For how we live.
Trained on 1.7 million ECGs paired with clinicians' report text, ECG-CLIP reached the same accuracy as the best comparator with about 90% less training data — a bid at the field's labelling bottleneck.
A review of 41 studies found that the great majority of AI medication-adherence prediction models carried high risk of bias, and that fancier algorithms did not reliably predict better.
A hamstring analysis separated pre-injury from return-to-sport sprint mechanics at AUCs near 0.79. A multi-team rugby model tried to predict injuries before they happened, and could not.
A Nature Reviews Drug Discovery audit says the problem isn't the models. They were built to be validated rather than used, and benchmarked against the wrong thing.
START-AI reads triage comments, case notes and whether anyone ordered a blood test. Adding heart rate and blood pressure to the model produced no measurable improvement — a result the team published rather than buried.
A secondary analysis of 8,376 adolescents built two models to predict who benefits from school-based mindfulness training. Both found a subgroup — and the difference in outcomes was trivial.
Trained on 2,202 retracted paper mill articles, a text classifier flagged 9.87 percent of the cancer literature — including in the highest-impact journals. Flagged is not the same as fraudulent.
A January study forecasts particulate pollution across six Gulf states and reports Qatar highest at 87.90 µg/m³. The modelling is careful; the input data is annual, national and thirty-one points long.