• J. P. Pramod
• Gundla Harika
• K.Krushna Sri

The integration of artificial intelligence (AI) with fundamental physical principles offers a transformative approach to disease detection. Traditional data-driven AI models, while effective in pattern recognition, often face limitations such as poor generalizability, lack of interpretability, and reliance on large annotated datasets. Physics-based AI models, or physics-informed neural networks (PINNs), address these challenges by incorporating known biological and physical laws into the learning process. This synergy enhances model accuracy, robustness, and trustworthiness, especially in complex medical environments where data may be sparse or noisy. This paper explores the application of physics-based AI in various domains of disease detection, including medical imaging, physiological signal interpretation, and biomechanics. Results indicate improved diagnostic performance and explainability, positioning physics-informed AI as a powerful tool for next-generation medical diagnostics.

Physics-Informed AI, Disease Detection, Medical Imaging, Physiological Signal Analysis, Explainable AI (XAI), Computational Medicine, Hybrid Modeling, PINNs (Physics-Informed Neural Networks), Biomedical Engineering, Data-Efficient Learning.

"LEVERAGING PHYSICS-BASED AI MODELS FOR ENHANCED DISEASE DETECTION ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.e575-e588, May-2025, Available :http://www.jetir.org/papers/JETIR2505538.pdf

"LEVERAGING PHYSICS-BASED AI MODELS FOR ENHANCED DISEASE DETECTION ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppe575-e588, May-2025, Available at : http://www.jetir.org/papers/JETIR2505538.pdf