人工智能(artificial intelligence，AI)在白内障手术中的应用越来越广泛，二者结合对于白内障手术的术前诊断和分级管理、术中人工晶状体选择、位置预测及术后管理(视力预测、并发症预测及随访)、手术培训和教学方面均起到巨大的促进作用。诚然，AI在与白内障手术相关的管理、分析和研究中还面临着许多问题，但其广泛的应用前景不可忽视。现对AI在白内障手术治疗和教学中的应用做以总结，并对其未来的发展做出展望。
Artificial intelligence (AI) has been widely used in cataract surgery. The combination of the two can play a great role in improving preoperative diagnosis, grading management of cataract surgery, intraoperative intraocular lens selection and location prediction, postoperative management (vision prediction, complication prediction and follow-up), surgical training and teaching. It is true that AI still faces many problems in the management, analysis and research related to cataract surgery, but its broad application prospects cannot be ignored. This review summarizes the application of AI in cataract surgery and teaching, and the future prospects of AI.
目的：分析医学人工智能通识课程“眼科人工智能的研发与应用”的开展效果，为相关医学人工智能通识课程的开展提供参考和借鉴。方法：纵向观察性研究。观察分析2020年秋季学期眼科人工智能的研发与应用通识课程学生人群，课程考核结果以及学生对课程的整体评价。结果：共有118名本科生同学参与了课程学习。其中大部分为低年级临床医学专业本科生。期中考核得分为77.21±10.07，有56位同学(47.46%)达到80分以上。期末考核得分为82.24±6.77，有91位同学(77.12%)达到80分以上。同学对课程的评分为98.76±3.55，超过90%的同学表示课程备课认真、授课条理清晰、表达准确。结论：本课程的顺利进展证明医学人工智能联合教学模式的可行性，理论和实践穿插的教学设置帮助同学们更好地掌握知识技术，完成教学目标。
Objective: To analyze the effectiveness of medical education curriculum named “Development and Application of Ophthalmic Artificial Intelligence”, and provide reference for the development of other related curriculums. Methods: Longitudinal observational study method was adopted. During the fall semester of 2020, we conducted an education curriculum named “Development and Application of Ophthalmic Artificial Intelligence” and analyzed the results of mid-term and final examinations, and curriculum evaluation of students. Results: There were 118 undergraduate students taking the course and most of them were junior students majoring in clinical medicine. The score of the mid-term examination was in the range of 77.2±10.07, and 56 students (47.46%) got more than 80 points. The score of the final examination was in the range of 82.24±6.77, and 91 students (77.12%) got more than 80 points. The score of course evaluation of students was in the range of 98.76±3.55, and more than 90% of the students thought that teachers have made full preparations before class, together with clear teaching logic and accurate expressions in class. Conclusion: The smooth progress of our course proved the feasibility of medical artificial intelligence teaching. The teaching setting interspersed with theory and practice could help students to master knowledge and technology better, so as to achieve the teaching objectives.
