Recursive Medical Identity: A Unified Framework for Entropy-Based Disease Modeling and AI Prognostics

This work introduces a revolutionary framework for redefining disease through a recursive, entropy-centered lens. Rejecting static taxonomies, this system models disease as a dynamic, phase-evolving identity regulated by entropy, nutritional influence, and immune response. Through mathematical formalism, symbolic modeling, simulation experiments, and AI-based classification, the Recursive Medical Identity Framework establishes a robust platform for real-time diagnostics and predictive medicine. Simulations of cancer, Alzheimer’s, Parkinson’s, and ALS validate the recursive entropy model, revealing critical tipping points and opportunities for therapeutic modulation. This self-contained system represents a foundational advancement in computational medicine.