
AI summaries fail to improve patient scan comprehension
Patients who read AI-generated summaries of their brain scans feel significantly more confident, but they do not actually understand their medical results any better.
Discover the newest research about AI innovations in 🩻 Radiology.

Patients who read AI-generated summaries of their brain scans feel significantly more confident, but they do not actually understand their medical results any better.

A new study shows that a consumer-grade computer running an LLM agent can build world-class medical AI without elite engineering teams.

Automating ultrasound analysis could finally break the bottleneck in breast cancer screening.

A simple anatomical marker combined with machine learning could change how clinicians predict long-term recovery after a severe brain bleed.

By fusing electrical and structural data, a new multimodal model flags hidden heart valve risks before symptoms appear.

A massive screening study reveals that chest X-ray AI can flag lung tumors before human radiologists, but its real value lies in how we manage the noise it creates.

Algorithms can now flag the earliest signs of lung cancer, but technology alone cannot fix a broken healthcare pipeline.

A new ensemble AI model predicts positive surgical margins before breast-conserving surgery, but its performance drop in external testing highlights the ongoing struggle with clinical generalization.

A new foundation model uses baseline CT scans to flag which lung cancer patients are likely to develop life-threatening lung inflammation from immunotherapy.

The flood of FDA-approved radiology AI is hitting a wall of clinical rejection because developers forgot a basic rule of medicine: do not disrupt the workflow.