Artificial Intelligence in Medicine and Healthcare: A Complexity-Based Framework for Model–Context–Relation Alignment

Abstract

Artificial intelligence (AI) is profoundly transforming medicine and healthcare, evolving from analytical tools aimed at automating specific tasks to integrated components of complex socio-technical systems. This work presents a conceptual and theoretical review proposing the Model–Context–Relation (M–C–R) framework to interpret how the effectiveness of Artificial Intelligence (AI) in medicine and healthcare emerges from the dynamic alignment among algorithmic, contextual, and relational dimensions. No new patient-level data were generated or analyzed. Through a qualitative conceptual framework analysis, the study integrates theoretical, regulatory, and applicative perspectives, drawing on the Revision of the Semiological Paradigm developed by the Palermo School, as well as on major international guidelines (WHO, European AI Act, FDA). The results indicate that AI-supported processes have been reported in the literature to improve clinical accuracy and workflow efficiency when appropriately integrated, yet its value depends on contextual adaptation and human supervision rather than on algorithmic performance alone. When properly integrated, AI functions as a digital semiotic extension of clinical reasoning and may enhance the physician’s interpretative capacity without replacing it. The M–C–R framework enables understanding of how performance, ethical reliability, and organizational sustainability emerge from the alignment between the technical model, the context of use, and relational trust. In this perspective, AI is conceptualized not as a decision-maker but as an adaptive cognitive partner, fostering a reflective, transparent, and person-centered medicine. The proposed approach supports the design of sustainable and ethically responsible AI systems within a Medicine of Complexity, in which human and artificial intelligence co-evolve to strengthen knowledge, accountability, and equity in healthcare systems.

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