Who / When / Where
Instructor: Zhiyu Wan
Teaching Assistant: Sihan Xie, Hongzhu Jiang
Semester: Fall 2025
Time: Wednesdays & Fridays (Odd Week) , 15:00-15:45, 15:55-16:40
Location: School of Life Science and Technology Room A103
Office Hours: Upon request, Location: BME Building, Room 228
Course Syllabus (PDF)
First Day of Class: September 17, 2025
Description
” Medical Data Privacy and Ethics in the Age of Artificial Intelligence ” is a specialized elective course for graduate students majoring in Biomedical Engineering and a foundational public course for Master of Engineering students in Biomedical Engineering. This course focuses on issues related to medical data privacy and ethics in the age of artificial intelligence (AI), with an in-depth exploration of privacy protection technologies, ethical dilemmas, and legal regulations surrounding medical data. The curriculum covers ethical challenges and privacy protection strategies that may arise during the collection, sharing, processing, and use of medical data. Through lectures, discussions, case studies, and project-based practice, this course aims to develop students’ capabilities in designing intelligent medical systems and managing biomedical data. It helps students understand how to balance privacy protection with technological innovation in AI-driven healthcare data systems, and provides a solid foundation in ethics, law, and technical practices for their future work in medical data processing.
The course comprises four fundamental modules: the first part introduces basic concepts and major challenges of data privacy and ethics in the age of artificial intelligence; the second part discusses social and legal approaches to protecting data privacy and ethics; the third part covers technical methods for privacy and ethics protection; and the fourth part applies these privacy and ethics protection methods to specific cases.
Learning Goal
1. Understand the core concepts, main challenges, and technical solutions related to medical data privacy and ethics in the age of artificial intelligence.
2. Explore ethical issues in medical data processing with AI, such as algorithmic bias, transparency, and informed consent.
3. Learn relevant domestic and international laws and regulations, such as HIPAA and GDPR, regarding medical data privacy protection.
4. Master common and advanced techniques for medical data privacy protection, including differential privacy, encryption methods, and de-identification techniques.
5. Develop critical thinking on data privacy and ethics in intelligent medicine and enhance practical skills for addressing privacy and ethical issues to support the design and implementation of future AI healthcare systems.
Instructional Pedagogy
This course is primarily lecture-based, with group discussions and literature reviews to enhance students’ understanding of theory, and case studies and project-based practice to strengthen their ability to apply theory and techniques. It aims to improve students’ skills in solving complex data privacy and ethical issues in real-world scenarios. A combination of lectures, in-class quizzes, reading summaries, homework assignments, case discussions, special topic seminars, and course research projects is employed, emphasizing an understanding-based, heuristic teaching approach aligned with international standards. This approach encourages students’ active learning and research interest. Additionally, teaching can be supplemented with MOOCs, guest lectures, and other methods. The primary spoken language is Chinese, while written materials are primarily in English.
Prerequisites
Required: There is no programming language requirement, but students should be able to design and write basic software applications.
Recommended: When appropriate, relevant methodology will be reviewed in class, but you should be comfortable learning about basic statistics, data structures, and algorithms. Prior experience with security principles is NOT a prerequisite for this course. Prior experience with Python programming is preferred.
Grading
This course does not have a final exam; instead, it primarily assesses students’ grasp of the content through a team project-based approach. Each team, composed of 1-4 students, will choose a project from a list of topics provided by the course instructor. They will conduct a literature review, investigate existing solutions, identify and summarize remaining challenges, and propose their own solutions. The teams will then seek relevant datasets to conduct experimental validation. Each team is required to have data-based experimental results, deliver an oral presentation, and submit a project report in the form of a paper written in English. With prior notice and approval from the course instructor, students may also select a topic outside the provided list for topics. The schedule for the course project is as follows:
| Week | Course Project Planning |
| 9 | Announce course project topics, and students begin forming teams. |
| 10 | Students complete team formation and begin selecting course project topics. |
| 12 | Each team completes project topic selection and begins preparing a preliminary project proposal. |
| 13 | Each team presents a project proposal for 5 minutes in class and submits a 4-page preliminary project proposal. |
| 14 | The instructor and teaching assistants conduct a mid-term evaluation of each team’s preliminary project proposal and provide feedback and guidance to each team. Students then prepare their final course project based on the feedback. |
| 16 | Each team gives an in-class oral presentation of approximately 10 minutes (timing adjusted based on the total number of teams, with the presentation order not disclosed in advance. Note: all teams are required to submit PPT before the first presentation). The instructor, invited experts, and teaching assistants, will provide fair and impartial ranking and scoring for each team’s presentation. |
| 17 | Students submit the final course project report (an updated 10-page project report. Note: co-authors are listed without ranking, but each co-author’s responsibilities and contributions must be specified in the report). |
| 18 | The instructor and teaching assistants conduct a final evaluation of the course project reports. |
The grading breakdown for the course project is as follows: mid-term evaluation (20%) + oral presentation performance (40%) + project report (40%). The grading for the preliminary project proposal and the project report is assisted by the teaching assistant and is finally determined by the instructor. The score for the oral presentation performance is obtained through a weighted average from the instructor (50%), invited experts (25%), and teaching assistants (25%).
