ArticlemedRxiv : the preprint server for health sciences2026
Autonomous Agents for Auditable Cardiovascular Artificial Intelligence Development.
Article in medRxiv : the preprint server for health sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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0 citing papers in PubMed.
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Authors and funding
6 authors.
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Abstract
Clinical artificial intelligence (AI) models are usually reported as finished artifacts, but each model reflects a limited human search across a much larger space of architectures, inputs, losses, optimizers, and training recipes. We tested whether autonomous code-writing agents could perform a controlled model-development experiment: proposing and evaluating code changes, and seeking performance gains without new data or human-guided edits. We built two such agents: an Iteration Agent that searches sequentially, keeping the best variant at each step, and an Evolution Agent that searches for variations in parallel using multiple large language models and prioritizes high-performing lineages across generations. In two architecturally distinct AI-enhanced electrocardiography (AI-ECG) models for structural heart disease, agent-optimized variants improved rank discrimination across held-out, external, and cross-institution evaluations, with area under the receiver operating characteristic curve gains of +0.006 to +0.039 (paired
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.