ArticleHealthcare informatics research2025
Large Language Models in Medicine: Clinical Applications, Technical Challenges, and Ethical Considerations.
Article in Healthcare informatics research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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
22 citing papers in PubMed.
- Performance of large language models in ambiguous treatment scenarios of early breast cancer.Breast (Edinburgh, Scotland) · 2026Article
- Patient-Centered Summarization Framework for AI Clinical Summarization: Mixed Methods Study.Journal of medical Internet research · 2026Article
- ChatNSG: An Overview of Contemporary and Emerging Artificial Intelligence Models for the Neurosurgeon.Journal of neurological surgery. Part B, Skull base · 2026Article
- Article
- Symptom-Only Localization of Brainstem Ischemia Using Large Language Models Versus Neurologists in Diffusion-Weighted Imaging-Positive Cases: Retrospective Single-Center Study.JMIR formative research · 2026Article
- Mapping ICU nursing intervention notes to clinical care classification using a large language model: an evaluation study.BMC nursing · 2026Article
- Simplifying radiology reports with large language models: privacy-compliant open- versus closed-weight models.European radiology · 2026Article
- AI in respiratory care: findings from the GOLD report.Journal of translational medicine · 2026Article
- Comparative performance of recent and prior large language models and pediatric residents on pediatric in-training examination questions.Scientific reports · 2026Article
- TCM-CoT-RAG: a chain-of-thought enhanced retrieval-augmented generation system for clinical decision support in Traditional Chinese Medicine rheumatology.Frontiers in pharmacology · 2026Article
- Performance benchmarking of LLMs on Chinese national medical licensing education: Cross-lingual and question-type effects.PloS one · 2026Article
- MEDAI-LLM-SUMM: a reporting checklist for medical text summarization studies using large language models.Frontiers in digital health · 2026Article
- Benchmarking the readability, quality, and educational suitability of large language models in communicating pertussis.Frontiers in public health · 2026Article
- Performance of large language models in reporting oral health concerns and side effects in head and neck cancer: a comparative study.Journal of cancer research and clinical oncology · 2025Article
- Medical Feature Extraction From Clinical Examination Notes: Development and Evaluation of a Two-Phase Large Language Model Framework.JMIR medical informatics · 2025Article
- Advances in Hereditary Colorectal Cancer: How Precision Medicine Is Changing the Game.Cancers · 2025Review
- Role of Medical Editors in the Age of Generative Artificial Intelligence.Healthcare informatics research · 2025Article
- Digital Cardiovascular Twins, AI Agents, and Sensor Data: A Narrative Review from System Architecture to Proactive Heart Health.Sensors (Basel, Switzerland) · 2025Review
- ChatGPT Performance Deteriorated in Patients with Comorbidities When Providing Cardiological Therapeutic Consultations.Healthcare (Basel, Switzerland) · 2025Article
- UpSMART: five years of digital innovation in cancer clinical research-achievements, challenges, and recommendations.Frontiers in digital health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study presents a comprehensive review of the clinical applications, technical challenges, and ethical considerations associated with using large language models (LLMs) in medicine.
methodsA literature survey of peer-reviewed articles, technical reports, and expert commentary from relevant medical and artificial intelligence journals was conducted. Key clinical application areas, technical limitations (e.g., accuracy, validation, transparency), and ethical issues (e.g., bias, safety, accountability, privacy) were identified and analyzed.
resultsLLMs have potential in clinical documentation assistance, decision support, patient communication, and workflow optimization. The level of supporting evidence varies; documentation support applications are relatively mature, whereas autonomous diagnostics continue to face notable limitations regarding accuracy and validation. Key technical challenges include model hallucination, lack of robust clinical validation, integration issues, and limited transparency. Ethical concerns involve algorithmic bias risking health inequities, threats to patient safety from inaccuracies, unclear accountability, data privacy, and impacts on clinician-patient interactions.
conclusionsLLMs possess transformative potential for clinical medicine, particularly by augmenting clinician capabilities. However, substantial technical and ethical hurdles necessitate rigorous research, validation, clearly defined guidelines, and human oversight. Existing evidence supports an assistive rather than autonomous role, mandating careful, evidence-based integration that prioritizes patient safety and equity.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.