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MiniGPT-Med: Large Language Model as a General Interface for Radiology Diagnosis

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arxiv 2407.04106 v1 pith:7FUTRO2I submitted 2024-07-04 cs.AI cs.CLcs.CV

Asma Alkhaldi , Raneem Alnajim , Layan Alabdullatef , Rawan Alyahya , Jun Chen , Deyao Zhu , Ahmed Alsinan , Mohamed Elhoseiny This is my paper · ORCID
classification cs.AIcs.CLcs.CV
keywords medicalminigpt-medmodeldiagnosticgenerationradiologyreportaccuracy
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Recent advancements in artificial intelligence (AI) have precipitated significant breakthroughs in healthcare, particularly in refining diagnostic procedures. However, previous studies have often been constrained to limited functionalities. This study introduces MiniGPT-Med, a vision-language model derived from large-scale language models and tailored for medical applications. MiniGPT-Med demonstrates remarkable versatility across various imaging modalities, including X-rays, CT scans, and MRIs, enhancing its utility. The model is capable of performing tasks such as medical report generation, visual question answering (VQA), and disease identification within medical imagery. Its integrated processing of both image and textual clinical data markedly improves diagnostic accuracy. Our empirical assessments confirm MiniGPT-Med's superior performance in disease grounding, medical report generation, and VQA benchmarks, representing a significant step towards reducing the gap in assisting radiology practice. Furthermore, it achieves state-of-the-art performance on medical report generation, higher than the previous best model by 19\% accuracy. MiniGPT-Med promises to become a general interface for radiology diagnoses, enhancing diagnostic efficiency across a wide range of medical imaging applications.

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Cited by 11 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MMRad-22K: A Structured Multimodal Evidence Dataset for Chest X-ray Report Generation

    cs.CV 2026-02 conditional novelty 6.0 of 10

    A 21,994-case chest X-ray dataset with interleaved regional text and image crops helps LVLMs generate more clinically accurate reports than text-only chain-of-thought.

  2. Knowledge to Sight: Reasoning over Visual Attributes via Knowledge Decomposition for Abnormality Grounding

    cs.CV 2025-08 conditional novelty 6.0 of 10

    Decomposing clinical terms into visual attributes lets 0.23B-2B vision-language models match or beat much larger medical VLMs for abnormality grounding with only 16k training pairs.

  3. Interpreting Chest X-rays Like a Radiologist: A Benchmark with Clinical Reasoning

    cs.CV 2025-05 conditional novelty 6.0 of 10

    A new 8-stage chest X-ray VQA benchmark and a context-aware model trained on it.

  4. MM-Skin: Enhancing Dermatology Vision-Language Model with an Image-Text Dataset Derived from Textbooks

    cs.CV 2025-05 conditional novelty 6.0 of 10

    The authors release MM-Skin, a ~10k image-text and 27k QA dermatology dataset from textbooks, and show that a LLaVA-Med model fine-tuned on it (SkinVL) improves dermatology VQA and zero-shot classification relative to...

  5. GEMeX: A Large-Scale, Groundable, and Explainable Medical VQA Benchmark for Chest X-ray Diagnosis

    cs.CV 2024-11 conditional novelty 6.0 of 10

    GEMeX provides 1.6 million explainable, groundable chest X-ray VQA pairs across four question types and shows that current LVLMs perform poorly on it.

  6. Clinical Cognition Alignment for Gastrointestinal Diagnosis with Multimodal LLMs

    cs.CV 2026-03 unverdicted novelty 5.5 of 10

    Hierarchical clinical-reasoning SFT plus counterfactual GRPO yields SoTA diagnostic accuracy for multimodal LLMs on gastrointestinal endoscopy benchmarks.

  7. MCA-RG: Enhancing LLMs with Medical Concept Alignment for Radiology Report Generation

    cs.CV 2025-07 conditional novelty 5.0 of 10

    MCA-RG uses concept alignment, contrastive learning, matching loss, and feature gating to generate radiology reports, reporting SOTA on MIMIC-CXR and CheXpert Plus.

  8. Online Iterative Self-Alignment for Radiology Report Generation

    cs.CV 2025-05 conditional novelty 5.0 of 10

    The paper proposes an iterative self-alignment loop for radiology report generation that re-trains a small model on its own metric-ranked reports and reports gains over three iterations.

  9. Large Language Model with Region-guided Referring and Grounding for CT Report Generation

    cs.CV 2024-11 conditional novelty 5.0 of 10

    Reg2RG grounds each generated CT report section in a specific anatomical region by combining decoupled local texture and geometry features with global volume features in an LLM, outperforming prior methods on two ches...

  10. Taming Vision-Language Models for Medical Image Analysis: A Comprehensive Review

    eess.IV 2025-06 conditional novelty 3.0 of 10

    A survey that classifies vision-language model adaptation for medical imaging into five strategies across eleven tasks, with challenges and future directions.

  11. Multimodal Large Language Models for Medicine: A Comprehensive Survey

    cs.LG 2025-04 conditional novelty 2.0 of 10

    A comprehensive review cataloging medical MLLMs, their uses in report generation, diagnosis, and treatment, and the challenges of accuracy, hallucination, fairness, privacy, and deployment.

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