眼科的基础模型:机遇和挑战
Mertcan Sevgi1,2,3, Eden Ruffell1,4,5,3, Fares Antaki1,2,6
1Institute of Ophthalmology, University College London.
Current opinion in ophthalmology
|September 27, 2024
概括
眼科的基础模型显示出前景,像RETFound这样的AI超越了传统方法. 由于数据和资源的局限性,在开发专门的多式联运模式方面仍然存在挑战.
科学领域:
- 眼科医生 眼科 眼科
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 基础模型的出现,如RETFound,标志着眼科医学的重要一步.
- 像GPT-4和Gemini这样的大型语言模型 (LLM) 的进步正在适应医疗应用.
- 可泛化医疗人工智能 (GMAI) 显示了适应新临床任务的潜力.
研究的目的:
- 审查在眼科中推进基础模型的机遇和挑战.
- 探索眼科大语言模型和多式模式模型的潜力.
- 确定眼科人工智能的当前局限性和未来方向.
主要方法:
- 审查眼科基础模型和大型语言模型的最新发展.
- 对眼科专用AI模型的性能指标的分析.
- 与LLM相比,大型多式模式 (LMM) 的功能评估.
主要成果:
- 在传统的深度学习模型中,RETFound表现出优越的性能,即使微调数据有限.
- 专门的LLM (Med-Gemini,Medprompt GPT-4) 在眼科任务中表现优于一般模型.
- 在眼科专用的多式联络模型中存在一个显著的差距,原因是高的计算成本和数据稀缺.
结论:
- 基础模型在眼科中提供了重要的机会,但高质量的标准化数据集对于培训和专业化至关重要.
- 虽然大型语言和视觉模型已经进步,但大型多式联络模型具有模仿临床专业知识的最大潜力.
- 解决数据限制和计算资源需求是释放眼科人工智能的全部潜力的关键.
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