ReviewArtificial intelligence in medicine2024
Transformers and large language models in healthcare: A review.
Review in Artificial intelligence in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers, 2 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
67 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Safeguarding biomedical AI: a critical scoping review of privacy-enhancing technologies, hybrid approaches, and deployment models.Frontiers in digital health · 2026Pooled it
- Systematic review of artificial intelligence use in behavioral analysis of invertebrate and larval model organisms: methods, applications and future recommendations.Frontiers in artificial intelligence · 2026Pooled it
- Trial
- Article
- Large Language Models for Ankle Fracture Classification and Management Prediction from Routine Clinical Documentation: A Single-Center Exploratory Study.Journal of imaging informatics in medicine · 2026Article
- Lung cancer research: how natural language processing enhances clinical data extraction.Journal of thoracic disease · 2026Article
- 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
- LLMs in Medical Education for Autism Caregivers: A Comparative Evaluation of Accuracy, Readability, Actionability, and Neurodiversity-Affirming Language.Healthcare (Basel, Switzerland) · 2026Article
- Diagnostic capability of large language models in critically ill patients: a prospective single-centre study comparing ChatGPT, Claude, and Gemini with emergency physicians.BMC emergency medicine · 2026Observational
- A Transformer-Residual Network-Integrated Deep Learning Model for Digital Pathology-Based Grading and Risk Stratification of Metabolic Dysfunction-Associated Steatotic Liver Disease-Related Liver Fibrosis.Clinical and translational gastroenterology · 2026Article
- AquaAI: development and internal validation of a Danish transformer-based model to identify drowning and aquatic incidents in prehospital medical records.Scandinavian journal of trauma, resuscitation and emergency medicine · 2026Article
- Large language models in neuro-oncology: promise, pitfalls, and the path forward.European radiology · 2026Article
- Automated RECIST tumor response classification through prompt-guided large language models.Scientific reports · 2026Article
- Can NLP Detect Loneliness in Electronic Health Records? A Proof-of-Concept Study.Studies in health technology and informatics · 2026Article
- Women's Cardiovascular Disease and Stroke Risk Stratification Using a Precision and Personalized Framework Embedded with an Explainable Artificial Intelligence Paradigm: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- K-STAMM: a knowledge-enhanced spatial - temporal attention model with multimodal fusion for pneumonia prediction.Scientific reports · 2026Article
- AI-assisted tumor board decision-making in pancreatic oncology.BMC medical informatics and decision making · 2026Article
- Alignment of Large Language Model Responses With Human Therapists in Motivational Interviewing.JAMA network open · 2026Article
- From BERT to GPT-4: A systematic review of AI-Driven toxicity extraction and grading in radiation oncology.Technical innovations & patient support in radiation oncology · 2026Article
- Multimodal foundation models in colorectal cancer: from prediction to trustworthy clinical insight.Briefings in bioinformatics · 2026Review
7 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
12 authors.
Funding
Abstract
With Artificial Intelligence (AI) increasingly permeating various aspects of society, including healthcare, the adoption of the Transformers neural network architecture is rapidly changing many applications. Transformer is a type of deep learning architecture initially developed to solve general-purpose Natural Language Processing (NLP) tasks and has subsequently been adapted in many fields, including healthcare. In this survey paper, we provide an overview of how this architecture has been adopted to analyze various forms of healthcare data, including clinical NLP, medical imaging, structured Electronic Health Records (EHR), social media, bio-physiological signals, biomolecular sequences. Furthermore, which have also include the articles that used the transformer architecture for generating surgical instructions and predicting adverse outcomes after surgeries under the umbrella of critical care. Under diverse settings, these models have been used for clinical diagnosis, report generation, data reconstruction, and drug/protein synthesis. Finally, we also discuss the benefits and limitations of using transformers in healthcare and examine issues such as computational cost, model interpretability, fairness, alignment with human values, ethical implications, and environmental impact.
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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.