To play this video you need to enable JavaScript in your browser. Patients in England with a specific type of incurable breast cancer can now access a life-extending drug on the NHS, two years after it was deemed too expensive by a health body. Enhertu can give patients almost seven extra months to live on average - with some living up to three years longer. Women in Scotland have had access to the drug on the NHS since 2023, and it is available in 26 other European countries. Charities and patients have campaigned for it to be more widely available since 2024, when the National Institute for Health and Care Excellence (NICE) said Enhertu was not good value for money.

The study examined 97,364 breast scans from 29,921 women who had an average age of 54. The study examined 97,364 breast scans from 29,921 women who had an average age of 54. Doctors have discovered a way to use routine mammograms that screen for breast cancer to spot heart disease, the world's leading - and frequently underdiagnosed - cause of death in women. Researchers analysed the scans using artificial intelligence and were able to successfully identify women with coronary heart disease, high blood pressure or who had suffered a stroke. Experts said it meant breast screening for cancer could become dual-purpose, helping to flag women with heart disease, and other cardiovascular issues, as well as spotting breast cancer early.

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I'd Rather Risk Cancer Than See AI Move This Fast I'd benefit if AI cured cancer. And I still want AI progress to slow down. On a fall afternoon 15 years ago, I met an idealistic researcher outside a Stanford coffee shop to discuss our shared dream: using AI to detect cancer. He had wiry hair, a penchant for talking with his hands, and a reputation for brilliance. He worked at a research lab that developed early screens for cancer; I, at 20, had just learned that I carried a mutation that conferred a very high risk of breast, ovarian, and other cancers.

No feature ranking can be simultaneously faithful, stable, and complete when features are collinear. For collinear pairs, ranking reduces to a coin flip. We prove this impossibility, quantify it for four model classes, resolve it via ensemble averaging (DASH), and machine-verify it with 305 Lean 4 theorems. We characterize the complete attribution design space: exactly two families of methods exist -- faithful-complete methods (unstable, with rankings that flip up to 50% of the time) and ensemble methods like DASH (stable, reporting ties for symmetric features) -- and no method lies outside this dichotomy. The impossibility is quantitative: the attribution ratio diverges as 1/(1-rho^2) for gradient boosting, is infinite for Lasso, and converges for random forests. DASH (Diversified Aggregation of SHAP) is provably Pareto-optimal among unbiased aggregations, achieving the Cramer-Rao variance bound with a tight ensemble size formula. In a survey of 77 public datasets, 68% exhibit attribution instability. Switching to conditional SHAP does not escape the impossibility when features have equal causal effects. The framework includes practical diagnostics -- a Z-test workflow and single-model screening tool -- and has direct consequences for fairness auditing: SHAP-based proxy discrimination audits are provably unreliable under collinearity. The design space theorem, diagnostics, and impossibility are mechanically verified in Lean 4 (305 theorems from 16 axioms, 0 sorry) -- to our knowledge, the first formally verified impossibility in explainable AI.

Dna demethylation is associated with malignant progression oflower-grade gliomas.Scientific reports,9(1):1-12, 2019.

Existing methods typically rely on either interpretable but inflexible models or highly flexible black-box approaches that sacrifice interpretability; moreover, most impose a single global decision rule across patients. We introduce the Locally Interpretable Individualized Treatment Rule (LI-ITR) method, which combines flexible machine learning models to accurately learn complex treatment outcomes with locally interpretable approximations to construct subject-specific treatment rules. LI-ITR employs variational autoencoders to generate realistic local synthetic samples and learns individualized decision rules through a mixture of interpretable experts. Simulation studies show that LI-ITR accurately recovers true subject-specific local coefficients and optimal treatment strategies. An application to precision side-effect management in breast cancer illustrates the necessity of flexible predictive modeling and highlights the practical utility of LI-ITR in estimating optimal treatment rules while providing transparent, clinically interpretable explanations.

Many important datasets contain samples that are missing one or more feature values. Maintaining the interpretability of machine learning models in the presence of such missing data is challenging. Singly or multiply imputing missing values complicates the model's mapping from features to labels. On the other hand, reasoning on indicator variables that represent missingness introduces a potentially largenumber ofadditional terms, sacrificing sparsity.

Risk-based breast cancer screening matches individual genetics to mammogram frequency, showing safety compared to annual screening in a 28,000-woman study.