The overall assessment breakdown is shown in the table below:
| 1 | Course Project | 40% |
| 2 | Homework Assignments | 30% |
| 3 | Reading Summaries | 10% |
| 4 | In-class quizzes | 15% |
| 5 | Attendances and Classroom Performance | 5% |
| Total | 100% |
Tests and submitted assignments are primarily in English. There are two homework assignments, which include Q&A, calculation, and programming tasks, etc. Collaboration is not allowed. Use of generative AI tools such as ChatGPT or DeepSeek need to be disclosed and documented the versions. Each homework assignment carries equal weight. For 10 weeks from week 2 to week 15, there is one required reading per week. Students are required to write summaries of the readings, which contribute 10% to the overall grade as an average score. In-class quizzes (closed-book) are given three times, in weeks 5, 7 and 9, covering material from weeks 1-4, 5-6, and 7-8 respectively. Each quiz carries equal weight. Grades and comments for homework, pre-class reading summaries, and quizzes are provided within two weeks.
Attendance is randomly checked by selecting students to answer questions in class; if a student is absent without prior notice, 1% is deducted from their overall grade, up to a maximum of 5%. Students who have excused absences may have no attendance points deducted as appropriate. Students who do not actively participate in class will be deducted 1% of their overall grade.
Each student has a one-week grace period in total for all their submissions (including readings, homework, project reports, etc.). For each additional day of delay, approximately one point (i.e., 1% of the overall grade) will be deducted from the submission. Readings and homework may have bonus points or questions.
Required Reading Assignments
Reading assignments will be selected from textbooks and various periodicals. Students will be required to read and submit brief summaries of assigned readings. Your summaries should be no longer than 2 pages in length. Readings will be made available online or as in-class handouts at least one lecture before they are due. Your summaries will be graded on a {A-minus, A, A-plus} scale.
- A- (or 0.5 point): You skimmed the assigned reading and barely understood, or summarized, its meaning and implications.
- A (or 1 point): You demonstrated that you read the material by providing a reasonable account of its contents, its strengths, and weaknesses.
- A+ (or 1.5 points): You provided a critical assessment of the reading and show insight regarding the reading’s topic.
These summaries constitute a total of 10% of your final grade. An average score of A (i.e., 1 points) will provide the student with the full 10%. An average score greater than A (i.e., greater than 1 point) will entitle the student to “extra” credit, with a maximum of 5 additional percentage points on their final grade.
You must email your summaries to the instructor and TAs before the beginning of class.
Requirements: The summary should include three parts. The first part is a summary of the paper (Basically the abstract but in your own words. Do not copy and paste the original abstract if it has one. Imagine you are required to write an abstract for the paper.) The second part is a list of the pros of the paper (i.e., the advantages and/or the contributions of the paper). It should include at least two bullets. The third part is a list of cons of the paper (i.e., the drawbacks and/or the limitations of the paper). The pros and cons can include those points that have been mentioned by the authors in the paper. However, points that have not been mentioned by the authors in the paper are encouraged and preferred. Always check your English grammar mistakes and/or typos before your submissions.
Specific Reading assignments: see the Schedule.
Textbooks & Recommended Reading
(1) Textbook
Recommended Textbook 1：
| Book Name: 信息科学技术伦理与道德 | Authors: 杨丽凤 虞晶怡 | Translator: | ISBN: 9780128163399 |
| Publisher: 机械工业出版社 | Publication Month: 2023.11 | Version: 1 |
Recommended Textbook 1：
| *Book Name: 工程伦理 | * Authors: 李正风、王前、丛杭青 | Translator: | *ISBN: 9787302524670 |
| * Publisher: 清华大学出版社 | *Publication Month: 2019.06 | * Version: 2 |
(2) Recommended Reading
Recommended Reading 1:
| Book Name: Ethics of Medical AI | Author: Giovanni Rubeis | Translator: | ISBN: 9783031557439 |
| Publisher: Springer | Publication Month: 2024.03 | Version: v2024 |
Recommended Reading 2:
| Book Name: Guide to the De-Identification of Personal Health Information | Author: Khaled El Emam | Translator: | ISBN: 9780429100659 |
| Publisher: Auerbach Publications | Publication Month: 2013.05 | Version: 1 |
Recommended Reading 3:
| *Book Name: Medical Data Privacy Handbook | *Author: Aris Gkoulalas-Divanis, Grigorios Loukides | Translator: | *ISBN: 9783319236322 |
| *Publisher: Springer | *Publication Month: 2015.12 | *Version: 1 |
Recommended Reading 4:
| Book Name: Responsible Genomic Data Sharing: Challenges and Approaches | Authors: Xiaoqian Jiang, Haixu Tang | Translator: | ISBN: 9787111727064 |
| Publisher: Academic Press | Publication Month: 2020.03 | Version: 1 |
Recommended Reading 5 (optional):
| Book Name: Ethics and Data Science | Authors: Mike Loukides, Hilary Mason, DJ Patil | Translator: | ISBN: 9781492043881 |
| Publisher: O’Reilly Media, Inc. | Publication Month: 2018.07 | Version: 1 |
Zhiyu Wan 