The integration of Artificial Intelligence (AI) into medicine promises to transform diagnosis and treatment. However, a significant portion of these tools fail to achieve sustained clinical adoption, not because of technical shortcomings, but due to a fundamental epistemological mismatch with medical practice. This article proposes that effective AI design should emulate clinical reasoning —a pragmatic, hierarchical, and optimized process for managing uncertainty with limited resources. We propose a three-phase conceptual framework—Screening, Diagnosis, and Management—that translates clinician logic into design principles for AI systems. It is argued that success lies not in the pursuit of maximum algorithmic accuracy, but in the creation of interpretable tools that integrate seamlessly into the workflow, augment the clinician’s cognitive capabilities, and, crucially, are validated with data representative of the local epidemiological and socioeconomic context. This paper aims to serve as a bridge between AI engineering and clinical epistemology, advocating a co-development paradigm for creating beneficial and safe technology.