Quality and Accountability of Large Language Models (LLMs) in Healthcare in Low- and Middle-Income Countries (LMIC): A Simulated Patient Study using ChatGPT - Yafei Si; Yuyi Yang; Xi Wang; Ruopeng An; Jiaqi Zu; Xi Chen; Xiaojing Fan; Sen Gong (Global Labor Organization (GLO), 2024)
Using simulated patients to mimic nine established non-communicable and infectious diseases over 27 trials, we assess ChatGPT's effectiveness and reliability in diagnosing and treating common diseases in low- and middle-income countries. We find ChatGPT's performance varied within a single disease, despite a high level of accuracy in both correct diagnosis (74.1%) and medication prescription (84.5%). Additionally, ChatGPT recommended a concerning level of unnecessary or harmful medications (85.2%) even with correct diagnoses. Finally, ChatGPT performed better in managing non-communicable diseases compared to infectious ones. These results highlight the need for cautious AI integration in healthcare systems to ensure quality and safety.
Keywords: C0; I10; I11; C90; 330; ChatGPT; Large Language Models; Generative AI; Simulated Patient; Healthcare; Quality; Safety; Low- and Middle-Income Countries