Dana Allison

A Georgia Senate candidate’s Passover ad in this week’s Atlanta Jewish Times features challah. It’s the thought that counts, I guess. #gapol

The 22-year-old wife of an Army staff sergeant came to the U.S. from Honduras as a toddler. Last week she was taken by ICE agents from a military base where the couple planned to live and put in detention facility with hundreds of women facing deportation. nytimes.com/2026/04/05/us/…

This week, the "AI replacing doctors" debate is back. The CEO of America's largest public hospital system says he's ready to replace radiologists with AI. The Stanford-Harvard NOHARM study shows top models outperforming generalists. The discourse is moving fast. I run AI at @UHN, the largest hospital in Canada. Here's what I actually see. We've developed AI models across imaging, pathology, and clinical decision support. In controlled conditions, the accuracy numbers are real. In some narrow tasks, models genuinely outperform. That's not hype. But the operational reality of running these systems inside a large hospital teaches you things benchmarks never will. The errors that hurt patients aren't the confident wrong answers. They're the quiet omissions, i.e., the thing the model didn't flag because it wasn't in the training distribution. NOHARM found 76.6% of AI errors were omissions. We see this too. And in a hospital, a missed finding doesn't just affect one case. It propagates: the downstream physician trusts the AI read, the patient waits, the window closes. The accountability structure also doesn't exist yet. When an AI-assisted diagnosis leads to harm, who is responsible: the physician, the hospital, the vendor? In Canada, we don't have a clear answer. No hospital system deploying AI at scale does. That's not a regulatory delay. That's a fundamental gap in the infrastructure for AI-in-medicine. What I'm genuinely optimistic about: AI is already changing how our radiologists work. Not replacing them, but changing the shape of the job. Routine reads get faster. Their time shifts toward complex cases, clinical correlation, cases where the AI flags uncertainty. That's the right direction. But "ready to replace radiologists" skips 10 hard years of work on deployment infrastructure, liability frameworks, clinician training, and failure mode monitoring that nobody wants to talk about because it's less exciting than accuracy benchmarks. The capability question is nearly answered. The deployment question has barely been asked. CEO story: beckershospitalreview.com/radiology/nyc-… NOHARM paper: arxiv.org/abs/2512.01241

For five months, a father waited for his 3-year-old daughter’s release from federal custody after she was apprehended by ICE while crossing the border with her mother. He just learned that the toddler was placed in a foster home where she was sexually abused. abcnews.com/US/wireStory/3…

NEWS: Massive budget cuts for US science proposed again by Trump administration "It's an extinction-level event for science". The US government is proposing massive cuts to almost every branch of science, from NASA to the National Institutes of Health. NSF would completely eliminate the social, economic and behavioral sciences directorate. This would decimate the world's leading scientific system. nature.com/articles/d4158…