ArticleCureus2023
Embracing Large Language Models for Medical Applications: Opportunities and Challenges.
Article in Cureus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers, 4 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
79 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- The use of artificial intelligence based modelling techniques in One Health-related infectious disease studies in Sub-Saharan Africa: a review.Frontiers in artificial intelligence · 2026Pooled it
- Unveiling the Potential of Large Language Models in Transforming Chronic Disease Management: Mixed Methods Systematic Review.Journal of medical Internet research · 2025Pooled it
- A systematic review of large language model (LLM) evaluations in clinical medicine.BMC medical informatics and decision making · 2025Pooled it
- Testing and Evaluation of Health Care Applications of Large Language Models: A Systematic Review.JAMA · 2025Pooled it
- Integrating large language models into prostate cancer training: evidence from comparative benchmarking and a pilot randomized trial.BMC medical education · 2026Trial
- A fine-tuned large language model chatbot for multi-scenario radiology cancer care: randomized controlled trial on interaction optimization, emotional support, and provider burnout reduction.Journal of translational medicine · 2026Trial
- Design and Evaluation of a Source-Grounded Medical LLM for Clinical Decision Support and Patient Care in Trustworthy Diagnostic Systems.Diagnostics (Basel, Switzerland) · 2026Article
- Interaction of artificial intelligence, mental disorders, and diverse data modalities: Potential treatment management based on the "method-disease-data" axis.Neural regeneration research · 2026Article
- Embracing Large Language Models for Medical Applications, Part II: Building a Framework for Clinical Stewardship.Cureus · 2026Article
- Exploring the Role of Large Language Models in Primary Care: Qualitative Study of Physicians in the United States and the Netherlands.JMIR medical informatics · 2026Article
- Enhancing patient care with AI agents: integrating advanced chatbot technologies for improved healthcare delivery.BMC medical informatics and decision making · 2026Article
- Evaluation of large language models in cardiovascular surgery: a comparative study of board-level clinical question answering and generation.Journal of cardiothoracic surgery · 2026Article
- Comparison of the performance of large language models in answering patient questions related to cataract.iScience · 2026Article
- Psychometric properties and detectability of GPT-4o-generated multiple-choice questions compared with human-authored items across imaging specialties.NPJ digital medicine · 2026Article
- A comparative study of four large language models in treatment decision-making for Hip impingement syndrome based on real-world data.Frontiers in sports and active living · 2026Article
- Artificial Intelligence-Assisted Error Detection in Complex Clinical Documentation: Leveraging Large Language Models to Enhance Patient Safety in Oncology.JCO clinical cancer informatics · 2026Article
- Med-Diet: evaluation of an LLM-based system for clinically guided nutrition care in chronic diseases.Frontiers in nutrition · 2026Article
- Research on the application of LLaVA model based on QLoRA fine-tuning in medical teaching.PloS one · 2026Article
- Appropriateness of Thyroid Nodule Cancer Risk Assessment and Management Recommendations Provided by Large Language Models.Journal of imaging informatics in medicine · 2025Article
- AI-enabled language models (LMs) to large language models (LLMs) and multimodal large language models (MLLMs) in drug discovery and development.Journal of advanced research · 2025Review
19 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large language models (LLMs) have the potential to revolutionize the field of medicine by, among other applications, improving diagnostic accuracy and supporting clinical decision-making. However, the successful integration of LLMs in medicine requires addressing challenges and considerations specific to the medical domain. This viewpoint article provides a comprehensive overview of key aspects for the successful implementation of LLMs in medicine, including transfer learning, domain-specific fine-tuning, domain adaptation, reinforcement learning with expert input, dynamic training, interdisciplinary collaboration, education and training, evaluation metrics, clinical validation, ethical considerations, data privacy, and regulatory frameworks. By adopting a multifaceted approach and fostering interdisciplinary collaboration, LLMs can be developed, validated, and integrated into medical practice responsibly, effectively, and ethically, addressing the needs of various medical disciplines and diverse patient populations. Ultimately, this approach will ensure that LLMs enhance patient care and improve overall health outcomes for all.
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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.