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Quality, Safety, and Disparities of AI Chatbots in Managing Chronic Diseases: Experimental Evidence

Author

Listed:
  • Si, Yafei

    (University of Melbourne)

  • Meng, Yurun

    (Xi’an Jiaotong University)

  • Chen, Xi

    (Yale University)

  • An, Ruopeng

    (Washington University, St. Louis)

  • Mao, Limin

    (University of New South Wales)

  • Li, Bingqin

    (University of New South Wales)

  • Bateman, Hazel

    (University of New South Wales)

  • Zhang, Han

    (Xi’an Jiaotong University)

  • Fan, Hongbin

    (Xi’an Jiaotong University)

  • Zu, Jiaqi

    (Duke Kunshan University)

  • Gong, Shaoqing
  • Zhou, Zhongliang

    (Xi’an Jiaotong University)

  • Miao, Yudong

Abstract

The rapid development of AI solutions reveals opportunities to address the underdiagnosis and poor management of chronic conditions in developing settings. Using the method of simulated patients and experimental designs, we evaluate the quality, safety, and disparity of medical consultation with ERNIE Bot in China among 384 patient-AI trials. ERNIE Bot reached a diagnostic accuracy of 77.3%, correct drug prescriptions of 94.3%, but prescribed high rates of unnecessary medical tests (91.9%) and unnecessary medications (57.8%). Disparities were observed based on patient age and household economic status, with older and wealthier patients receiving more intensive care. Under standardized conditions, ERNIE Bot, ChatGPT, and DeepSeek demonstrated higher diagnostic accuracy but a greater tendency toward overprescription than human physicians. The results suggest the great potential of ERNIE Bot in empowering quality, accessibility, and affordability of healthcare provision in developing contexts but also highlight critical risks related to safety and amplification of sociodemographic disparities.

Suggested Citation

  • Si, Yafei & Meng, Yurun & Chen, Xi & An, Ruopeng & Mao, Limin & Li, Bingqin & Bateman, Hazel & Zhang, Han & Fan, Hongbin & Zu, Jiaqi & Gong, Shaoqing & Zhou, Zhongliang & Miao, Yudong, 2025. "Quality, Safety, and Disparities of AI Chatbots in Managing Chronic Diseases: Experimental Evidence," IZA Discussion Papers 18074, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp18074
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    References listed on IDEAS

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    1. Momtazmanesh, Sara & Moghaddam, Sahar Saeedi & Ghamari, Seyyed-Hadi & Rad, Elaheh Malakan & Rezaei, Negar & Shobeiri, Parnian & Aali, Amirali & Abbasi-Kangevari, Mohsen & Abbasi-Kangevari, Zeinab & Ab, 2023. "Global burden of chronic respiratory diseases and risk factors, 1990–2019: an update from the Global Burden of Disease Study 2019," Open Access Publications from Kiel Institute for the World Economy 273507, Kiel Institute for the World Economy (IfW Kiel).
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      JEL classification:

      • C0 - Mathematical and Quantitative Methods - - General
      • I10 - Health, Education, and Welfare - - Health - - - General
      • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets
      • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
      • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments

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