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, Yudong Miao, Xiaojing Fan and Gang Chen

No 1665, GLO Discussion Paper Series from Global Labor Organization (GLO)

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.

Keywords: Generative AI; simulated patient; healthcare; quality and safety; health disparities (search for similar items in EconPapers)
JEL-codes: C0 C90 C93 I10 I11 (search for similar items in EconPapers)
Date: 2025
New Economics Papers: this item is included in nep-cna and nep-exp
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:glodps:1665

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