In a new Lancet lecture, a cancer researcher argues that screening should match each person's risk. I agree, as long as it also means doing less for people at low risk.

An AI system wrote a paper that cleared reviewers at an ICLR workshop. It is a real milestone, and an honest stress test of peer review itself.

A January 2026 Nature study links AI use to bigger scientific careers and a narrower spread of research topics. Both can be true at once, and that is the problem.

A Swedish screening trial randomized more than 100,000 women to test AI in real mammography. That, not another benchmark score, is what earns trust.

A small MIT study found that people who let ChatGPT write their essays remembered little of them. That is a real cost for learning, and it is not brain damage.

AlphaFold 3 predicts how proteins, DNA, RNA and drug-like molecules fit together. That is a real advance. A predicted shape is still a starting point, not a finding.

A 2024 checklist for medical imaging AI quietly asks the questions a headline AUC skips: who was tested, where, how trustworthy the numbers are, and who benefits.

A CT scan gives more radiation than a regular X-ray. How much more depends on the scan and the person, and the risk is small but uncertain.

A 2024 Nature Perspective argues that AI can make scientists feel they understand more than they do. The fix starts with knowing the assumptions behind a fluent answer.

When one patient's scans end up in both training and test data, a medical imaging model can look far better than it is, and nobody has to cheat.

A December 2022 Liver-Chip study reported strong results on one defined job. The useful question is what a model is validated for, not whether it replaces animals.

Nature's 2023 postdoc survey found more optimism than in 2020, alongside familiar pressures. I think the healthiest reading is that a science career has many homes.