ReviewQuantitative imaging in medicine and surgery2024
The role of large language models in medical image processing: a narrative review.
Review in Quantitative imaging in medicine and surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
43 citing papers in PubMed.
- Large language models and large multimodal models in radiology: opportunities, challenges, and the path toward sustainable long-term clinical integration.Japanese journal of radiology · 2026Review
- Artificial intelligence-enabled cytopathological interpretation of fluorescence microscopy images via a multimodal large language model.Quantitative biology (Beijing, China) · 2026Article
- A CNN-based liver ultrasound plane recognition system with multimodal large language model-generated interpretable feedback.Abdominal radiology (New York) · 2026Article
- Automated iridocorneal angle classification using a multimodal large language model.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
- Large language models in neuro-oncology: promise, pitfalls, and the path forward.European radiology · 2026Article
- Artificial Intelligence in Occupational Health Surveillance: Evaluating AI-Assisted ILO Classification of Radiographs of Pneumoconioses.La Medicina del lavoro · 2026Article
- Imaging Biomarkers in Radiotherapy.Cancers · 2026Review
- AgentMRI: A Vison Language Model-Powered AI System for Self-regulating MRI Reconstruction with Multiple Degradations.Journal of imaging informatics in medicine · 2026Article
- LLM-based medical dialogue dataset generation with automated instructions.Scientific reports · 2026Article
- Advancing trauma scoring through large language models: automated estimation of injury severity.BMC emergency medicine · 2026Article
- A data-efficient 3D medical vision-language model using only a 2D encoder.Scientific reports · 2026Article
- Quantifying uncert-AI-nty: Testing the accuracy of LLMs' confidence judgments.Memory & cognition · 2026Article
- Observational
- Benchmarking multimodal large language models for medicinal plant identification.Frontiers in plant science · 2026Article
- Ethical Use of Artificial Intelligence for Processing Medical Images.Journal of Korean medical science · 2025Article
- Using generative artificial intelligence in clinical practice: a narrative review and proposed agenda for implementation.The Medical journal of Australia · 2025Review
- Assessment of ChatGPT performance in orbital MRI reporting with multimetric evaluation of transformer based language models.Scientific reports · 2025Article
- Evaluating the clinical utility of multimodal large language models for detecting age-related macular degeneration from retinal imaging.Scientific reports · 2025Article
- Two stage large language model approach enhancing entity classification and relationship mapping in radiology reports.Scientific reports · 2025Article
- DeepGut: A collaborative multimodal large language model framework for digestive disease assisted diagnosis and treatment.World journal of gastroenterology · 2025Observational
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Background and Objective: The rapid advancement of artificial intelligence (AI) has ushered in a new era in natural language processing (NLP), with large language models (LLMs) like ChatGPT leading the way. This paper explores the profound impact of AI, particularly LLMs, in the field of medical image processing. The objective is to provide insights into the transformative potential of AI in improving healthcare by addressing historical challenges associated with manual image interpretation. Methods: A comprehensive literature search was conducted on the Web of Science and PubMed databases from 2013 to 2023, focusing on the transformations of LLMs in Medical Imaging Processing. Recent publications on the arXiv database were also reviewed. Our search criteria included all types of articles, including abstracts, review articles, letters, and editorials. The language of publications was restricted to English to facilitate further content analysis. Key Content and Findings: The review reveals that AI, driven by LLMs, has revolutionized medical image processing by streamlining the interpretation process, traditionally characterized by time-intensive manual efforts. AI's impact on medical care quality and patient well-being is substantial. With their robust interactivity and multimodal learning capabilities, LLMs offer immense potential for enhancing various aspects of medical image processing. Additionally, the Transformer architecture, foundational to LLMs, is gaining prominence in this domain. Conclusions: In conclusion, this review underscores the pivotal role of AI, especially LLMs, in advancing medical image processing. These technologies have the capacity to enhance transfer learning efficiency, integrate multimodal data, facilitate clinical interactivity, and optimize cost-efficiency in healthcare. The potential applications of LLMs in clinical settings are promising, with far-reaching implications for future research, clinical practice, and healthcare policy. The transformative impact of AI in medical image processing is undeniable, and its continued development and implementation are poised to reshape the healthcare landscape for the better.
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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.