Bias in respiratory diagnoses by Large Language Models (LLMs) in Low Middle Income Countries (LMICs)

Use of Large Language Models (LLMs) created in North America and Europe, could lead to a Western bias if used in Low and Middle Income Countries (LMIC) healthcare settings. Clinicians and patients are likely to increasingly use LLMs for diagnostic support. <bold>Aims:</bold> To explore if diagnostic suggestions made by LLMs are relevant in LMIC healthcare settings. <bold>Methods:</bold> Five short respiratory clinical vignettes were produced. For each vignette, a doctor from one of 4 countries (Ghana, India, Jordan and Brazil) independently gave the 4 most likely diagnoses. 4 LLMs (ChatGPT, Claude Sonnet, Google Gemini and Microsoft Copilot) were prompted with the same vignettes. The top 4 diagnoses for each case was requested. A Virtual Private Network was used so each LLM was accessed from each of the 4 countries. In a second experiment, the LLM was informed of the country in which the case was being seen. LLM output was compared with the doctors’ responses. <bold>Results:</bold> LLMs consistently offered diagnoses irrelevant in the LMIC setting, with little overlap between LLM and doctor diagnoses. Doctor diagnoses were different from LLM diagnoses (see fig). <fig><object-id>erj;66/suppl_69/OA5379/F1</object-id><object-id>F1</object-id><object-id>F1</object-id><graphic></graphic></fig> <bold>Conclusions:</bold> LLMs should not be used as diagnostic aids in LMICs. LLMs are misaligned with the goal of an internationally useful diagnostic aid.