%0 Report
        %A Si, Yafei
%A Yang, Yuyi
%A Wang, Xi
%A An, Ruopeng
%A Zu, Jiaqi
%A Chen, Xi
%A Fan, Xiaojing
%A Gong, Sen
        %T Quality and Accountability of Large Language Models (LLMs) in Healthcare in Low- And Middle-Income Countries (LMIC): A Simulated Patient Study Using ChatGPT
        %D 2024
        %8 2024 Aug
        %I Institute of Labor Economics (IZA)
        %C Bonn
        %7 IZA Discussion Paper
        %N 17204
        %U https://www.iza.org/publications/dp17204
        %X 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.
        %K safety
%K quality
%K ChatGPT
%K Large Language Models
%K generative AI
%K simulated patient
%K healthcare
%K low- and middle-income countries