TY  - RPRT
        AU  - Si, Yafei
AU  - Yang, Yuyi
AU  - Wang, Xi
AU  - An, Ruopeng
AU  - Zu, Jiaqi
AU  - Chen, Xi
AU  - Fan, Xiaojing
AU  - Gong, Sen
        TI  - Quality and Accountability of Large Language Models (LLMs) in Healthcare in Low- And Middle-Income Countries (LMIC): A Simulated Patient Study Using ChatGPT
        PY  - 2024/Aug/
        PB  - Institute of Labor Economics (IZA)
        CY  - Bonn
        T2  - IZA Discussion Paper
        IS  - 17204
        UR  - https://www.iza.org/index.php/publications/dp17204
        AB  - 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.
        KW  - safety
KW  - quality
KW  - ChatGPT
KW  - Large Language Models
KW  - generative AI
KW  - simulated patient
KW  - healthcare
KW  - low- and middle-income countries
        ER  -