Introduction to AI Healthcare and Medicine Seminar (2)

(1) Course Outline

Following the first semester’s Introduction to AI Healthcare and Medicine (1), the second semester’s Introduction to AI Healthcare and Medicine (2) will feature speakers from several global IT platformers and professors from Keio’s Faculty of Medicine and Faculty of Science and Technology, who will introduce actual examples and cutting-edge research in healthcare and AI medicine running on cutting-edge IT, With the development of IT Cloud, AI / Data Science technologies are now easily available. In addition, Dr. Sakurada, School of Medicine, Dr. Tetsuro Ishikawa, School of Medicine, and Dr. Mitsukura, School of Science and Technology, will give lectures on their research in the field of medical-engineering collaboration. Due to the availability of outside lecturers and other professors, some lectures will be recorded, but in the fall semester, we will hold face-to-face lectures. In the case of recorded lectures, I will provide a Q/A session to deepen your understanding of the content. Please note that the order of the lecturers may change due to their availability. 

(2) Content of each session

Basically, each session will provide an opportunity to talk in groups.

Session 1: Mr. Toyama, et al, Amazon Web Service, “Trends in AI Health and Medical Data Science”　11/6

AWS, a cloud computing business, provides a wide range of services, from AI services that allow users to easily use fine-tuned machine learning models via APIs, to ML services that support the process of developing, learning, and deploying machine learning, and to virtual The company also provides AI/ML services tailored to various user cases, such as the provision of virtual instances that support the main frameworks of deep learning models. In addition, the company is also developing AI services specialized for healthcare and medical domains, and these services are actively used globally.

Session 2: Mr. Kudo, et al. IBM Japan, Ltd.  11/13

IBM, which has the longest history in the IT world, has been working on AI/Data Science from the early stage, as represented by “Watson”. In this lecture, we will introduce the application of AI to the medical and healthcare fields, which have been the focus of much attention, from the perspectives of basic research laboratories, IT platforms, and practical applications.

Session 3: Tetsuro Ishikawa, Associate Professor, Ishii/Ishibashi Memorial Chair in Augmented Intelligent Medicine, School of Medicine　12/4

Visualization to decipher people’s curiosity and infodemics

The informatization of the world, which has facilitated the accumulation and diffusion of vast amounts of healthcare- and medical-related information and connectivity, also has a negative aspect. In contrast, the spread of misinformation and fake news in the information space (infodemics) is its digital twin. The fusion of big data spreading through social media and AI has created a multi-layered way of perceiving the state of the world. As a concrete example, we will introduce a visualization that can predict infectious disease epidemics from people’s information search logs and read the transition of people’s curiosity and the diversity of symptoms of the coronary disaster. 

Session 4: Mr. Shimizu, et al, Microsoft Japan Co.  12/11

In addition to the explosive growth of medical and health information, expectations for cloud computing, AI, and mixed reality are increasing in the medical field due to technological innovation in ICT. On the other hand, AI ethics and compliance are also becoming very important because of the demand for data utilization. In this lecture, we will introduce the global research on AI and other advanced technologies and their application in healthcare, as well as the domestic and international examples of digital innovation and AI utilization in healthcare that Microsoft is working on.

Session 5: Kazuhiro Sakurada, Professor, Ishii/Ishibashi Memorial Chair (Augmented Intelligent Medicine), Faculty of Medicine 12/18

In the field of health care, prognostic predictors for different diseases have been developed using machine learning and deep learning. This technology is expected to realize personalized preventive medicine. In addition, based on the progress of generative AI represented by ChatGPT, efforts have begun in the biomedical science field to develop AI that automatically generates prognostic predictions and treatment policies for patients by constructing a general-purpose basic model. On the other hand, the integration of health and medical data, which is key to AI research, has not progressed from the perspective of protecting personal information. This problem has been opened up to be solved by the recently developed technologies of federative learning and SWARM learning. This lecture will provide an overview of the latest status of AI medical data science.

Session 6: Yasue Mitsukura, Professor, Faculty of Science and Engineering, “The Possibility of Future Medicine Changed by AI”  12/25

With the constant progress of AI algorithms such as ChatGPT, human jobs are being replaced by AI. AI is also playing a dizzying role in the field of medicine. However, there are things that AI cannot do; I would like to introduce what AI can do and what humans can do, and vice versa, with actual examples.