A knowledge graph-driven framework for deploying AI-powered patient digital twins

• Enable patient digital twin deployment via a knowledge-driven, modular framework • Provide clinical data access and simulation through a FHIR-compliant API • Publish MIMO ontology to standardize and increase transparency of AI model interfaces • Automate AI model integration using a manifest-based binding protocol • Benchmark stroke risk models with real-time, time-aware clinical data streams Background: The healthcare sector faces diverse challenges, including poor interoperability and a lack of personalized approaches, which limit patient outcomes. Ineffective data exchange and one-size-fits-all treatments fail to meet individual needs. Emerging technologies like digital twins (DTs), the semantic web, and AI show promise in tackling these obstacles. For this reason, we introduced CONNECTED, a conceptual multi-level framework that combines these techniques to deploy general-purpose patient DTs. Objective: This study assesses CONNECTED’s comprehensiveness, applicability, and utility for developing intelligent, personalized healthcare applications. Specifically, we deliver a preliminary version of the framework to predict future patient states and demonstrate its automation benefits in deploying semantically enriched, AI-powered patient DTs. Methods: We enhanced the CONNECTED architecture by providing a formal definition of DT and modularizing its core functionalities into microservices—Properties, State, Capabilities, and Manifest. The Manifest service facilitates AI model integration through the Model Interface Manifest Ontology (MIMO), enabling automatic data-to-model binding via a reasoner. Using the HeartBeatKG quality assessment tool, we validated MIMO and tested the internal logic by integrating a well-established stroke-risk model. Results: Our implementation comprehends: (1) deploying a FHIR-compliant, patient-centric API for clinical history access, real-time monitoring, and predictive simulation; (2) publishing MIMO; (3) establishing the Manifest protocol for seamless, general-purpose AI model integration tailored to individual patient profiles; and (4) a proof-of-concept benchmarking application comparing multiple stroke risk classifiers. Conclusion: CONNECTED establishes a flexible, scalable foundation for interoperable semantic patient DTs. Automation reduces technical overhead and enables users to focus on delivering personalized, insight-driven care.