A large-scale analysis of millions of cancer studies has uncovered patterns suggesting that a significant portion of the literature may not be as reliable as it appears.

Investigators sought to train and validate a machine learning model to distinguish paper mill publications from genuine articles and assess the prevalence of paper mill publications.

A new machine learning tool has identified more than 250,000 cancer research papers that may have been produced by so-called ...

Cancer research laboratory tests can now be done using micro-physiological systems mimicking human physiology, allowing ...

Forbes contributors publish independent expert analyses and insights. Peter Suciu covers trends in the world of aerospace and defense. Social media has become a breeding ground for misinformation, and ...

A University student developed a machine-learning model to identify factors associated with cancer outcomes across the globe, according to a Jan. 14 study.  Biochemistry senior Milit Patel developed ...

Three scientists working in a laboratory. They are bending over a table looking at data and a paper. Heather Candrilli, 36, a mother of two, is battling metastatic colon cancer, underscoring the ...

Startling new research is amplifying concerns about the association between alcohol use and your risk of developing cancer, ...

A new report from OpenAI and a group of outside scientists shows how GPT-5, the company’s latest AI large language model (LLM), can help with research from black holes to cancer‑fighting cells to math ...