Lecture 28 — Course recap#
⚠️ AI-synthesized; not fully reviewed by course staff. Treat as a study aid; released slides, notebooks, and lecture recordings are authoritative.
Date. Thu Apr 30, 2026.
The standalone deployment lecture originally planned for this slot was not taught this cycle. Instead, L28 is a guided walk back through the course arc — making the cross-cutting through-lines explicit and giving you a final-exam study frame.
The course arc in one paragraph#
We started with mathematical foundations (L1-L10): linear algebra, probability, calculus and optimization, then the supervised-learning loop (probabilistic modeling, evaluation, training, generalization). We then introduced modern AI methods (L13-L15: neural networks, LLMs, foundation models) and used three in-class lab days (L11, L12, L16) to wrestle with the orthogonality of model / loss / data type on real datasets. After spring break we walked through clinical AI modality by modality — EHR / claims (L17-L18), clinical text (L19-L20), medical imaging (L21-L22) — pairing a “what is the data” lecture with a “how do we model it” lecture every time. Then we lifted the lens beyond a single hospital with population health and survival analysis (L23) and causality and fairness (L24), and closed with the molecular tier (L25-L27): DNA and gene regulation, proteins and small molecules, and modern biological AI.
The six through-lines (TL1-TL6)#
These are the cross-cutting threads. For every one, name an instance from at least three different lectures and you’ve internalized it.
TL1 — The model is never separate from the task#
Same score, different decisions, depending on threshold, utility, prevalence, calibration, and workflow. There is no “good model” in the abstract — only a model that is good for this decision in this context. Touched in: L8 (calibration / threshold / utility), L9 (loss is a proxy for the decision), L17-L18 (cohort definition and windows are the task), L23 (survival vs. binary at fixed horizon), L24 (prediction vs. intervention).
TL2 — Data are generated, not given#
Every health dataset has a measurement system, an incentive structure, and a selection mechanism. Patterns in the data reflect those before they reflect biology. Touched in: L17 (EHR / claims as care + billing byproducts), L18 (recording timing is itself signal), L19-L20 (clinical text is documentation), L21-L22 (DICOM metadata leaks site information), L23 (healthy-volunteer effect; selection), L24 (Obermeyer: the label was the bias), L25 (population structure in genotypes).
TL3 — Representation is a scientific claim#
Tabularizing, chunking, windowing, tokenizing, embedding, fingerprinting all impose assumptions. The choice of representation is part of the model, not preprocessing trivia. Touched in: L2 (basis choice), L13 (architecture as inductive bias), L18 (tabular vs. chunked vs. event-stream EHR), L20 (tokenization, embeddings, RAG), L21-L22 (2D vs. 3D vs. WSI; MIL forced by data size), L25 (PCA on genotypes), L26 (SMILES, ECFP fingerprints), L27 (equivariance for 3D molecules).
TL4 — Generalization is contextual#
A model that works in one place may not work in another. Generalization depends on sites, populations, workflows, time, prevalence, coding practices, scanners — not on parameter count or training-set size alone. Touched in: L6 (expected vs. empirical loss), L10 (inductive bias; domain shift), L17-L18 (cohort selection), L21-L22 (multi-site DICOM artifacts; CXR generalization), L25 (PRS transferability across ancestries).
TL5 — Modern AI does not remove classical baselines#
Bayes, kNN, calibration, Cox regression, multiple sequence alignment, PSSMs, homology modeling, QSAR, docking — these are still strong, often state-of-the-art for specific subproblems. New methods should be benchmarked against them, not just against the previous deep-learning paper. Touched in: L2-L7 (the foundations themselves), L9 (kNN as a real baseline), L20 (NegEx and ClinicalBERT vs. LLMs), L23 (Cox vs. DeepSurv), L26 (MSA, PSSMs, QSAR, docking), L27 (genomic-FM critique).
TL6 — Deployment creates new distributions#
A model in production becomes part of the data-generating process it predicts over. Calibration drifts, labels arrive late, alerts cause fatigue, access disparities widen. Touched in: L8 (calibration drift), L17-L18 (label / action windows), L24 (mechanism + threshold mitigation). (The originally-planned L28 deployment lecture covered this through-line directly. It is not taught this cycle; see “Self-study” below.)
Lecture-to-through-line matrix#
TL1 |
TL2 |
TL3 |
TL4 |
TL5 |
TL6 |
|
|---|---|---|---|---|---|---|
L2 |
✓ |
✓ |
||||
L3-L4 |
✓ |
|||||
L6 |
✓ |
|||||
L7 |
✓ |
|||||
L8 |
✓ |
✓ |
||||
L9 |
✓ |
✓ |
||||
L10 |
✓ |
|||||
L13 |
✓ |
|||||
L17 |
✓ |
✓ |
✓ |
✓ |
||
L18 |
✓ |
✓ |
✓ |
✓ |
✓ |
|
L19-L20 |
✓ |
✓ |
✓ |
|||
L21-L22 |
✓ |
✓ |
✓ |
|||
L23 |
✓ |
✓ |
✓ |
|||
L24 |
✓ |
✓ |
✓ |
|||
L25 |
✓ |
✓ |
✓ |
|||
L26 |
✓ |
✓ |
||||
L27 |
✓ |
✓ |
What was not covered (self-study, not on the exam)#
The L28 slot was originally planned as a deployment lecture (drift monitoring, label delay, feedback loops, alert fatigue, regulation). It was not taught this cycle, and is not on the final exam. The deployment-adjacent material that was taught — calibration (L8), domain shift (L10), label/action windows (L17-L18), causal-mechanism mitigation (L24) — is on the exam. For your own knowledge, the deployment-specific material below is worth reading:
Finlayson et al., “The Clinician and Dataset Shift in Artificial Intelligence,” NEJM 385, 2021. The single most accessible piece on clinical-AI deployment failure modes.
Perdomo, Zrnic, Mendler-Dünner & Hardt, “Performative Prediction,” ICML 2020. Formal feedback-loop treatment.
US FDA, “AI/ML-Based Software as a Medical Device (SaMD) Action Plan,” 2021. The regulatory framing.
Sendak et al., “A path for translation of machine learning products into healthcare delivery,” EMJ Innov 4, 2020.
Final-exam study checklist#
If you can answer all of these without notes, you’re ready:
State the eight course-level learning objectives and name a lecture that contributed to each.
Articulate each of the six through-lines (TL1-TL6) in your own words.
Pick a clinical ML scenario (e.g., 30-day hospital readmission) and walk through: task definition (windows / censoring), representation (tabular / chunked / event-stream), loss, evaluation (calibration and discrimination per subgroup), and at least three failure modes the course identified.
Read a hypothetical paper that claims “AUROC 0.94 for sepsis prediction” and list five questions to ask the authors.
Distinguish prediction from intervention; explain why two natural fairness criteria are mutually incompatible at unequal base rates.
Critique a “foundation model” claim by asking what input domain and what task distribution it covers.
For at least three modalities (EHR, imaging, text, survival, genetics, proteins), name one classical baseline that is still competitive in 2026.
Materials#
No new lecture deliverable. Re-read the syllabus and concept map. Each lecture page also has a “Study guide” section at the bottom with key terms and self-check questions — those are the fastest way to active-recall.