Over the past two decades, exponential growth in data availability, computational power, and newly available modeling techniques has led to an expansion in interest, investment, and research in Artificial Intelligence (AI) applications. Ophthalmology is one of many fields that seek to benefit from AI given the advent of telemedicine screening programs and the use of ancillary imaging. However, before AI can be widely deployed, further work must be done to avoid the pitfalls within the AI lifecycle. This review article breaks down the AI lifecycle into seven steps: data collection; defining the model task; data pre-processing and labeling; model development; model evaluation and validation; deployment; and finally, post-deployment evaluation, monitoring, and system recalibration and delves into the risks for harm at each step and strategies for mitigating them.

翻译：过去二十年，数据可用性、计算能力及新兴建模技术的指数级增长，推动了人工智能应用领域的兴趣、投资与研究扩张。随着远程医疗筛查项目的普及及辅助影像技术的应用，眼科成为众多希望受益于人工智能的领域之一。然而，在人工智能得到广泛部署之前，仍需进一步研究以避免人工智能生命周期中的陷阱。本综述将人工智能生命周期分解为七个步骤：数据收集、定义模型任务、数据预处理与标注、模型开发、模型评估与验证、部署，以及最终部署后的评估、监测与系统校准，并深入探讨了每个步骤中的潜在危害风险及其缓解策略。