1
课程详述
COURSE SPECIFICATION
以下课程信息可能根据实际授课需要或在课程检讨之后产生变动。如对课程有任何疑问，请
联系授课教师。
The course information as follows may be subject to change, either during the session because of unforeseen
circumstances, or following review of the course at the end of the session. Queries about the course should be
directed to the course instructor.
1.
课程名称 Course Title
医学人工智能导论 Introduction to Medical Artificial Intelligence
2.
授课院系
Originating Department
医学院 School of Medicine
3.
课程编号
Course Code
MED331
4.
课程学分 Credit Value
2
5.
课程类别
Course Type
专业基础课 Major Foundational Courses
6.
授课学期
Semester
秋季/Fall
7.
授课语言
Teaching Language
中英双语 English & Chinese
8.
授课教师、所属学系、联系方
式（如属团队授课，请列明其
他授课教师）
Instructor(s), Affiliation&
Contact
（For team teaching, please list
all instructors）
刘江，教授，计算机科学与工程系，liuj@sustech.edu.cn
Jiang Liu, Professor, Department of Computer Science and Engineering,
liuj@sustech.edu.cn
9.
实验员/助教、所属学系、联系
方式
Tutor/TA(s), Contact
10.
选课人数限额(可不填)
Maximum Enrolment
（Optional）
2
11.
讲授
Lectures
习题/辅导/讨论
Tutorials
实验/实习
Lab/Practical
其它(请具体注明)
Other（Please specify）
总学时
Total
32
32
12.
先修课程、其它学习要求
Pre-requisites or Other
Academic Requirements
NA
13.
后续课程、其它学习规划
Courses for which this course
is a pre-requisite
无 None
14.
其它要求修读本课程的学系
Cross-listing Dept.
无 None
教学大纲及教学日历 SYLLABUS
15.
教学目标 Course Objectives
This course provides an introduction to artificial intelligence in Medicine （AIM）. Topics talked will help students to
achieve the following 4 main objectives:
1) Highlight fundamental AI concepts and development milestones: introduce the AI development history, the
formation of some key AI concepts like Agent, Learning, Reasoning and key algorithms like Perceptron, SVM, CNN
algorithms, and importantly biological, psychological and medical foundations behind AI development. Teaching
medical students the inherited relationship between the AI and Medicine development.
2) Introduce the AI models, algorithms and platforms behind the medicine research in terms of CAD (Computer Aided
medical Diagnosis) for medical disease screening, CAI (Computer Aided Intervention) for medical operation
planning/intervention/evaluation.
3) Introduce the popular deep neural network ALPHAFOLD and various popular deep learning network structures and
development platform. Introduce data driven deep learning model and how it can be combined with the medicine big
data to assist medical science discovery.
4) Inspire student’s interest using AI for their medical career: In order to encourage students to engage AI in their
medical careers and study, various AI medical applications will be introduced and discussed. Students are asked to
work on AI medical application projects with the help from clinicians, group project presentation will be graded.
医学人工智能导论课程将介绍医学人工智能的基本概念及理论，将从以下 4 个方面进行展开：
1） 重点介绍人工智能的基本概念和发展里程碑：介绍人工智能的发展历史、智能体、学习、推理等一些关键人工智
能概念的形成，以及感知器、SVM、CNN 算法等关键算法。以及人工智能发展背后重要的生物学、心理学和医学基础。
教导医学生掌握人工智能与医学发展之间的内在传承关系。
2） 介绍医学研究背后的人工智能模型、算法和平台，包括 CAD（计算机辅助医疗诊断）和计算机辅助医学疾病筛
查、CAI（计算机辅助干预）和医疗手术规划/干预/评估。
3） 介绍流行的深度神经网络 ALPHAFOLD 和各种流行的深度学习网络结构和开发平台。介绍数据驱动的深度学习模
型，以及如何将其与医学大数据相结合，以帮助医学科学发现。
4） 激发学生在医疗职业业中使用人工智能的兴趣：为了鼓励学生在医疗事业和学习中使用人工智能，将介绍和讨论
各种人工智能医疗应用。学生们被要求在临床医生的帮助下从事人工智能医学应用项目，小组项目演示将被评分。
3
16.
预达学习成果 Learning Outcomes
On completion of the “Introduction to Artificial Intelligence in Medicine” module, students should be able to:
1) Understand the AI concepts and learn the AI development milestones. Understand the biological, psychological and
medical foundations behind AI development. Teaching medical students the inherited relationship between the AI
and Medicine development.
