Blog by Qhala: “…In AI, benchmarks are the gold standard for evaluation. They are used to test whether large language models (LLMs) can reason, diagnose, and communicate effectively. In healthcare, LLMs are tested against benchmarks before they’re considered “safe” for clinical use.
But here’s the problem: These benchmarks are primarily built for Western settings. They reflect English-language health systems, Western disease burdens, and datasets scraped from journals and exams thousands of kilometres away from the real-world clinics of Kisumu, Kano, or Kigali.
A study in Kenya found over 90 different clinical guidelines used by frontline health workers in primary care. That’s not chaos, it’s context. Medicine in Africa is deeply localised, shaped by resource availability, epidemiology, and culture. When a mother arrives with a feverish child, a community nurse doesn’t consult the United States Medical Licensing Examination (USMLE). She consults the local Ministry of Health protocol and speaks in Luo, Hausa, or Amharic.
In practice, Human medical Doctors have to go through various levels of rigorous, context-based, localised assessment before they can practise in a country and in a specific specialisation. These licensing exams aren’t arbitrary; they’re tailored to national priorities, clinical practices, and patient populations. They acknowledge that even great doctors must be assessed in context. These assessments are mandatory and are an obvious logic when it comes to human clinicians. A Kenyan-trained doctor must pass the United States Medical Licensing Examination (USMLE). In the United Kingdom, it is the Professional and Linguistic Assessments Board (PLAB) test. In Australia, the relevant assessment is the Australian Medical Council (AMC) examination.
However, unlike the nationally ratified assessments for humans, the LLM benchmarks and subsequently the LLMs and Health AI tools are not created for local realities, nor are they reflective of the local context.
…Amidst the limitations of global benchmarks, a wave of important African-led innovations is starting to reshape the landscape. Projects like AfriMedQA represent some of the first structured attempts to evaluate large language models (LLMs) using African health contexts. These benchmarks thoughtfully align with the continent’s disease burden, such as malaria, HIV, and maternal health. Crucially, they also attempt to account for cultural nuances that are often overlooked in Western-designed benchmarks.
But even these fall short. They remain Anglophone…(More)”.
