From segmentation to explanation: Generating textual reports from MRI with LLMs
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View abstract on PubMed
Summary
This summary is machine-generated.This study enhances AI explainability in medical imaging by combining semantic segmentation with Large Language Models (LLMs) to generate trustworthy, human-readable diagnostic reports, improving clinician confidence in AI. The code is publicly available for reproducibility.
Area Of Science
- Medical Imaging and Artificial Intelligence
- Natural Language Processing in Healthcare
Background
- Deep learning models in medical imaging lack transparency, hindering clinician trust in AI diagnoses.
- Explainable AI (XAI) is crucial for integrating AI into clinical practice and ensuring reliable healthcare outcomes.
Purpose Of The Study
- To develop a novel framework for enhancing AI explainability in medical imaging.
- To generate comprehensive, human-readable medical reports from AI analyses using Large Language Models (LLMs).
Main Methods
- Integration of semantic segmentation models with atlas-based mapping and LLMs for report generation.
- Implementation of an anti-hallucination design using structured JSON and prompt constraints to ensure factual accuracy.
- Validation of the framework on brain tumor (glioma) and multiple sclerosis lesion detection tasks.
Main Results
- High segmentation accuracy achieved with the SegResNet model.
- LLMs (Gemma, Llama, Mistral) demonstrated effectiveness in generating diverse and informative explanatory reports.
- Generated reports were evaluated for lexical diversity, readability, coherence, and information coverage.
Conclusions
- The proposed method significantly enhances the transparency and interpretability of AI in medical imaging.
- The framework's generalizability was validated across different medical imaging scenarios, increasing trust in AI applications.
- Publicly available code and examples facilitate the adoption and further development of explainable AI in healthcare.
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