AI showed superior accuracy.
This study compares the efficacy of GPT-4 and clinalytix Medical AI in predicting the clinical risk of delirium development. Findings indicate that GPT-4 exhibited significant deficiencies in identifying positive cases and struggled to provide reliable probability estimates for delirium risk, while clinalytix Medical AI demonstrated superior accuracy. A thorough analysis of the large language model's (LLM) outputs elucidated potential causes for these discrepancies, consistent with limitations reported in extant literature. These results underscore the challenges LLMs face in accurately diagnosing conditions and interpreting complex clinical data. While LLMs hold substantial potential in healthcare, they are currently unsuitable for independent clinical decision-making. Instead, they should be employed in assistive roles, complementing clinical expertise. Continued human oversight remains essential to ensure optimal outcomes for both patients and healthcare providers.