笔者从长期回顾的角度阐述了对人工智能（AI）/数据科学与生物医学之间关系的看法。随着新技术的不断出现，现代生物医学的发展持续加速。由于所有生命系统基本上都受其自身DNA中信息的支配，因此信息科学对生物医学的研究具有特别重要的意义。与物理学不同，在生物学中没有发现（或很少有）主导定律。因此，在生物学中，“数据到知识”方法很重要。人工智能在历史上一直应用于生物医学，最近的新闻表明，基于人工智能的方法在国际蛋白质结构预测竞争中获得了最佳性能，这可能被视为该领域的另一个里程碑。类似的方法可能有助于解决基因组序列解释中的问题，如确定患者基因组中的癌症驱动突变。最近，新一代测序（NGS）的爆炸性发展已产生大量数据，并且这种趋势将加速。NGS不仅用于“读取”DNA序列，而且还用于在单细胞水平上获得各种类型的信息。这些数据可以视为气候模拟中的网格数据点。数据科学和人工智能对于这些数据的综合解释/模拟都将变得至关重要，并将在未来的精密医学中起主导作用。

In this commentary, I explain my perspective on the relationship between artificial intelligence (AI)/data science and biomedicine from a long-range retrospective view. The development of modern biomedicine has always been accelerated by the repeated emergence of new technologies. Since all life systems are basically governed by the information in their own DNA, information science has special importance for the study of biomedicine. Unlike in physics, no (or very few) leading laws have been found in biology. Thus, in biology, the ″data-to-knowledge″ approach is important. AI has historically been applied to biomedicine, and the recent news that an AI-based approach achieved the best performance in an international competition of protein structure prediction may be regarded as another landmark in the field. Similar approaches could contribute to solving problems in genome sequence interpretation, such as identifying cancer-driving mutations in the genome of patients. Recently, the explosive development of next-generation sequencing (NGS) has been producing massive data, and this trend will accelerate. NGS is not only used for ″reading″ DNA sequences, but also for obtaining various types of information at the single-cell level. These data can be regarded as grid data points in climate simulation. Both data science and AI will become essential for the integrative interpretation/simulation of these data, and will take a leading role in future precision medicine.