and my none-too-awake brain apparently combined them.
My relative who battled breast cancer 9 years ago had apparently beaten it, but she lost that breast and had lasting effects from chemo. Then an MRI ordered a couple of weeks ago because she was wheezing during a doctor's appointment revealed a suspicious lung nodule. A PET-CT scan last week made the lung nodule look less worrisome (but it will be checked again in three months), but found a suspicious mass in her breast that wasn't cancerous before, and she had a biopsy Friday. Should have the results in a couple more days.
I don't know if AI was used in reading any of those results.
But there's already evidence that use of AI deskills doctors:
https://www.forbes.com/sites/jessepines/2026/02/23/will-ai-de-skill-doctors-evidence-is-starting-to-trickle-in/
-snip-
Recent evidence of de-skilling comes from a 2025 observational study published in The Lancet Gastroenterology & Hepatology. The study examined AI systems designed to detect adenomas—non-cancerous tumors in the GI tract that can sometimes transform into cancer.
In the study, endoscopists who routinely used AI assistance had a significant decline in adenoma detection from 29% to 22% during subsequent non-AI procedures. This suggest that sustained AI exposure can negatively impact measurable clinical performance.
-snip-
A study found that radiologist’s ability to catch AI-generated errors in mammograms correlated strongly with experience. In a simulated scenario where an AI system provided an incorrect suggestion, the rates of correctly read mammograms was 20% for inexperienced radiologists, 25% for the moderately experienced and 46% for the very experienced.
This raises the specter of what is called “never-skilling.” If medical trainees rely on AI-generated differentials before wrestling with clinical ambiguity themselves, the scaffolding of diagnostic reasoning that typically emerges during the years of residency training may never fully develop.
-snip-
= new reply since forum marked as read