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CCAI6001 Artificial Intelligence
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[This is a 3-credit Common Core Microcredentials course focused on transdisciplinary project-based learning in a highly compressed format.]
- CCMCs are optional, i.e. whether or not you take CCMCs (in place of one standard 6-credit CC course) for fulfilling the UG5(c) Common Core requirements, is at your discretion. However, if you opt to take CCMCs, you must take TWO of them, not more or less.
- Students who have been granted Advanced Standing / Credit Transfer / Course Exemption / Internal Transfer for Common Core courses in their current programme are NOT eligible.
- For students who have successfully completed two 3-credit CCMCs in place of one 6-credit Common Core course, the average grade point of the two CCMCs will be treated as the grade point of a 6-credit Common Core course for calculation of Graduation GPA under the Common Core Special Proviso.
Course Description
“AI Foundations in Healthcare” is an innovative summer course designed to introduce undergraduate students to the transformative role of artificial intelligence in modern medicine. Bringing together students from diverse disciplines, the course explores how AI technologies are reshaping clinical decision-making, drug discovery, and personalized care. Through a blend of engaging lectures, hands-on coding workshops with real healthcare datasets, and interdisciplinary teamwork, students gain practical experience in developing AI-driven solutions to pressing healthcare challenges. Key topics include ethical and regulatory considerations, data privacy, machine learning fundamentals, and the integration of large language models in clinical workflows. The curriculum is enriched by case studies, debates on contemporary issues such as algorithmic bias, and a field visit to local research centers to observe cutting-edge medical chatbots and surgical robots. This course empowers students to critically evaluate, ethically design, and creatively implement AI technologies, equipping them to contribute meaningfully to the future of healthcare innovation.
[A site visit will be arranged during the course.]
Course Learning Outcomes
On completing the course, students will be able to:
- Explain how machine learning works and what is AI’s potential and limitations.
- Use relevant information about AI and machine learning to address healthcare and medical problems.
- Demonstrate that leveraging multimodal clinical data can inform smart clinical decision making.
- Apply new understanding to adapt to emerging AI’s application in healthcare.
- Analyse in order to ethically use AI in clinical and healthcare practice.
Offer Semester and Day of Teaching
Summer Semester
Lecture
9:00 am – 10:50 am on August 3, 4, 5, 6, 7
Tutorial
1:00 pm – 2:50 pm August 3, 4, 5, 6, 7
Study Load
| Activities | Number of hours |
| Lectures | 10 |
| Tutorials | 10 |
| Fieldwork / Visits | 5 |
| Reading / Self-study | 10 |
| Individual assessment task preparation | 5 |
| Assessment: Group project | 20 |
| Assessment: Presentation (incl preparation) | 5 |
| Assessment: Writing assignments | 5 |
| Total: | 70 |
Assessment: 100% coursework
| Assessment Tasks | Weighting |
| In-class worksheets | 50 |
| Group project report | 30 |
| Group project presentation | 20 |
Required Reading
- Catacutan, D. B., Alexander, J., Arnold, A., & Stokes, J. M. (2024, August). Machine learning in preclinical drug discovery. Nature Chemical Biology, 20(8), 960-73.
- Huang, K., Chandak, P., Wang, Q., Havaldar, S., Vaid, A., Leskovec, J., Nadkarni, G. N., Glicksberg, B. S., Gehlenborg, N., & Zitnik, M. (2024, December). A foundation model for clinician-centered drug repurposing. Nature Medicine, 30(12), 3601-13.
- Kuenzi, B. M., Park, J., Fong, S. H., Sanchez, K. S., Lee, J., Kreisberg, J.F., Ma, J., & Ideker, T. (2020, November 9). Predicting drug response and synergy using a deep learning model of human cancer cells. Cancer Cell, 38(5), 672-84.
- Wang, H., Fu, T., Du, Y., Gao, W., Huang, K., Liu, Z., Chandak, P., Liu, S., Van Katwyk, P., Deac, A., & Anandkumar, A. (2023, August 3). Scientific discovery in the age of artificial intelligence. Nature, 620(7972), 47-60.
- Wang, X., Zhao, J., Marostica, E., Yuan, W., Jin, J., Zhang, J., Li, R., Tang, H., Wang, K., Li, Y, & Wang, F. (2024, October 24). A pathology foundation model for cancer diagnosis and prognosis prediction. Nature, 634(8035),970-8.
- Yao, Y., Chen, Y. F., & Zhang, Q. (2024, November). Optimized patient-specific immune checkpoint inhibitor therapies for cancer treatment based on tumor immune microenvironment modeling. Briefings in Bioinformatics, 25(6), bbae547.
Course Co-ordinator and Teacher(s)
| Course Co-ordinator | Contact |
| Professor Q. Zhang Department of Pharmacology and Pharmacy, LKS Faculty of Medicine |
Tel: 3910 2337 Email: qpzhang@hku.hk |
| Teacher(s) | Contact |
| Professor Q. Zhang Department of Pharmacology and Pharmacy, LKS Faculty of Medicine |
Tel: 3910 2337 Email: qpzhang@hku.hk |