Neuro-Symbolic AI in Healthcare

Abstract

Medical AI has achieved strong predictive performance, yet most systems remain limited by shallow reasoning, poor transparency, and weak generalisation in safety-critical settings. Neurosymbolic AI offers a path beyond these constraints by combining neural models' ability to learn from complex clinical data with the explicit structure, logic, and domain knowledge of symbolic methods. This article examines how neurosymbolic approaches can address core challenges in healthcare AI through five key areas: hybrid reasoning that unifies learning and logic; symbol grounding that links internal representations to clinically meaningful concepts; clinical interpretability that exposes reasoning steps; human-integrated decision-making that keeps clinicians in control; and knowledge-driven diagnosis that incorporates guidelines, ontologies, and causal understanding. Together, these elements outline how neurosymbolic AI can support systems that are not only accurate but also transparent, clinically aligned, and robust in complex or data-sparse scenarios. Advancing this paradigm will require collaboration across AI research, clinical practice, and knowledge engineering, as well as governance mechanisms that ensure fairness and accountability. Neurosymbolic AI thus represents a promising direction for building trustworthy, knowledge-rich intelligence in healthcare.

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