Large Language Models in Healthcare

• URL: http://arxiv.org/abs/2503.04748v2
• Date: Wed, 02 Apr 2025 22:56:12 GMT
• Title: Large Language Models in Healthcare
• Authors: Mohammed Al-Garadi, Tushar Mungle, Abdulaziz Ahmed, Abeed Sarker, Zhuqi Miao, Michael E. Matheny,
• Abstract summary: Large language models (LLMs) hold promise for transforming healthcare.<n>Their successful integration requires rigorous development, adaptation, and evaluation strategies tailored to clinical needs.
• Score: 4.119811542729794
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large language models (LLMs) hold promise for transforming healthcare, from streamlining administrative and clinical workflows to enriching patient engagement and advancing clinical decision-making. However, their successful integration requires rigorous development, adaptation, and evaluation strategies tailored to clinical needs. In this Review, we highlight recent advancements, explore emerging opportunities for LLM-driven innovation, and propose a framework for their responsible implementation in healthcare settings. We examine strategies for adapting LLMs to domain-specific healthcare tasks, such as fine-tuning, prompt engineering, and multimodal integration with electronic health records. We also summarize various evaluation metrics tailored to healthcare, addressing clinical accuracy, fairness, robustness, and patient outcomes. Furthermore, we discuss the challenges associated with deploying LLMs in healthcare--including data privacy, bias mitigation, regulatory compliance, and computational sustainability--and underscore the need for interdisciplinary collaboration. Finally, these challenges present promising future research directions for advancing LLM implementation in clinical settings and healthcare.

Related papers

• The Rise of Small Language Models in Healthcare: A Comprehensive Survey [8.586655727885374]
  Small language models (SLMs) offer a scalable and clinically viable solution for next-generation healthcare informatics. Our comprehensive survey presents a taxonomic framework to identify and categorize them for healthcare professionals. We present a compilation of experimental results across widely studied NLP tasks in healthcare to highlight the transformative potential of SLMs in healthcare.
  arXiv  Detail & Related papers  (2025-04-23T22:02:25Z)
• Med-CoDE: Medical Critique based Disagreement Evaluation Framework [72.42301910238861]
  The reliability and accuracy of large language models (LLMs) in medical contexts remain critical concerns. Current evaluation methods often lack robustness and fail to provide a comprehensive assessment of LLM performance. We propose Med-CoDE, a specifically designed evaluation framework for medical LLMs to address these challenges.
  arXiv  Detail & Related papers  (2025-04-21T16:51:11Z)
• Structured Outputs Enable General-Purpose LLMs to be Medical Experts [50.02627258858336]
  Large language models (LLMs) often struggle with open-ended medical questions. We propose a novel approach utilizing structured medical reasoning. Our approach achieves the highest Factuality Score of 85.8, surpassing fine-tuned models.
  arXiv  Detail & Related papers  (2025-03-05T05:24:55Z)
• Vision Language Models in Medicine [3.964982657945488]
  Medical Vision-Language Models (Med-VLMs) integrate visual and textual data to enhance healthcare outcomes.<n>The transformative impact of Med-VLMs on clinical practice, education, and patient care is highlighted.<n> challenges like data scarcity, narrow task generalization, interpretability issues, and ethical concerns like fairness, accountability, and privacy are highlighted.<n>Future directions include leveraging large-scale, diverse datasets, improving cross-modal generalization, and enhancing interpretability.
  arXiv  Detail & Related papers  (2025-02-24T22:53:22Z)
• Participatory Assessment of Large Language Model Applications in an Academic Medical Center [1.244412242301951]
  Large Language Models (LLMs) have shown promising performance in healthcare-related applications. Their deployment in the medical domain poses unique challenges of ethical, regulatory, and technical nature.
  arXiv  Detail & Related papers  (2024-12-09T21:45:35Z)
• Demystifying Large Language Models for Medicine: A Primer [50.83806796466396]
  Large language models (LLMs) represent a transformative class of AI tools capable of revolutionizing various aspects of healthcare. This tutorial aims to equip healthcare professionals with the tools necessary to effectively integrate LLMs into clinical practice.
  arXiv  Detail & Related papers  (2024-10-24T15:41:56Z)
• The Role of Language Models in Modern Healthcare: A Comprehensive Review [2.048226951354646]
  The application of large language models (LLMs) in healthcare has gained significant attention. This review examines the trajectory of language models from their early stages to the current state-of-the-art LLMs.
  arXiv  Detail & Related papers  (2024-09-25T12:15:15Z)
• Clinical Insights: A Comprehensive Review of Language Models in Medicine [1.5020330976600738]
  This paper explores the advancements and applications of language models in healthcare, focusing on their clinical use cases.<n>It examines the evolution from early encoder-based systems requiring extensive fine-tuning to state-of-the-art large language and multimodal models capable of integrating text and visual data through in-context learning.<n>The analysis emphasizes locally deployable models, which enhance data privacy and operational autonomy, and their applications in tasks such as text generation, classification, information extraction, and conversational systems.
  arXiv  Detail & Related papers  (2024-08-21T15:59:33Z)
• A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directions [23.36640449085249]
  We trace the recent advances of Medical Large Language Models (Med-LLMs)<n>The wide-ranging applications of Med-LLMs are investigated across various healthcare domains.<n>We discuss the challenges associated with ensuring fairness, accountability, privacy, and robustness.
  arXiv  Detail & Related papers  (2024-06-06T03:15:13Z)
• Evaluating large language models in medical applications: a survey [1.5923327069574245]
  Large language models (LLMs) have emerged as powerful tools with transformative potential across numerous domains. evaluating the performance of LLMs in medical contexts presents unique challenges due to the complex and critical nature of medical information.
  arXiv  Detail & Related papers  (2024-05-13T05:08:33Z)
• Does Biomedical Training Lead to Better Medical Performance? [2.3814275542331385]
  Large Language Models (LLMs) are expected to significantly contribute to patient care, diagnostics, and administrative processes. This study investigates the effect of biomedical training in the context of six practical medical tasks evaluating $25$ models.
  arXiv  Detail & Related papers  (2024-04-05T12:51:37Z)
• AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator [69.51568871044454]
  We introduce textbfAI Hospital, a framework simulating dynamic medical interactions between emphDoctor as player and NPCs. This setup allows for realistic assessments of LLMs in clinical scenarios. We develop the Multi-View Medical Evaluation benchmark, utilizing high-quality Chinese medical records and NPCs.
  arXiv  Detail & Related papers  (2024-02-15T06:46:48Z)
• Privacy-preserving machine learning for healthcare: open challenges and future perspectives [72.43506759789861]
  We conduct a review of recent literature concerning Privacy-Preserving Machine Learning (PPML) for healthcare. We primarily focus on privacy-preserving training and inference-as-a-service. The aim of this review is to guide the development of private and efficient ML models in healthcare.
  arXiv  Detail & Related papers  (2023-03-27T19:20:51Z)
• Large Language Models for Healthcare Data Augmentation: An Example on Patient-Trial Matching [49.78442796596806]
  We propose an innovative privacy-aware data augmentation approach for patient-trial matching (LLM-PTM) Our experiments demonstrate a 7.32% average improvement in performance using the proposed LLM-PTM method, and the generalizability to new data is improved by 12.12%.
  arXiv  Detail & Related papers  (2023-03-24T03:14:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.