Ocular Oncology Service, Wills Eye Hospital, Thomas Jefferson University, Philadelphia, PA, USA.
Pediatric Emergency and Infectious Disease, Centre Hospitalier Universitaire Timone Enfants, Marseille, France.
Semin Ophthalmol. 2024 May;39(4):289-293. doi: 10.1080/08820538.2023.2300808. Epub 2024 Jan 5.
Large language models (LLMs) show great promise in assisting clinicians in general, and ophthalmology in particular, through knowledge synthesis, decision support, accelerating research, enhancing education, and improving patient interactions. Specifically, LLMs can rapidly summarize the latest literature to keep clinicians up-to-date. They can also analyze patient data to highlight crucial insights and recommend appropriate tests or referrals. LLMs can automate tedious research tasks like data cleaning and literature reviews. As AI tutors, LLMs can fill knowledge gaps and assess competency in trainees. As chatbots, they can provide empathetic, personalized responses to patient inquiries and improve satisfaction. The visual capabilities of LLMs like GPT-4 allow assisting the visually impaired by describing environments. However, there are significant ethical, technical, and legal challenges around the use of LLMs that should be addressed regarding privacy, fairness, robustness, attribution, and regulation. Ongoing oversight and refinement of models is critical to realize benefits while minimizing risks and upholding responsible AI principles. If carefully implemented, LLMs hold immense potential to push the boundaries of care, discovery, and quality of life for ophthalmology patients.
大型语言模型 (LLMs) 在辅助临床医生方面具有巨大的潜力，尤其是在眼科领域，可以通过知识综合、决策支持、加速研究、加强教育和改善医患互动来实现。具体来说，LLMs 可以快速总结最新文献，使临床医生能够及时了解最新信息。它们还可以分析患者数据，突出关键见解并推荐适当的检查或转诊。LLMs 可以自动化数据清理和文献综述等繁琐的研究任务。作为人工智能导师，LLMs 可以填补学员的知识空白并评估其能力。作为聊天机器人，它们可以为患者提供富有同情心的个性化回答，提高患者满意度。GPT-4 等 LLM 的视觉功能可以通过描述环境来帮助视障人士。然而，在使用 LLM 方面存在着重大的伦理、技术和法律挑战，需要解决隐私、公平性、稳健性、归因和监管等问题。对模型进行持续的监督和改进对于在最小化风险和遵守负责任的 AI 原则的同时实现效益至关重要。如果谨慎实施，LLMs 有望极大地推动眼科患者护理、发现和生活质量的发展。
