Trustworthy AI for healthcare, examined in depth.

Healthcare Technology

Neurosymbolic Artificial Intelligence in Healthcare: From Foundations to Future Transformation

Artificial intelligence (AI) in healthcare stands at a critical juncture. Although deep learning has achieved remarkable pattern-recognition capabilities, its opacity and brittleness limit clinical adoption in high-stakes medical settings. Neurosymbolic AI, which integrates the perceptual power of neural networks with the logical reasoning of symbolic systems, offers a 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 in diagnostic decision support, oncology information extraction, and clinical trial recruitment; analyze key technical and regulatory challenges; and outline a strategic research agenda. The neurosymbolic approach promises systems that are simultaneously high-performing and clinically auditable, transparent and adaptive, and data-efficient and robust: qualities essential for the next generation of trustworthy medical AI.