surgery
A new training tool combines multi-user virtual reality with a text-reading AI to help operating room teams practise communication, leadership and decision-making. The aim is straightforward: fewer misunderstandings during surgery and safer care for patients. Teamwork errors are a known cause of harm, yet most training still focuses on instruments
healthtech
Voice tech is entering the clinic—but we still judge automatic speech recognition (ASR) mainly by Word Error Rate (WER). That score can’t tell you if a mistake is harmless or could change a care plan. * What they did: Clinicians compared ground-truth and ASR transcripts from real doctor–patient
AI
AI can be impressively accurate in medicine—but dangerously overconfident when cases are ambiguous. MedBayes‑Lite offers a fix: a lightweight add‑on that helps clinical language models say how sure they are. How it helps, without retraining: * Estimates what the model doesn’t know (epistemic uncertainty) using Monte Carlo
ASR
Not all transcription mistakes are equal. In clinic visits, the go-to score Word Error Rate (WER) can look fine even when errors change care-critical meaning. * Expert clinicians compared true utterances with ASR transcripts in two doctor-patient datasets, labeling the impact: No, Minimal, or Significant. * WER and many other metrics poorly
AI
Lowering CT radiation keeps patients safer—but it can make images noisy and blurry. LMM-IQA is an AI that judges low‑dose CT quality and explains what went wrong. * Gives a quality score plus short, plain‑English notes on noise, blur, and contrast loss. * Works without task‑specific training and