Neuro-Symbolic Digital Twin Intelligence for Explainable Precision Healthcare: Integrating Foundation Models, Multimodal Clinical Data and Causal Decision Analytics

Abstract

Precision healthcare requires models that not only predict clinical outcomes accurately but also explain why a given intervention should work for a specific patient and support reasoning about interventions that have not yet been observed. Purely neural foundation models excel at pattern recognition over multimodal clinical data but offer limited guarantees of logical consistency with established clinical knowledge and their correlational predictions do not, on their own, support reliable treatment-effect reasoning. This paper proposes a neuro-symbolic digital twin framework that maintains a continuously updated, patient-specific representation combining neural embeddings of clinical text, imaging and genomic data with a symbolic knowledge layer grounded in clinical ontologies and guideline rules. A causal decision analytics engine operates over this twin state to estimate individualized treatment effects and support counterfactual, 'what-if' clinical reasoning, while a dedicated explainability layer exposes causal graphs, counterfactual traces and confidence intervals to the clinician. We evaluate the framework on a curated multimodal pilot cohort against a correlational machine learning baseline and a neural-only foundation model, finding improvements in treatment recommendation accuracy, causal effect estimation error, counterfactual consistency and clinician-rated trust, at a modest increase in computational latency. We discuss the architectural and governance implications of these findings and outline a path toward prospective clinical validation.