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Neurosymbolic Artificial Intelligence in Healthcare: From Foundations to Future Transformation

Healthcare artificial intelligence stands at a critical moment. While deep learning has achieved remarkable pattern recognition capabilities, its opacity and brittleness limit clinical adoption in high-stakes medical environments. Neurosymbolic AI, integrating neural networks' perceptual power with symbolic systems' logical reasoning, offers a transformative synthesis that addresses these fundamental limitations. This article traces the evolution of medical AI from early expert systems through the deep learning revolution to the emerging neurosymbolic paradigm. We examine core architectural principles, survey clinical applications across imaging, decision support, and drug discovery, analyze key technical and regulatory challenges, and chart a strategic research agenda. The neurosymbolic approach promises systems that are simultaneously high-performing and clinically auditable, transparent and adaptive, data-efficient and robust: qualities essential for the next generation of trustworthy medical AI.