Healthcare

Challenges, Opportunities,
and the Case for Hybrid AI

Healthcare organisations worldwide are accelerating digital transformation to improve efficiency, reduce clinician burden, and enhance patient outcomes. Language understanding AI and generative AI tools, such as ChatGPT and Copilot, have demonstrated promise in processing unstructured text, automating administrative tasks, and engaging patients. However, their adoption in healthcare is constrained by unique challenges: strict regulation, data fragmentation, the complexity of medical language, and the need for explainability.

Hybrid AI—an approach that combines symbolic reasoning (ontologies, knowledge graphs, rules) with machine learning and generative models—emerges as the more intelligent choice for healthcare. It provides the explainability, compliance, and domain accuracy essential for clinical environments, while allowing large language models (LLMs) to complement use cases in patient engagement, summarisation, and research exploration.

Why Hybrid AI is the More Intelligent Choice

Hybrid AI addresses these challenges by combining symbolic AI (rules, knowledge graphs, and ontologies such as SNOMED CT, UMLS, and ICD-10) with machine learning and generative models.

Explainability and Compliance: Symbolic reasoning provides transparent rules and auditability, essential for clinical and regulatory oversight.

Domain Accuracy: Ontologies and taxonomies ensure precise interpretation of medical language, reducing ambiguity and error.

Risk Reduction: Symbolic constraints mitigate hallucinations, guiding generative models to stay within clinically validated boundaries.

Interoperability: Knowledge graphs bridge structured standards with unstructured records, facilitating EHR integration.

Trust and Adoption: Clinicians gain confidence in AI systems that explain their outputs and align with professional standards.

Proven Use Cases of Hybrid AI in Healthcare

In Europe and the US, Hybrid AI is already demonstrating value across a range of clinical and operational use cases:

Compliance in Decision Support: Checking treatment plans against clinical guidelines and safety protocols.

Adverse Event Monitoring: Detecting potential drug interactions and safety issues from patient records.

Medical Coding Automation: Mapping narrative reports to ICD, CPT, or DRG codes with high accuracy.

Clinical Research and Trial Matching: Identifying patient cohorts for clinical studies.

Intelligent Triage and Routing: Prioritising patient cases and referrals for specialist care.

Population Health Management: Analysing records for risk stratification and early intervention strategies.

Conclusion and Policy Implications

Healthcare faces unique barriers to AI adoption due to the regulatory environment, semantic complexity, and high stakes of clinical decision-making. Purely generative approaches, while promising, cannot meet the demands of compliance, explainability, and accuracy required in this domain.

A hybrid AI strategy provides the intelligent balance: symbolic AI ensures safety, governance, and trustworthiness, while LLMs bring flexibility, engagement, and innovation. Together, they enable healthcare organisations to modernise safely and effectively, improving outcomes for both patients and providers.

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