Doctoral Course (4 years)
Students will learn broad knowledge about AI, the current status of medical AI techniques, including legal and ethical issues and pharmaceutical approval, to be able to develop AI models needed by society.
The course is targeted for
Graduate students who are enrolled in Doctoral Program at Hokkaido University Graduate School of Medicine
The images of students to be cultivated
Leadership of medical professionals who have knowledge of “clinical medicine,” “data science,” and “law and ethics” is essential in medical AI R&D teams. Based on the knowledge of the latest clinical medicine related to genomic medicine, diagnosis and treatment support, drug development, nursing care and dementia, and surgical support, with a focus on diagnostic imaging support, students will systematically acquire knowledge of relevant AI technologies, law and ethics. By ding so, the course will develop human resources who are capable of developing medical AI needed by society promptly.
Curriculum
Medical AI Core Subjects（Programs common to all 3 universities）
Medical AI Advanced Theory I (2 credits)
Medical AI Advanced Theory II (2 credits)
Medical AI Seminar (2 credits)
Medical AI Advanced Theory Subjects
Advanced Theory on Diagnostic Imaging (2 credits)
General Theory on Diagnostic Imaging for AI Research and Development
Basics of Medical Imaging Principles１
Basics of Medical Imaging Principles２
Medical Imaging Technology
Data Structure and Handling of Medical Images
Medical Imaging Software
Quantitative Analysis of Medical Imaging
Diagnostic Imaging and Pathology
Remote Image Diagnostics
Research of Diagnostic Imaging AI in Practice１
Research of Diagnostic Imaging AI in Practice２
Research of Diagnostic Imaging AI in Practice３
Research of Diagnostic Imaging AI in Practice４
Challenges of Diagnositic Imaging in AI Research and Development
Advanced Theory on Machine Learning (2 credits)
History and Current State of Machine Learning
Types and Principles of Machine Learning
Applications of Machine Learning
Image Classification by Machine Learning
Anomaly Detection by Machine Learning
Image Segmentation
Image Generation and Quality Improvement
Video Processing
Natural Language Processing (NPL)
Drug Development and Machine Learning
Real-world Machine Learning Applications and Challenges１
Real-world Machine Learning Applications and Challenges２
Real-world Machine Learning Applications and Challenges３
Real-world Machine Learning Applications and Challenges４
Challenges in Application of Real-World Solutions for Deep Learning
Data Handling Seminars (2 credits)
Medical Imaging Method（hands-on seminar）
Deep Learning Framework（hands-on seminar）
Building and Improving the Accuracy of Deep Learning（hands-on seminar）
Data-based Prognostics（hands-on seminar）
Problem Analysis and Solution Development（hands-on seminar）
Project Management Techniques and Practices（hands-on seminar）
Advanced Theory on Genome Medicine (2 credits)
General Theory on Genome Medicine
Medical AI in Genome Medicine
Cancer Genomics and Medical AI１
Cancer Genomics and Medical AI２
Ethical, Legal, and Social Issues in Genomic Medicine
Cancer Genomics
Challenges of Genome Medicine
Applications, Challenges, and Future of Medical AI in Genome Medicine
Advanced Theory on Diagnosis and Treatment Support (2 credits)
General Theory on Diagnosis and Treatment Support with Medical AI
Clinical Positioning and Usage Patterns of Medical AI
Diagnosis Support with Medical AI１
Diagnosis Support with Medical AI２
Treatment Support with Medical AI１
Treatment Support with Medical AI２
AI-based Caregiving Support
AI-based Disability Assistance
AI-based Reginal Medical Care Support
Ethics, Responsibilities and Challenges in the Operation of Medical AI Systems
Advanced Theory on Surgical Support (2 credits)
General Theory on AI-baed Surgical Support
Clinical Positioning and Usage Patterns of Surgical Robots and AI
Da Vinci Surgery in Practice１
Da Vinci Surgery in Practice２
Da Vinci Surgery in Practice３
Initiatives for AI Surgery 1
Initiatives for AI Surgery 2
Initiatives for AI Surgery 3
Initiatives for AI Surgery 4
Initiatives for AI Surgery 5
Challenges and Solutions for AI Surgery and Surgical Robots 1
Challenges and Solutions for AI Surgery and Surgical Robots２
Challenges and Solutions for AI Surgery and Surgical Robots３
Course Completion Requirements
Students shall be enrolled in doctoral program for minimum of 4 years (this does not apply for those who meet the requirements for short-term graduation) and shall select one of the 3 doctoral courses offered/specified by the institute (Basic Medicine Course, Clinical Medicine Course, Social Medicine Course). In addition to the required and elective courses specified by each course, students shall select and obtain minumun of 6 credits from the Medical AI Core subjects and 6 credits from the Medical AI Advanced Theory Subjects. Each course is offered as a common university course, and the credits earned through this program can be applied to a portion of the elective courses for doctoral credits .
Intensive Course (1 year)
Students will intensively learn equivalent knowledge as for doctoral course in a year. It is not necessary to be a graduate student in Hokkaido University to take Intensive course.
The course is targeted for
Graduate students, full-time workers (those with bachelor’s degree, and those with knowledge in the field of health science)
*Those who wish to enroll in the program, and do not qualify with criteira assigned above shall contact secretariat of CLAP by e-mail (med_ai@pop.med.hokudai.ac.jp).
The images of students to be cultivated
In the development of medical AI, collaboration among medical professionals, data scientists, and computer programmers is crucial. We intend to cultivate students who have a good comprehension of medical AI development in wide-ranging fields without being restricted to their own specialization, and who are able to promote medical AI development from their respective standpoints.
Course Completion Requirements
Students shall select 3 courses from the Medical AI Core subjects (Medical AI Advanced Theory I, Medical AI Advanced Theory II and Medical AI Seminar) and the Medical AI Advanced Theory Subjects. Although it is not mandatory, students are strongly encouraged to take courses from the Medical AI Core subjects. Each course is offered as a common university course.