2) Understand AI CAD (Computer Aided medical Diagnosis) algorithms for medical disease screening and diagnosis,
AI CAI (Computer Aided Intervention) algorithms for medical operation planning/intervention/evaluation.
3) Know some popular deep neural network like ALPHAFOLD as well as some AI development platforms. Know how
AI can be combined with the medicine big data to assist medical science discovery.
4) Inspire students to think and explore further in engaging AI for real-world medical applications in the future career
and studies
完成“医学中的人工智能入门”模块后，学生应能够获得如下技能：
1） 理解人工智能概念，学习人工智能开发里程碑。了解人工智能开发背后的生物学、心理学和医学基础。使医学学生了
解人工智能与医学发展之间的内在关系。
2） 了解用于医疗疾病筛查和诊断的 AI CAD（计算机辅助医疗诊断）算法，以及用于医疗手术规划/干预/评估的 AI CAI
（计算机辅助干预）算法。
3） 了解一些流行的深层神经网络，比如 ALPHAFOLD，以及一些人工智能开发平台。了解人工智能如何与医学大数据相结
合，以帮助医学科学发现。
4） 鼓励学生在未来的职业和研究中进一步思考和探索人工智能在现实世界中的医学应用
17.
课程内容及教学日历 （如授课语言以英文为主，则课程内容介绍可以用英文；如团队教学或模块教学，教学日历须注明
主讲人）
Course Contents (in Parts/Chapters/Sections/Weeks. Please notify name of instructor for course section(s), if
this is a team teaching or module course.)
4
Week 1: Introduction of AI: Definition and Issues
Week 2: AI and Medicine
Week 3: AI Development Milestone
Week 4: Biology Neuron and Artificial Neuron
Week 5: Biological Hebb Rule and AI Learning
Week 6: Perceptron
Week 7: Perceptron Learning
Week 8: Mid-Term Examination
Week 9: Back Propagation Learning and Feed Forward Neural Networks
Week 10: Machine Learning
Week 11: Machine Learning SVM algorithm and AI Applications
Week 11: Deep Learning - Convolutional Neural Networks
Week 12: Deep Learning Algorithms and AI Application in Medicine
Week 14: Medical AI Applications and Student Project Presentations
Week 15: Medical AI Applications and Student Project Presentations
Week 16: Summary and Revision
第一周：人工智能介绍：定义和问题
第 2 周：人工智能和医学
第 3 周：人工智能发展里程碑
第 4 周：生物神经元和人工神经元
第 5 周：生物 Hebb 规则和人工智能学习
第 6 周：感知机
第 7 周：感知机学习
第 8 周：期中考试
第 9 周：反向传播学习和前馈神经网络
第 10 周：机器学习
5
第 11 周：机器学习 SVM 算法和人工智能应用
第 11 周：深度学习-卷积神经网络
第 12 周：深度学习算法和人工智能在医学中的应用
第 14 周：医学人工智能应用和学生项目演示
第 15 周：医学人工智能应用和学生项目演示
第 16 周：总结和修订
18.
教材及其它参考资料 Textbook and Supplementary Readings
1. Artificial Intelligence – A Modern Approach (AIMA) (Russell/Norvig)
Web site: http://aima.cs.berkeley.edu/
2. Neural Networks and Deep Learning 《神经网络与深度学习》邱锡鹏
Web site: https://nndl.github.io/
课程评估 ASSESSMENT
19.
评估形式
Type of
Assessment
评估时间
Time
占考试总成绩百分比
% of final
score
违纪处罚
Penalty
备注
Notes
出勤 Attendance
10
课堂表现
Class
Performance
小测验
Quiz
课程项目 Projects
40
平时作业
Assignments
期中考试
Mid-Term Test
20
期末考试
30
6
Final Exam
期末报告
Final
Presentation
其它（可根据需要
改写以上评估方
式）
Others (The
above may be
modified as
necessary)
20.
记分方式 GRADING SYSTEM
A. 十三级等级制 Letter Grading
B. 二级记分制（通过/不通过） Pass/Fail Grading
课程审批 REVIEW AND APPROVAL
21.
本课程设置已经过以下责任人/委员会审议通过
This Course has been approved by the following person or committee of authority