Induced differentiation is one of the most experience- and skill-dependent experimental processes in regenerative medicine, and establishing optimal conditions often takes years. We developed a robotic AI system with a batch Bayesian optimization algorithm that autonomously induces the differentiation of induced pluripotent stem cell-derived retinal pigment epithelial (iPSC-RPE) cells. The system performed 216 forty-day cell culture experiments, with a total experimentation time of 8,640 days. From 200 million possible parameter combinations, the system performed cell culture in 143 different conditions in 111 days, resulting in 88% better iPSC-RPE production than that by the pre-optimized culture in terms of pigmented scores. Our work demonstrates that the use of autonomous robotic AI systems drastically accelerates systematic and unbiased exploration of experimental search space, suggesting immense use in medicine and research.

中文翻译：

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在再生医学中寻找最佳细胞培养的机器人

诱导分化是再生医学中最依赖经验和技能的实验过程之一，建立最佳条件通常需要数年时间。我们开发了一个具有批量贝叶斯优化算法的机器人 AI 系统，该算法自动诱导诱导多能干细胞衍生的视网膜色素上皮 (iPSC-RPE) 细胞的分化。该系统进行了 216 次为期 40 天的细胞培养实验，总实验时间为 8640 天。从 2 亿个可能的参数组合中，该系统在 111 天内在 143 种不同条件下进行了细胞培养，在色素评分方面，iPSC-RPE 的产量比预先优化的培养高 88%。