Language-dependent performance of large language models in medical diagnostics: A comparative study of ChatGPT-4o and Claude 3.5 Sonnet

Conclusions: No significant differences in diagnostic accuracy were found between the LLMs; rather, the discrepancies were primarily related to the languages used. In multilingual healthcare settings, such as those involving Arabic, various underlying factors-particularly language complexity, cultural context, and the type of clinical cases-affect their performance. Further research is essential to improve the capabilities of LLMs, ensuring equitable healthcare access for diverse linguistically representative populations.