眼科人工智能研究发展存在的挑战及应对

Challenges and countermeasures in the development of artificial intelligence research in ophthalmology

人工智能技术的兴起给眼病诊疗带来了颠覆性的变革,为眼科影像诊断技术提供了全新的智能辅助诊断模式。然而,随着临床应用反馈和需求的不断变化,眼科人工智能研究面临着一些挑战,包括缺乏标准化的数据集和共享平台,算法模型单一,跨模态信息融合不足,以及缺乏临床可解释性等问题。为了满足眼科人工智能研究的新需求,我们需要努力建设眼科数据标准及共享平台,突破核心智能算法创新,并建立临床逻辑可解释的眼病筛查、诊断、预测模型。此外,与5G、虚拟现实、手术机器人等前沿技术的深度融合,也将推动我国眼科智能医学发展进入新的阶段。

The advent of artificial intelligence(AI)technology has led to revolutionary advancements in the diagnosis and treatment of ophthalmic diseases,introducing a novel AI-assisted diagnostic approach for ophthalmology that is rich in imaging diagnostic technologies.However,as clinical applications continue to evolve,AI research in ophthalmology faces challenges such as the lack of standardized datasets and innovative algorithm models,insufficient cross-modal information fusion,and limited clinical interpretability.In response to the growing demand for AI research in ophthalmology,it is essential to establish ophthalmic data standards and sharing platforms,innovate core algorithms,and develop clinical logic interpretable models for the screening,diagnosis,and prediction of eye diseases.Additionally,the deep integration of cutting-edge technologies such as 5G,virtual reality,and surgical robots would advance the development of ophthalmic intelligent medicine into a new phase.