Foundations of Artificial Intelligence in Health

Course information

Artificial intelligence is often used interchangeably with machine learning — but ML is a subfield of AI concerned with inductive reasoning, and a great deal of what biomedical informatics actually deploys rests on deductive reasoning instead. This course covers those founding principles: knowledge representation, logic, ontologies, search, rule-based expert systems, and probabilistic inference. Machine learning shows up in the last few weeks, for contrast.

The course is about how biomedical data can be organized, represented, interpreted, searched, and applied in order to derive knowledge, make decisions, and ultimately make predictions while mitigating bias. BMIN 5200 is the first of three: it is followed by BMIN 5210 (Machine Learning) and BMIN 5220 (Natural Language Processing).

Objectives

• Describe the challenges of representing human knowledge in silico
• Explain the difference between deduction and induction, and the role of both in AI
• Discuss past and current obstacles to implementing AI in biomedical domains
• Show how common AI approaches address real biomedical problems
• Implement core AI methods in Python — not only describe them

Prerequisites

Familiarity with basic biomedical concepts and terminology, statistics, and computer programming. BMIN 5010, BMIN 5030, and a programming course are suggested but not required. All programming is in Python. No previous exposure to AI is assumed.

Example lecture itinerary

0–30 min

Homework review

~60 min

Lecture

~25 min

In-class coding exercise

10 min

Break

~25 min

Journal club

Grading

1.0 cu. No late submissions without prior approval. Each unexcused absence deducts 5% from the final grade — contact Joe in advance if you need to miss a class.

Journal club

From Week 3 onward we spend roughly 25 minutes on one paper per week. A pair of students presents each paper — one covers background and methods, the other results and critique, and both lead the discussion. With eleven papers, everyone presents once. Presenters must also run the paper past an LLM chatbot and report which of its criticisms hold up and which are wrong.

Final project

A research project (doctoral students) or an implementation project (master's and professional students), addressing a biomedical problem of your choice. Deductive inference must be central — a project built primarily on machine learning is not acceptable. Both paths produce an 8–10 page report typeset in the AAAI LaTeX template, plus a 10-minute recorded presentation. Full specification in the final project instructions.

Generative AI policy

Although this is a course on AI, all graded work — the four homework assignments and the final project — must be completed without generative AI assistance. Violations are treated as plagiarism under Penn's Code of Academic Integrity. Two exceptions: the LLM-driven critique in journal club, where its use is required, and the ungraded in-class exercises.

Disability accommodations

Penn provides reasonable accommodations to students with disabilities approved by Student Disabilities Services. Please make an appointment with Joe as early as possible to discuss your accommodations. SDS services are free and confidential; appointments can be made at (215) 573-9235.

Office hours

To be scheduled by poll in the first week of the semester. One-on-one meetings with the course director or TA can be arranged as needed.

Resources

Documents are hosted on Penn Box and require PennKey sign-in. Canvas is used only for submitting assignments and viewing grades.

Schedule

Thirteen meetings. Lecture slides and journal club papers are on Box (PennKey required); the in-class notebooks open directly in Colab.