A Multimodal, Electrocardiography-Centric Clinical Data Platform With Artificial Intelligence-Assisted Querying for...

Large-scale electrocardiographic (ECG) datasets are increasingly available but remain fragmented, poorly integrated with patient-level phenotypic data, and difficult for clinicians to query without programming expertise. We present VRCardio-Helper-Database, a unified clinical data platform that integrates ECG recordings from nine sources with patient demographic and anthropometric information and exposes an artificial-intelligence-assisted natural-language querying interface, built on a Model Context Protocol server, that translates clinical questions into safe, read-only database queries.

The integrated repository comprises approximately 1,185,800 ECG records from approximately 432,000 patients, with derived signal-processing metrics for the standard 12-lead sources and, for the 46-record in-house VRCardio-Explore cohort only, simultaneously acquired anthropometric measurements and electrocardiographic imaging signals. We evaluated the platform across five dimensions: dataset characterization; semantic accuracy of natural-language querying against a 40-query benchmark with consensus ground-truth Structured Query Language (SQL); robustness to linguistic variation; representative clinical use cases; and an expert clinical assessment.

Query interpretation achieved 90% precision (95% confidence interval (CI), 77-96) and 98% recall (95% CI, 87-100), with a mean intersection-over-union of 0.97 across five reformulation clusters. Two senior cardiologists posed 35 free-form research queries, of which 28 returned a response (an 80% response-completion rate; the remaining 20% timed out), and rated the returned responses highly for medical correctness and interpretability (pooled overall quality 7.6/10; range, 6.9-8.4); because these ratings are computed only over responded queries, they should be read as an upper bound on real-world usability. Error analysis found that the few failures were associated with identifiable schema and terminology mismatches (unit conventions, dataset-specific diagnostic code strings, and cross-source synonyms).

By reducing the programming expertise needed to work with multimodal ECG data and enabling intuitive access, the platform may facilitate hypothesis generation in cardiovascular research. Given the modest evaluation size (a 40-query benchmark and 28 rated expert responses), a subset of queries that returned service-availability timeouts, and data-quality caveats in some derived metrics, these results should be read as an initial, carefully curated evaluation, and larger prospective multicenter validation is still needed.