Evaluating the Reliability of ChatGPT for Health-Related Questions: A Systematic Review

Abstract

The rapid advancement of large language models like ChatGPT has significantly impacted natural language processing, expanding its applications across various fields, including healthcare. However, there remains a significant gap in understanding the consistency and reliability of ChatGPT’s performance across different medical domains. We conducted this systematic review according to an LLM-assisted PRISMA setup. The high-recall search term “ChatGPT” yielded 1101 articles from 2023 onwards. Through a dual-phase screening process, initially automated via ChatGPT and subsequently manually by human reviewers, 128 studies were included. The studies covered a range of medical specialties, focusing on diagnosis, disease management, and patient education. The assessment metrics varied, but most studies compared ChatGPT’s accuracy against evaluations by clinicians or reliable references. In several areas, ChatGPT demonstrated high accuracy, underscoring its effectiveness. However, performance varied, and some contexts revealed lower accuracy. The mixed outcomes across different medical domains emphasize the challenges and opportunities of integrating AI like ChatGPT into healthcare. The high accuracy in certain areas suggests that ChatGPT has substantial utility, yet the inconsistent performance across all applications indicates a need for ongoing evaluation and refinement. This review highlights ChatGPT’s potential to improve healthcare delivery alongside the necessity for continued research to ensure its reliability.

Ähnliche Arbeiten