Medical Education: Considerations for a Successful Integration of Learning with and Learning about AI

医学教育:成功整合人工智能学习与人工智能相关学习的考量因素

阅读:1

Abstract

Artificial intelligence (AI) with its diverse domains such as expert systems and machine learning already has multiple potential applications in medicine. Based on the latest developments in the multifaceted field of AI, it will play a pivotal role in medicine, with a high transformative potential in multiple areas, including drug development, diagnostics, patient care and monitoring. In the pharmaceutical industry AI is also rapidly gaining a crucial role. The introduction of innovative medicines requires profound background knowledge and the latest means of communication. This drives us to intensively engage with the topic of medical education, which is becoming more and more demanding due to the dynamic knowledge landscape, among other things, accelerated even more by digitalization and AI. Therefore, we argue for the incorporation of AI-based tools and methods in medical education, including personalized learning, diagnostic pathways, and data analysis, to prepare healthcare professionals for the evolving landscape of AI in medicine and support the fluency in dealing with AI by regular contact with various AI-based tools (Learning with AI). Understanding AI's vast potential and its caveats as well as gaining a basic knowledge of how AI works should be an important part of medical education to ensure that physicians can effectively and responsibly leverage AI-based systems in their daily practice and in scientific communication (Learning about AI).

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。