ArticleJournal of medical Internet research2024
Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook.
Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 132 papers, 7 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
132 citing papers in PubMed, 7 syntheses or guidelines pooled it.
- The role of AI-assisted drug repurposing in neurological disorders: a systematic review of validation strategies, challenges and opportunities.Journal of nanobiotechnology · 2026Pooled it
- From interface to outcome: a 4I framework for AI-linked functionality in electronic health records.BMC medical informatics and decision making · 2026Pooled it
- Artificial Intelligence for Predicting Treatment Response in Patients With Anxiety Disorders After Cognitive Behavioral Therapy: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Unimodal to multimodal: a systematic review of predictive machine learning models for valvular heart diseases.Frontiers in cardiovascular medicine · 2026Pooled it
- Performance and improvement strategies for adapting generative large language models for electronic health record applications: A systematic review.International journal of medical informatics · 2026Pooled it
- Implementation of generative AI for the assessment and treatment of autism spectrum disorders: a scoping review.Frontiers in psychiatry · 2025Pooled it
- Exploring the application boundaries of LLMs in mental health: a systematic scoping review.Frontiers in psychology · 2025Pooled it
- Periodontitis Risk Assessment and Prevention Planning: Comparative Study of Multimodal Large Language Models and Periodontist Evaluations.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
- Diagnostic Accuracy of Multimodal Large Language Models (LLMs) for Detecting Tuberous Breast Deformity from Standardized Photographs: A Descriptive in Silico Study.Aesthetic plastic surgery · 2026Article
- Multimodal Artificial Intelligence in Lung Cancer: From Data Integration to Precision Oncology.Cancers · 2026Review
- Less can be better: decomposing clinical data modalities in large language model-based healthcare applications.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Limitations of off-the-shelf multimodal foundation models in secondary caries detection on interproximal radiographs.Odontology · 2026Article
- Evaluating foundation models on official German medical licensing examinations: Implications for high-stakes assessment and AI-assisted medical education.NPJ digital medicine · 2026Article
- Article
- Large language models in spine care and research.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Review
- Critical Care-Specific vs General-Purpose Large Language Models in Emergency Intensive Care Unit Diagnosis: Single-Center Retrospective Paired Comparative Study.Journal of medical Internet research · 2026Article
- Can ChatGPT pass the polish national medical specialization examination in orthopedics and traumatology?Archives of orthopaedic and trauma surgery · 2026Article
- Divergent impacts of explainable AI for dermatological diagnosis on clinicians versus lay people.Nature medicine · 2026Article
- Review
72 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the complex and multidimensional field of medicine, multimodal data are prevalent and crucial for informed clinical decisions. Multimodal data span a broad spectrum of data types, including medical images (eg, MRI and CT scans), time-series data (eg, sensor data from wearable devices and electronic health records), audio recordings (eg, heart and respiratory sounds and patient interviews), text (eg, clinical notes and research articles), videos (eg, surgical procedures), and omics data (eg, genomics and proteomics). While advancements in large language models (LLMs) have enabled new applications for knowledge retrieval and processing in the medical field, most LLMs remain limited to processing unimodal data, typically text-based content, and often overlook the importance of integrating the diverse data modalities encountered in clinical practice. This paper aims to present a detailed, practical, and solution-oriented perspective on the use of multimodal LLMs (M-LLMs) in the medical field. Our investigation spanned M-LLM foundational principles, current and potential applications, technical and ethical challenges, and future research directions. By connecting these elements, we aimed to provide a comprehensive framework that links diverse aspects of M-LLMs, offering a unified vision for their future in health care. This approach aims to guide both future research and practical implementations of M-LLMs in health care, positioning them as a paradigm shift toward integrated, multimodal data-driven medical practice. We anticipate that this work will spark further discussion and inspire the development of innovative approaches in the next generation of medical M-LLM systems.
Indexed as
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What Socratic holds
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.