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

Yafei Si (), Yurun Meng, Xi Chen, Ruopeng An (), Limin Mao, Bingqin Li, Hazel Bateman, Han Zhang, Hongbin Fan, Jiaqi Zu, Shaoqing Gong, Zhongliang Zhou () and Yudong Miao
Additional contact information
Yafei Si: University of Melbourne
Yurun Meng: Xi’an Jiaotong University
Ruopeng An: Washington University, St. Louis
Limin Mao: University of New South Wales
Bingqin Li: University of New South Wales
Hazel Bateman: University of New South Wales
Han Zhang: Xi’an Jiaotong University
Hongbin Fan: Xi’an Jiaotong University
Jiaqi Zu: Duke Kunshan University
Zhongliang Zhou: Xi’an Jiaotong University

No 18074, IZA Discussion Papers from Institute of Labor Economics (IZA)

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.

JEL-codes: C0 C90 C93 I10 I11 (search for similar items in EconPapers)
Date: 2025-08
New Economics Papers: this item is included in nep-exp and nep-hea
References: Add references at CitEc
Citations:

Downloads: (external link)
https://docs.iza.org/dp18074.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:iza:izadps:dp18074

Ordering information: This working paper can be ordered from
IZA, Margard Ody, P.O. Box 7240, D-53072 Bonn, Germany

Access Statistics for this paper

More papers in IZA Discussion Papers from Institute of Labor Economics (IZA) IZA, P.O. Box 7240, D-53072 Bonn, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Holger Hinte ().