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Khabre desk
Recent reporting, analysis and explainers from the medical-imaging desk.
health
Researchers introduced EVLF-FM, a medical vision-language model that diagnoses images, answers questions and identifies relevant image regions. Its evaluations covered more than 1.3 million development and test samples, with external validation on 13,443 samples.
health
A medRxiv preprint reports that label-free imaging of 10 μL whole-blood samples distinguished sepsis from other critical illness and healthy controls in a prospective study. The study included 29 critically ill adults, so its performance findings are preliminary.
health
Google describes a study with 2,000 mothers in Nairobi and Chicago that used AI to interpret video from simple “blind sweep” ultrasound scans. The company says the model estimated gestational age and fetal presentation as accurately as a trained sonographer.
health
A Nature Medicine study introduces the openly released MedGemma models and MedSigLIP, a medical-image encoder, and evaluates them across medical text and imaging benchmarks. The results suggest useful starting points for adapted systems, while clinical applications still require task-specific validation.
health
A medRxiv preprint reports that muFM, a model pretrained on more than 1.7 million unlabeled micro-ultrasound images, improved performance on three prostate image-analysis tasks. Gains were largest when the model was fine-tuned with limited annotated data.
science
Researchers at OmiCore have released an openly licensed image-analysis model that marks tumour-enriched regions in colorectal cancer tissue slides. In a preprint, the team reports agreement with transcriptomics on one section and describes limits including its 112 μm resolution and lack of validation across scanners and institutions.