Enhancing Explainable Cardiac Diagnosis with Guide-Grounded Multimodal LLMs

2026-07-23 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Medical Devices & Health Technology, Health & Medical Research · Depth: Expert, quick

Summary

A novel guide-grounded multimodal framework enhances explainable cardiac diagnosis by integrating clinical knowledge into LLM-based ECG report generation. This system utilizes a convolutional neural network (CNN) and Grad-CAM to process 12-lead ECG images, producing class probabilities and heatmaps. Concurrently, authoritative ECG textbooks and guidelines are distilled into a structured "ECG Interpretation Guide," which is injected as a fixed knowledge block. A multimodal LLM then generates diagnostic reports, conditioned on the ECG image, Grad-CAM overlay, CNN-derived fact pack, and the injected guide, ensuring guideline-consistent terminology. Experiments on the full PTB-XL test set demonstrated improved semantic quality and perceived consistency, with the average BERTScore of generated impressions increasing from 0.818 to 0.953 compared to a CNN+Grad-CAM+MLLM baseline. This method offers a practical pathway to reduce hallucinations and enhance clinical plausibility for real-world deployment.

Key takeaway

For AI Scientists developing explainable medical AI, integrating external knowledge sources is crucial. You should consider distilling authoritative clinical guidelines into fixed knowledge blocks to ground multimodal LLMs, as this approach significantly reduces hallucinations and improves diagnostic report consistency. This method enhances the clinical plausibility of your models, bringing them closer to real-world deployment and increasing trust among medical professionals.

Key insights

Injecting a distilled clinical guide into multimodal LLMs significantly improves explainable cardiac diagnosis by reducing hallucinations and enhancing report consistency.

Principles

• Ground LLM explanations in curated clinical knowledge.
• Distill authoritative guidelines into structured knowledge blocks.

Method

A CNN and Grad-CAM process ECGs. An offline "ECG Interpretation Guide" is created from clinical texts. A multimodal LLM generates reports, conditioned on ECG data, heatmaps, CNN facts, and the injected guide.

In practice

• Use structured clinical guides to ground LLM outputs.
• Integrate Grad-CAM heatmaps for visual explanation.
• Apply BERTScore to evaluate report semantic quality.

Topics

• Explainable AI
• Cardiac Diagnosis
• Multimodal LLMs
• ECG Interpretation
• Clinical Knowledge Grounding
• Grad-CAM

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.