Lin Chihung, Kuo Chang-Fu
Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan.
Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan; Division of Rheumatology, Allergy, and Immunology, Chang Gung Memorial Hospital, Taoyuan, Taiwan; Division of Rheumatology, Orthopaedics and Dermatology, School of Medicine, University of Nottingham, Nottingham, UK.
Biomed J. 2025 Apr 29:100868. doi: 10.1016/j.bj.2025.100868.
Large Language Models (LLMs) are capable of transforming healthcare by demonstrating remarkable capabilities in language understanding and generation. They have matched or surpassed human performance in standardized medical examinations and assisted in diagnostics across specialties like dermatology, radiology, and ophthalmology. LLMs can enhance patient education by providing accurate, readable, and empathetic responses, and they can streamline clinical workflows through efficient information extraction from unstructured data such as clinical notes. Integrating LLM into clinical practice involves user interface design, clinician training, and effective collaboration between Artificial Intelligence (AI) systems and healthcare professionals. Users must possess a solid understanding of generative AI and domain knowledge to assess the generated content critically. Ethical considerations to ensure patient privacy, data security, mitigating biases, and maintaining transparency are critical for responsible deployment. Future directions for LLMs in healthcare include interdisciplinary collaboration, developing new benchmarks that incorporate safety and ethical measures, advancing multimodal LLMs that integrate text and imaging data, creating LLM-based medical agents capable of complex decision-making, addressing underrepresented specialties like rare diseases, and integrating LLMs with robotic systems to enhance precision in procedures. Emphasizing patient safety, ethical integrity, and human-centered implementation is essential for maximizing the benefits of LLMs, while mitigating potential risks, thereby helping to ensure that these AI tools enhance rather than replace human expertise and compassion in healthcare.
大语言模型（LLMs）能够通过在语言理解和生成方面展现出卓越能力来变革医疗保健领域。它们在标准化医学考试中已达到或超越人类表现，并在皮肤科、放射科和眼科等多个专科的诊断中提供协助。大语言模型可以通过提供准确、易懂且富有同理心的回复来加强患者教育，还能通过从临床记录等非结构化数据中高效提取信息来简化临床工作流程。将大语言模型集成到临床实践中涉及用户界面设计、临床医生培训以及人工智能（AI）系统与医疗保健专业人员之间的有效协作。用户必须对生成式人工智能和领域知识有扎实的理解，以便批判性地评估生成的内容。确保患者隐私、数据安全、减轻偏差并保持透明度的伦理考量对于负责任的部署至关重要。大语言模型在医疗保健领域的未来发展方向包括跨学科合作、制定纳入安全和伦理措施的新基准、推进整合文本和影像数据的多模态大语言模型、创建能够进行复杂决策的基于大语言模型的医疗智能体、解决罕见病等代表性不足的专科问题，以及将大语言模型与机器人系统集成以提高手术精度。强调患者安全、伦理诚信和以人为本的实施对于最大化大语言模型的益处至关重要，同时减轻潜在风险，从而有助于确保这些人工智能工具在医疗保健中增强而非取代人类专业知识和同情心。
