Interactive Medical AI Lab

A synthetic ECG built from Gaussian P, QRS and T waves at a known, slowly drifting heart rate, plus motion artifact made of baseline wander, muscle noise and movement bursts. The detector is a classical QRS detector: zero-phase 5–15 Hz band-pass, derivative, squaring, a 150 ms moving window, an adaptive threshold and a 250 ms refractory period. Detected beats are matched to true beats within 100 ms. It is the same code as the live demo on the Medical AI page.

Each synthetic patient has a stable set of 8 features, a fingerprint, and a single outcome that shifts 3 of those features slightly. Each visit adds small visit-to-visit noise to the fingerprint, so a patient’s visits are much more alike than two different patients. A distance-weighted 7-nearest-neighbour classifier is scored with the area under the ROC curve (AUC). The data is fixed by a seed, so the same settings always give the same numbers.

4,000 synthetic patients with a risk score. True risk is the logistic function of the score plus an intercept set by the prevalence slider. The model is the same logistic function with the intercept that fits 10% prevalence; the miscalibrated variant multiplies its log-odds by 0.6 and adds 1, so most patients get too high a risk and the highest-risk patients too low a one. Outcomes are expected values, not random draws, so the numbers do not jitter. Net benefit follows decision-curve analysis: true positives minus false positives weighted by the threshold odds, t / (1 − t), per patient.

A procedural slice: a body outline, two organs, a bright bony structure and an irregular lesion with a brighter core. Scanner A adds fixed Gaussian noise. Scanner B scales contrast around the image mean and adds its own noise. The “model” is a 3 × 3 mean filter followed by an intensity window, grid-searched for the best Dice on scanner A and then frozen. Normalisation, when on, z-scores each image before the window is applied, and the window is re-tuned on normalised scanner A.

Binary masks on a 160 × 160 grid with 1 mm pixels. The large true lesion is a slightly irregular disc; the prediction follows its boundary with a fixed wobble scaled by the roughness slider. Dice is 2|A∩B| / (|A| + |B|) over all pixels. Boundary distances are measured between the boundary pixels of the two masks in both directions; HD95 is the larger of the two directed 95th percentiles, and the maximum is the Hausdorff distance. A lesion counts as detected if any predicted pixel overlaps it.

One fictional note in three versions, with a ground-truth annotation of 13 identifiers: names, dates, an ID number and a phone number (all zeros), an address and towns, and two rare details. The detector is a list of regular expressions and word lists, switched on in five levels. An identifier counts as caught when at least half of its characters are redacted; a redaction that touches no identifier counts as over-redaction. Real systems combine rules with trained models and human review.