- Neurosymbolic AI strengthens clinical AI by combining neural networks with clinical terminology knowledge graphs
- Clinical terminology provides the structure needed to validate AI outputs and reduce hallucinations
- This approach improves trust, accuracy, and explainability in clinical AI applications
The path to more reliable clinical AI
As the digital healthcare landscape rapidly evolves, we find ourselves at a crossroads. Large language models (LLMs) and deep learning have equipped us with almost a magical ability to parse unstructured clinical notes and identify patterns that were not obvious to the human eye before. At the same time, these “black-box” models introduce real risks in clinical artificial intelligence (AI) – including hallucinations – and most users do not understand how these insights are generated.
Healthcare requires making high-stakes decisions about patient lives, and we in the healthcare technology community need to build trustworthy applications. One promising solution for advancing clinical AI is neurosymbolic artificial intelligence – a paradigm that combines the statistical power of neural networks with the “ground truth” of healthcare knowledge graphs.
Bringing reasoning into AI with neurosymbolic systems
Neurosymbolic AI draws on “dual-process models of human cognition,” which combine intuitive perception (system 1) and deliberative reasoning (system 2) to approximate “human-like decision making.” Neural networks acting as system 1 can ingest thousands of notes and flag visit diagnoses, billable codes, and disease risks because they excel at pattern recognition. However, they lack a fundamental understanding of clinical terminology and medical ontology.
This is where IMO Health’s knowledge graph comes in – system 2 – the symbolic component of neurosymbolic AI. IMO Health has expertly curated medical concepts and mapped relationships across standard terminologies like SNOMED-CT® and ICD-10-CM, creating a knowledge graph that serves as the symbolic anchor for clinical AI outputs.
IMO Health has expertly curated medical concepts and mapped relationships across standard terminologies like SNOMED-CT® and ICD-10-CM, creating a knowledge graph that serves as the symbolic anchor for clinical AI outputs.
Why knowledge graphs matter in healthcare
A knowledge graph is more than just a dictionary. It is a structured map of clinical reality. When a model based on neural networks, like an LLM, predicts a relationship, the symbolic layer can verify it against a knowledge graph.
For clinical AI, this means predictions are grounded in established clinical terminology rather than statistical likelihood alone. For example, if a model suggests a diagnosis or a course of treatment, the symbolic layer can verify it using hierarchical relationships and semantic constraints.
This “fact-checking” layer acts as a safety guardrail, vetoing predictions when needed, and ensuring outputs adhere to rigorous standards of medical practice.
Building trust through explainability
Additionally, the explainability of predictions is vital for widespread adoption of AI in healthcare. Neurosymbolic systems improve explainability in clinical AI by providing a clear audit trail. The symbolic layer is key here because it provides the explicit logic and defined relationships in the knowledge graph.
For instance, a neural network can predict a visit diagnosis based on statistical correlations, but when that prediction is supported by a transparent logical path through a medical ontology, users can better understand and trust the result.
Enhancing clinical AI with clinical terminology
For clinical AI to be adopted at scale, it must combine neural learning with symbolic reasoning grounded in clinical terminology. Healthcare AI is not about prediction; it’s about reasoning. Clinical terminology knowledge graphs, like the one offered by IMO Health, can modify AI-based systems from guessing machines into reasoning-driven intelligence that is both verifiable and explainable.
Curious how IMO Health can improve your clinical AI strategy? Book a demo to learn more.
References:
Neurosymbolic AI for Safe and Trustworthy High-Stakes Applications. Sujit Bhattacharya, Naveen Ashish. https://www.preprints.org/manuscript/202511.1342
Explainable Diagnosis Prediction through Neuro-Symbolic Integration. https://arxiv.org/html/2410.01855v1
Can Neuro-Symbolic AI Solve AI’s Weaknesses? https://tdwi.org/articles/2024/04/08/adv-all-can-neuro-symbolic-ai-solve-ai-weaknesses.aspx
