A theoretical framework for the application of artificial intelligence in medical diagnosis

The evolution of artificial intelligence from a specialized computational tool to a foundational pillar of diagnostic medicine has necessitated the development of a unified theoretical framework to govern its integration, evaluation, and evolution. As of ۲۰۲۵, clinical adoption of AI has nearly doubled, with ۴۸% of clinicians incorporating AI tools into their workflows to mitigate the pressures of rising patient volumes and diagnostic complexity. However, this rapid implementation has outpaced the development of rigorous conceptual models, leading to a fragmented landscape of task-specific "point solutions" and significant trust gaps. This research proposes a comprehensive theoretical framework that synthesizes the "Medicine for Artificial Intelligence" (MAI) paradigm with the "Model-Context- Relation" (M-C-R) framework. By reconceptualizing AI anomalies as clinical conditions requiring systematic diagnosis and "treatment," and by framing AI as a digital semiotic extension of clinical reasoning, this framework provides a roadmap for sustainable integration. The report further details the architectural shift from isolated applications to modular, agentic systems supported by federated learning and digital twin technologies. Critical analysis of explainable AI (XAI), the "deskilling" of the medical workforce, and the ethical implications of algorithmic bias highlights the necessity of a "human-on-the-loop" oversight model. This study contributes a novel synthesis of the sociotechnical, epistemic, and regulatory dimensions essential for the next generation of AI-enabled diagnostic systems.

کلیدواژه ها:

Artificial Intelligence ، Medical Diagnosis ، Theoretical Framework ، Medicine for Artificial Intelligence (MAI) ، Explainable AI (XAI) ، Federated Learning