Abstract :
Mechanistic inference and data-driven discovery are complementary strengths that come with converging forces of artificial intelligence and computational physiology in the quantitative study of human systems. Predictive modeling and simulation, through a combination of principled, biophysically informed models with machine-learning pipelines, allows high-fidelity reconstruction of physiological dynamics at molecular, cellular, organ and systemic scales; this convergence enables mechanistic hypothesis testing, virtual cohort experiments and accelerated parameter estimation challenging to each individually. Used in modern health care systems, such hybrid systems improve diagnostic sensitivity based on learned biomarkers, make it possible to plan therapy individually based on patient-specific virtual physiological models, and form the basis of large-scale monitoring and early warning by combining continuous sensor streams with electronic health records. The opportunities are high: better precision medicine, accelerated translational research by in-silico trials, more efficient allocation of resources, and more economical care delivery. However, significant issues remain, including data reliability (heterogeneity, missingness, and bias), important ethical issues (privacy, informed consent, fairness, and accountability) and low interpretability and provenance of AI-based predictions that impede clinical trust and regulatory acceptance. To achieve the full potential of this interdisciplinary paradigm, a tight standard of validation, clear reporting, data infrastructures that are interoperable and governance structures that reflect, closely align technological innovation with clinical, legal, and societal anticipations will be required.
Keyword :
Artificial Intelligence (AI), Computational Physiology, Predictive Modeling, Digital Twins, Precision Medicine