ReviewFrontiers in artificial intelligence2024
Vision-language models for medical report generation and visual question answering: a review.
Review in Frontiers in artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
What it found
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Who cites it
48 citing papers in PubMed.
- AI-Based EMG Reporting: A Randomized Controlled Trial.Journal of neurology · 2025Trial
- Comparison of Two AI Chatbots for Diagnosis and Providing Treatment Suggestions in Retinopathy of Prematurity: Retrospective Study.Journal of medical Internet research · 2026Article
- Verification of the impact of differences between objective and subjective evaluation methods on the interpretation of artificial intelligence systems generating mammogram reports using vision-language model.Japanese journal of radiology · 2026Article
- Multimodal large language models for bladder tumor detection in cystoscopy: a retrospective benchmarking study.World journal of urology · 2026Article
- CONRep: Uncertainty-Aware Vision-Language Report Drafting Using Conformal Prediction.Journal of imaging informatics in medicine · 2026Article
- On the Utility of Foundation Models for Fast MRI: Vision-Language-Guided Image Reconstruction.Magnetic resonance in medicine · 2026Article
- Illusion of competence: vision-language models provide confident but inaccurate explanations in cytological diagnostics.Scientific reports · 2026Article
- Zero-shot multimodal large language models underperform a domain-trained CNN baseline in pediatric wrist fracture detection.Scientific reports · 2026Article
- Artificial intelligence-assisted quality assessment of mid-trimester ultrasound examinations using large vision-language models.BMC pregnancy and childbirth · 2026Article
- Context-Aware Sentence Classification of Radiology Reports Using Synthetic Data: Development and Validation Study.Journal of medical Internet research · 2026Article
- Validation of a Dermatology-Focused Multimodal Image-and-Data Assistant in Diagnosis and Management of Common Dermatologic Conditions.Medicina (Kaunas, Lithuania) · 2026Article
- Leveraging AI solutions for sustainable practice in pediatric radiology: a practical guide and an educational tool.Pediatric radiology · 2026Review
- SmartSport: crowd counting meets large language models for smart facility management.Scientific reports · 2026Article
- Review
- FaceScanPaliGemma multi-agent vision language models for facial attribute recognition.Scientific reports · 2026Article
- A data-efficient 3D medical vision-language model using only a 2D encoder.Scientific reports · 2026Article
- A Comparative Assessment of ChatGPT, Gemini, and DeepSeek Accuracy: Examining Visual Medical Assessment in Internal Medicine Cases with and Without Clinical Context.Diagnostics (Basel, Switzerland) · 2026Article
- A Systematic Literature Review on Integrated Deep Learning and Multiagent Vision-Language Frameworks for Pathology Image Analysis and Report Generation.Computational and structural biotechnology journal · 2026Review
- TC3-VLM: A Vision-Language Model for Tactical Combat Casualty Care.IEEE access : practical innovations, open solutions · 2026Article
- Benchmark evaluation of multi-modal large language models for ophthalmic diagnosis in real world.Frontiers in medicine · 2026Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Medical vision-language models (VLMs) combine computer vision (CV) and natural language processing (NLP) to analyze visual and textual medical data. Our paper reviews recent advancements in developing VLMs specialized for healthcare, focusing on publicly available models designed for medical report generation and visual question answering (VQA). We provide background on NLP and CV, explaining how techniques from both fields are integrated into VLMs, with visual and language data often fused using Transformer-based architectures to enable effective learning from multimodal data. Key areas we address include the exploration of 18 public medical vision-language datasets, in-depth analyses of the architectures and pre-training strategies of 16 recent noteworthy medical VLMs, and comprehensive discussion on evaluation metrics for assessing VLMs' performance in medical report generation and VQA. We also highlight current challenges facing medical VLM development, including limited data availability, concerns with data privacy, and lack of proper evaluation metrics, among others, while also proposing future directions to address these obstacles. Overall, our review summarizes the recent progress in developing VLMs to harness multimodal medical data for improved healthcare applications.
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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 Socratic graph.