Part 6 — Course Recap#
⚠️ AI-synthesized; not fully reviewed by course staff. Treat as a study aid; released slides, notebooks, and lecture recordings are authoritative.
Lecture 28
What this part is about#
The L28 slot was originally planned as a deployment lecture (drift, feedback loops, alert fatigue, regulation). This cycle, the deployment material has been moved to optional self-study (see external resources below) and the slot is used as a course-wide recap — a guided walk back through the arc of the semester, with the cross-cutting through-lines (TL1-TL6 in the concept map) made explicit.
If you missed any earlier lectures, this is the slot where the cross-references that have been quietly accumulating get tied together into a single picture.
Goals for this part#
Re-state the eight course-level learning objectives from the syllabus, and identify which lectures contributed to each.
Articulate the six course-wide through-lines (TL1-TL6) in your own words and name an instance of each from at least three different lectures.
Identify what was not covered — deployment-specific topics that the optional self-study fills in.
Walk into the final exam with an integrated picture of the course rather than 27 isolated lecture summaries.
Key takeaways for this part#
These are the same six course-wide takeaways from Level 0 of the syllabus, restated as the recap’s load-bearing claims:
The model is never separate from the task. Same score, different decisions.
Data are generated, not given. Every health dataset has a measurement system, an incentive structure, and a selection mechanism.
Representation is a scientific claim. Tabularizing, chunking, windowing, tokenizing, embedding, fingerprinting all impose assumptions.
Generalization is contextual. It depends on sites, populations, workflows, time, prevalence, scanners.
Modern AI does not remove classical baselines. Bayes, kNN, calibration, Cox, MSA, PSSMs still matter.
Deployment creates new distributions (the through-line that was supposed to be L28’s main subject; covered briefly in L8, L17-L18, L24, and the self-study below).
Lectures in this part#
External resources for this part#
Deployment material (the dropped lecture, for self-study)
Finlayson et al., “The Clinician and Dataset Shift in Artificial Intelligence,” NEJM 385, 2021. Most accessible single piece on deployment failure modes.
Perdomo, Zrnic, Mendler-Dünner & Hardt, “Performative Prediction,” ICML 2020 — the formal treatment of feedback loops.
US FDA, “Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan,” 2021. The regulatory framing; relatively short.
Sendak et al., “A path for translation of machine learning products into healthcare delivery,” EMJ Innov 4, 2020.
Lenert, Sakaguchi & Weir, “Rethinking the discovery of medical knowledge in the age of artificial intelligence,” Lancet Digital Health 5, 2023.
McKinney et al., “International evaluation of an AI system for breast cancer screening,” Nature 577, 2020 — a contested case study worth reading alongside the critique by Haibe-Kains et al. (Nature 586, 2020).
Cross-cutting course resources
Wiens et al., “Do no harm: a roadmap for responsible machine learning for health care,” Nat Med 25, 2019.
Ghassemi, Naumann, Schulam, Beam, Chen & Ranganath, “A Review of Challenges and Opportunities in Machine Learning for Health,” AMIA Joint Summits, 2020.
Topol, “High-performance medicine: the convergence of human and artificial intelligence,” Nat Med 25, 2019.