A single-centre pilot put GPT-5 against radiologists on PET staging of oesophageal cancer. Its overall accuracy looks respectable. Its sensitivity for the metastases that change the treatment plan does not — and that is the number that decides whether a tool is usable.
A family used a chatbot to make sense of an MRI report in the weeks before a surgeon could see them. The account is honest and human — and it is one self-reported case, with no comparator and no follow-up. Worth reading for the gap it names, not the method it proves.
A JMIR correspondent piece surveys digital narrative medicine and reports a striking figure: over 90 percent of one antistigma project's participants had head injuries that no chart recorded. It is opinion, not data — and still worth reading carefully.
A recovery tool that about a third of patients found painful to reread. A JMIR feature on digital intensive-care diaries is genuinely moving — and it is journalism borrowing other studies' numbers, not new evidence.
A stepped-wedge trial gave 67 families a web tool to set and track care goals for medically complex children. Nearly all used it in the room; most never touched it after. The signal that survived came from the conversation, not the software.
A viewpoint in JMIR Dermatology argues that for millions, the first dermatological opinion arrives by phone — from someone with no clinical training. It has no new data of its own, but it reads the field honestly.
A benchmark fed three language models deliberately corrupted medical text. Misspellings and sound-alikes barely dented them; blacking words out did the damage. Useful — but it is synthetic noise on a test set, not evidence from a ward.
Twenty junior doctors describe how clinical alerts stop being read. The useful finding is not that they ignore warnings — it is that fatigue is a moving equilibrium shaped by culture and design, not a fixed trait you can configure away.
A small eye-tracking pilot makes an uncomfortable point: the people asked to keep a precise sleep diary are the ones whose attention the poor sleep has already eroded. The interface is not neutral — but this is a pilot, and it measured strain, not cure.
An explainable AI model predicted how long myeloma patients would stay on treatment, using twenty years of Japanese claims data and 647 variables. The discrimination is modest and fairly reported. The part that needs a careful eye is who paid, and which finding they got.
Four NEJM AI podcast interviews, recorded months apart, keep landing in the same three places: a values vacuum, a bias we taught the machine, and a trust gap that tracks consequence. None of it is evidence. The agreement is still worth an hour.
A scoping review screened 275 records to find every AI model meant to prevent surgical complications and follow it to the bedside. Of 19 studies, the models were often accurate. Two are in routine use — and the bottleneck is not the algorithm.
A meta-analysis of machine learning for detecting depression in text screened 3,067 papers and kept 11. The pooled signal is strong — but the prediction interval, from near-zero to near-perfect, is the finding that should travel.
A Chinese single-centre study built a model that flags older surgical patients likely to sleep badly the night before an operation. The discrimination is high — but it was validated on itself, and what it predicts is a label, not a recovery.
A PRISMA review of 65 studies finds language models consistently beat classical methods at classifying clinical text. The honest reading is narrower: it is a synthesis of single-site accuracy studies that mostly never asked whether the models work at the bedside.
A small Stanford study had clinicians and parents rate the same AI-written clinical summaries. They disagreed, significantly — and that disagreement, not the scores, is the finding worth keeping.
A clinical model clears validation, goes live, and slowly drifts — and no one is assigned to watch. A narrative review maps why oversight, not algorithms, is now the binding constraint on healthcare AI. Read for what a review can and cannot prove.
A genuine randomized trial put a medical language model beside nine cardiologists on 107 complex cases. The result is real — and narrower than the headline. It measured preference, not outcomes, and the system was its makers' own.
A study of 326 physicians found pass rates on a clinical-reasoning test rose from 6.4 to 58.6 percent after a 90-minute AI course. The number is real. The design cannot tell us how much of it was the teaching and how much was simply being handed GPT-4.
A single-centre survey of 330 oncology patients found comfort with AI falls steadily as the decision moves closer to life and death — and that the loudest worry is not the machine but the loss of the doctor. A careful read of who was asked.
An experiment had 149 students fact-check health myths with ChatGPT. Misconceptions about flu vaccination fell measurably; those about climate change did not move at all. The asymmetry tells you more than either result alone — and the evidence is thin.
In 118 of 150 blind comparisons, physicians preferred GPT-4.0's answer to a doctor's. The figure is real. But the doctors' answers came from a Reddit forum, and no one ever checked which answer was correct.
A retrospective study grounded a triage language model in two sources at once — a local guideline and three thousand past cases. The honest finding is quieter: it beat an ungrounded model, the authors call it preclinical, and no patient was followed forward.
A Dutch model flags who will be admitted from the emergency department a median 111 minutes before the clinician commits. The number holds. What it measures — decision time, on records, never a patient — is narrower than it sounds.