Research map / Vision-language and multimodal / Human and embodied modeling
From Complexity to Clarity: Transforming Chest X-ray Reports with Chained Prompting (Student Abstract)
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Proposes CPIR-MR, a chained prompting strategy using Gemini 1.5 Flash to rewrite IU X-Ray radiology reports into layperson-friendly Simplified Medical Reports, and a BLIP-based multi-modal text decoder (MTD) with QLoRA that generates simplified explanations from chest X-rays.
Problem
Chest X-ray radiology reports contain complex medical terminology that patients and non-healthcare professionals find hard to understand.
Approach
Iterative chained few-shot, chain-of-thought, and constrained prompting of an LLM to rewrite each radiology finding into a seven-line layperson explanation, creating a simplified extension of IU X-Ray, and a BLIP plus classifier multimodal decoder with biomedical condition embedding fine-tuned via QLoRA on those simplified reports.
Key results
- SMR similarity scores are biased toward the 8-10 range, per the similarity score distribution (Fig. 1c).
- Sentiment preservation shows a strong positive correlation between original and simplified text clustered around the diagonal (Fig. 1b).
- Qualitative examples show pneumothorax rephrased as collapsed lung while retaining the original meaning (Fig. 2).
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use CPIR-MR to simplify original medical findings | IU X-Ray reports · IU X-Ray | text complexity | Helped |
| Use CPIR-MR to simplify original medical findings | IU X-Ray reports · IU X-Ray | sentiment consistency | Helped |
| Use CPIR-MR to simplify original medical findings | IU X-Ray reports · IU X-Ray | similarity score | Helped |
| Use CPIR-MR to simplify original medical findings | IU X-Ray reports · IU X-Ray | readability | Helped |
| Train a multi-modal text decoder with SMRs, BLIP embeddings, classification probabilities, and BCE prompting | MTD · IU X-Ray | simplified explanation generation | Helped |
Limitations
- Evaluation relies on automatic metrics and an LLM judge (Gemini) with no human evaluation reported.
- Quantitative results are presented only as figures with no numeric tables.
- Some simplified outputs may over-simplify or lose detail, such as the cardiomediastinal silhouette rewording.
Experimental setup
Datasets: IU X-Ray
Benchmarks: IU X-Ray
Metrics: Flesch Reading Ease difference lexicon-based sentiment consistency similarity score (1-10) keyword overlap
Builds on: BLIP QLoRA IU X-Ray
Closest papers by meaning
- Reporting Practice Matters: The Impact of Reference Choice on Chest X-ray Report Evaluation (NeurIPS 2026 · similarity 0.86)
- MedRAX: Medical Reasoning Agent for Chest X-ray (ICML 2025 · similarity 0.86)
- CXReasonBench: A Benchmark for Evaluating Structured Diagnostic Reasoning in Chest X-rays (NeurIPS 2025 · similarity 0.85)
- DAMPER: A Dual-Stage Medical Report Generation Framework with Coarse-Grained MeSH Alignment and Fine-Grained Hypergraph Matching (AAAI 2025 · similarity 0.85)
- PromptMRG: Diagnosis-Driven Prompts for Medical Report Generation (AAAI 2024 · similarity 0.85)
- Bootstrapping Chest CT Image Understanding by Distilling Knowledge from X-ray Expert Models (CVPR 2024 · similarity 0.85)
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