MED 661AIM - Artificial Intelligence in Medicine

(2 units)

Students will complete preparatory and independent reading and modules in artificial intelligence before coming to live seminars where we will discuss the reliability, issues, and projects for this elective. Students will receive instruction on the projects they need to do, have office hours to ask follow-up questions and get help, and present their project at the end of the elective to their peers and the course leadership. External guests in AI may be invited to assess the presentations.

Maximum units a student may earn: 2

Grading Basis: Medical School Grades
Units of Lecture: 2
Offered: Every Fall and Summer

Student Learning Outcomes
Upon completion of this course, students will be able to:
1. define the fundamental concepts of Medicine and artificial intelligence (AI) in the context of medical practice and patient care, including the law of bioinformatics, augmentation, and artificial intelligence.
2. explain the potential benefits and challenges of integrating AI technologies into Medical practice.
3. recognize common applications of AI in Medicine, such as image analysis, pattern recognition, and decision support systems.
4. describe the key machine learning techniques used in medical image analysis, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs).
5. differentiate between supervised, unsupervised, and semi-supervised learning approaches in the context of medical image interpretation.
6. evaluate the advantages and limitations of AI algorithms in tasks such as image segmentation, object detection, and classification within Medicine.
7. analyze the ethical implications of using AI in Medicine, considering issues like patient privacy, algorithm bias, and human-AI collaboration.
8. discuss the importance of validation, regulatory approval, and clinical trials in ensuring the safety and effectiveness of AI-based Medicine tools.

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