Artificial intelligence (AI) is having a transformational impact on society, yet its adoption in laboratory medicine has proceeded notably slower than in many other industries and even different specialities within medicine. This review sets out to examine why, despite such technical progress, meaningful clinical translation beyond rule-based autoverification has remained elusive. We argue that three principal barriers account for this gap. First, modelling approaches have been insufficiently robust for the inherent complexity of laboratory data.
