Why generic AI fails on local clinical reality
General-purpose models perform well on many tasks, but they often struggle inside a hospital's workflow. The main reason is usually not the model's general ability but how well it fits the hospital's language, codes and rules.
A common assumption in healthcare technology is that a sufficiently powerful general-purpose model, given good instructions, can handle clinical work almost anywhere. High scores on comparison tables seem to support this view. But these tables measure a model on average tasks, while the work in a hospital depends on its own conditions. Each hospital has its own language, its own coding system and its own regulations that decide what gets paid. Many clinical AI systems struggle with the gap between doing well on general tests and being useful in a specific institution.
To see why, it is enough to look at what a general model was trained on. General models learn from a very large body of internet text, most of it in English and most of it general content. The local material that governs clinical work, such as a country's reimbursement clauses, its national coding conventions and the way patients describe their symptoms in everyday language, is barely present in that data. So when such a model is asked whether a procedure is reimbursable under a specific national rule, it does what models usually do outside their knowledge: it gives a fluent, confident answer that may be wrong.
This concern is not only theoretical. In our internal evaluation, we compared a localized clinical model with leading general-purpose models on the same tasks, and the gap was widest on local questions. On reimbursement-rule decisions the localized model did clearly better than the general-purpose models. On understanding symptoms described in regional dialects, the gap pointed the same way. Inventing a regulatory clause or citing the wrong one is the most dangerous type of error, and the general-purpose models made it far more often than the localized model. Task-level evaluation results for MINA v3.7 are on the MINA v3.7 page.
Closing this gap is an engineering task with fairly clear steps. First, the model is taught the local clinical language through continued pre-training; instead of memorizing a few set phrases, it keeps training on texts in that language. Specialty knowledge is then added through modular components, so the model can work like a coder, a cardiologist or a reimbursement specialist as needed. Finally, its answers are grounded in retrieval, so it cites a real, current source instead of relying on its own memory. None of these steps is unusual, and none of them is specific to one country.
This is why the result is not limited to one market. The localized model does better because of a method that can be applied to any market's language, codes and rules, not because of a formula that works only in Türkiye. General ability is a starting point, but an institution needs local competence before it can trust the system and use its output in billing and daily practice.
These results come from an internal evaluation on synthetic, anonymized data; the comparison models were kept anonymous and all were run under identical conditions. They are a strong indication of where localized models pull ahead, but they are not a clinical guarantee and do not replace real-world validation. That validation is underway. The aim of the evaluation was to show that fit with local conditions can be measured and that a general-purpose model and a localized model do not give the same results.