Discover the newest research about AI innovations in 🩻 Radiology.
A new study reveals that neurologists struggle to accurately predict stroke recovery because of systematic optimism and poor visual assessments, but AI models can correct these human errors.
A new study reveals that while AI can draft highly informative medical answers, patients still prefer the simpler, clearer touch of a human clinician.
Automating the search for mucus plugs in lung scans reveals a hidden driver of COPD mortality that human eyes routinely miss.
Throwing millions at AI imaging algorithms will not solve the UK’s cancer crisis without the human staff to act on the results.
A six-year head start on breast cancer sounds like a clinical triumph, but it comes with a massive catch.
A new active learning model proves that medical AI does not need massive, expensive datasets to outperform human-labeled benchmarks.
A new computational model uses a single brain scan to predict adolescent anxiety and depression before symptoms fully emerge.
Evaluating multimodal LLMs in CT scan interpretation reveals GPTRadScore’s accuracy: Pearson’s correlation up to 0.91! 📊🩻
AI in radiology: Leadership drives ethical integration, enhancing workflow efficiency and patient care. 📈🤖
MHRA receives £1 million for AI Airlock regulatory sandbox, promoting safe testing of innovative medical devices. 🤖💰
