For the first time, researchers have used machine learning – a type of artificial intelligence (AI) – to identify the most important drivers of cancer survival in nearly all the countries in the world ...

Please provide your email address to receive an email when new articles are posted on . Machine learning models may accurately predict outcome measures for patients undergoing MPFL reconstruction.

UT biochemistry major Milit Patel collaborated with researchers at Memorial Sloan Kettering Cancer Center on research published in a top cancer journal.

Novel computational techniques have rapidly developed in the last decade. Software engineers and computer programmers are pushing the boundaries of what we can do using machine learning as an ...

AI has revealed why cancer survival differs so dramatically around the world, highlighting the specific health system factors that matter most in each country.

Sylvester Tafirenyika, an AI and machine-learning engineer, is focused on building tools designed to improve health care ...

Reliability of Large Language Model Knowledge Across Brand and Generic Cancer Drug Names This retrospective cohort study of limb-sparing eSTS resections integrated clinical variables and radiomic ...

The standard practice for limited-stage hepatocellular carcinoma (HCC) is the resection or the use of local ablative techniques, such as radiofrequency ablation (RFA). The outcome after RFA depends on ...

This is an archived article and the information in the article may be outdated. Please look at the time stamp on the story to see when it was last updated. Machine learning doesn’t replace humans. But ...