ArticleCommunications engineering2024
Interactive computer-aided diagnosis on medical image using large language models.
Article in Communications engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
53 citing papers in PubMed.
- Large language models and conditional rules in clinical decision support systems.Health information science and systems · 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
- Responsible AI for safer opioid risk management in older adults.npj health systems · 2026Article
- Understanding uncertainty in large language model predictions of early death in critically ill patients: a conformal prediction approach.JAMIA open · 2026Article
- Evaluating the performance of ChatGPT in responding to myotonic dystrophy type 1 patient inquiries: a specialist physician-based study.BMC neurology · 2026Article
- A Language Model for Pediatric Occupational Therapy Documentation: Model Development and Pilot Study.JMIR AI · 2026Article
- Overcoming domain-specific challenges for artificial intelligence in abdominal oncology toward clinical translation.Discover oncology · 2026Review
- Artificial intelligence and computer-aided diagnosis in diagnostic decisions: 5 questions for medical informatics and human-computer interface research.Journal of the American Medical Informatics Association : JAMIA · 2026Review
- Self-Driving Microscopes: AI Meets Super-Resolution Microscopy.Small methods · 2026Review
- Educational support and weekly telemonitoring during rapid maxillary expansion in children: a prospective preference-based comparative cohort study.Frontiers in dental medicine · 2026Article
- Recent advances in artificial intelligence for radiology report generation: a brief review.BJR artificial intelligence · 2026Review
- Agreement of Feline Grimace Scale scores between chatbots and an expert rater.Scientific reports · 2025Article
- Generative AI and foundation models in medical image.Radiological physics and technology · 2025Review
- PlaqueCap: lesion-centered captioning of atherosclerotic plaques in intravascular ultrasound using vision-language models and prompt injection.NPJ digital medicine · 2025Article
- State of the art review of AI in renal imaging.Abdominal radiology (New York) · 2025Review
- A deep learning based automatic report generator for retinal optical coherence tomography images.NPJ digital medicine · 2025Article
- Assessment of ChatGPT performance in orbital MRI reporting with multimetric evaluation of transformer based language models.Scientific reports · 2025Article
- Diagnostic accuracy of generative large language artificial intelligence models for the assessment of dental crowding.BMC oral health · 2025Article
- From large language models to multimodal AI: a scoping review on the potential of generative AI in medicine.Biomedical engineering letters · 2025Review
- Large Language Models in Medical Image Analysis: A Systematic Survey and Future Directions.Bioengineering (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Computer-aided diagnosis (CAD) has advanced medical image analysis, while large language models (LLMs) have shown potential in clinical applications. However, LLMs struggle to interpret medical images, which are critical for decision-making. Here we show a strategy integrating LLMs with CAD networks. The framework uses LLMs' medical knowledge and reasoning to enhance CAD network outputs, such as diagnosis, lesion segmentation, and report generation, by summarizing information in natural language. The generated reports are of higher quality and can improve the performance of vision-based CAD models. In chest X-rays, an LLM using ChatGPT improved diagnosis performance by 16.42 percentage points compared to state-of-the-art models, while GPT-3 provided a 15.00 percentage point F1-score improvement. Our strategy allows accurate report generation and creates a patient-friendly interactive system, unlike conventional CAD systems only understood by professionals. This approach has the potential to revolutionize clinical decision-making and patient communication.
Identifiers
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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 OpenQuestion graph.