HAD7001H-S

Prerequisite

N/A

Objectives

• Develop an understanding of the fundamental concepts behind different health data modalities and their processing, and learn how AI methods are applied to tabular data, clinical text, medical images, and multimodal data in healthcare and medicine.
• Acquire the ability to select, design, and implement appropriate AI approaches for applied health research. This includes building, fitting, and evaluating models across different data modalities and interpreting results.
• Gain experience with Python programming for carrying out data preparation, model training, and performance evaluation using real-world healthcare datasets.
• Develop an understanding of ethical considerations and challenges in applying AI to healthcare, including issues of data quality, interpretability, bias, fairness, generalizability, transparency, privacy, deployment, and responsible use.

Description

Artificial Intelligence (AI) is rapidly transforming healthcare by enabling new approaches to diagnosis, prognosis, and personalized treatment. This course provides a comprehensive introduction to AI in health and medicine, with a focus on tabular data, clinical text (including large language model based methods), medical images, and multimodal data. It also introduces generative AI concepts in the context of health applications. Students will first learn core methods for processing and analyzing each data type, then apply AI techniques across modalities. The course emphasizes both theoretical foundations and hands-on practice using Python. A multi-phase course project and an assignment give students experience applying AI methods to realistic healthcare datasets and strengthen practical skills. The course also examines current challenges in deploying AI in healthcare and public health, including methodological and ethical considerations. By the end of the course, students will be able to design, implement, and critically assess AI approaches across multiple health data modalities.

Instructors

Evaluation Breakdown

Competencies