Sakai Takamasa
Drug Informatics, Faculty of Pharmacy, Meijo University.
Yakugaku Zasshi. 2023;143(6):491-495. doi: 10.1248/yakushi.22-00179-2.
Recent developments have enabled daily accumulated medical information to be converted into medical big data, and new evidence is expected to be created using databases and various open data sources. Database research using medical big data was actively conducted in the coronavirus disease 2019 (COVID-19) pandemic and created evidence for a new disease. Conversely, the new term "infodemic" has emerged and has become a social problem. Multiple posts on social networking services (SNS) overly stirred up safety concerns about the COVID-19 vaccines based on the analysis results of the Vaccine Adverse Event Reporting System (VAERS). Medical experts on SNS have attempted to correct these misunderstandings. Incidents where research papers about the COVID-19 treatment using medical big data were retracted due to the lack of reliability of the database also occurred. These topics of appropriate interpretation of results using spontaneous reporting databases and ensuring the reliability of databases are not new issues that emerged during the COVID-19 pandemic but issues that were present before. Thus, literacy regarding medical big data has become increasingly important. Research related to artificial intelligence (AI) is also progressing rapidly. Using medical big data is expected to accelerate AI development. However, as medical AI does not resolve all clinical setting problems, we also need to improve our medical AI literacy.
最近的发展使得每日积累的医学信息能够转化为医学大数据，并且有望利用数据库和各种开放数据源创造新的证据。在2019冠状病毒病（COVID-19）大流行期间，人们积极开展了利用医学大数据的数据库研究，并为一种新疾病创造了证据。相反，新术语“信息疫情”出现并成为一个社会问题。社交网络服务（SNS）上的多篇帖子基于疫苗不良事件报告系统（VAERS）的分析结果，过度引发了人们对COVID-19疫苗安全性的担忧。SNS上的医学专家试图纠正这些误解。还发生了一些事件，即由于数据库缺乏可靠性，关于使用医学大数据进行COVID-19治疗的研究论文被撤回。这些关于使用自发报告数据库对结果进行合理解释以及确保数据库可靠性的话题，并非COVID-19大流行期间出现的新问题，而是之前就存在的问题。因此，医学大数据素养变得越来越重要。与人工智能（AI）相关的研究也在迅速发展。利用医学大数据有望加速人工智能的发展。然而，由于医学人工智能并不能解决所有临床环境问题，我们还需要提高我们的医学人工智能素养。
