The small numbers of medicine and the big numbers of artificial intelligence: a curated-data approach to appropriateness in laboratory medicine and coagulation

Abstract

Inappropriateness in medical decision-making remains a persistent challenge, particularly in laboratory medicine. This phenomenon is largely driven by cognitive inadequacy, defined as the growing mismatch between the exponential expansion of medical knowledge and the limited capacity of human cognition to assimilate and apply it in real time. Artificial intelligence (AI), especially in its narrow form, designed and trained to perform specialized tasks, offers practical tools to mitigate this gap. This article examines the potential role of AI in improving prescription appropriateness in laboratory medicine, with a focus on coagulation diagnostics, and proposes a novel AI-based system that supports physicians at the point of decision-making through the use of curated, expert-annotated data.

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