Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/325473 
Erscheinungsjahr: 
2025
Schriftenreihe/Nr.: 
GLO Discussion Paper No. 1665
Verlag: 
Global Labor Organization (GLO), Essen
Zusammenfassung: 
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.
Schlagwörter: 
Generative AI
simulated patient
healthcare
quality and safety
health disparities
JEL: 
C0
I10
I11
C90
C93
Dokumentart: 
Working Paper

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