Multimodal AI models, trained on numerous types of data, could help doctors screen patients at risk of developing multiple different cancers more accurately.. Researchers from the Brigham and Women's Hospital part of Harvard University's medical school developed a deep learning model capable of identifying 14 types of cancer. Most AI algorithms are trained to spot signs of disease from a single source of data, like medical scans, but this one can take inputs from multiple sources. Predicting whether someone is at risk of developing cancer isn't always as straightforward, doctors often have to consult various types of information like a patient's healthcare history or perform other tests to detect genetic biomarkers. These results can help doctors figure out the best treatment for a patient as they monitor the progression of the disease, but their interpretation of the data can be subjective, Faisal Mahmood, an assistant professor working at the Division of Computational Pathology at the Brigham and Women's Hospital, explained. "Experts analyze many pieces of evidence to predict how well a patient may do. These early examinations become the basis of making decisions about enrolling in a clinical trial or specific treatment regimens. But that means that this multimodal prediction happens at the level of the expert. We're trying to address the problem computationally," he said in a statement.

An international research team, including medical scientists from the University of Leeds, says it has developed a novel method of using artificial intelligence to predict cancer from patient data without putting personal information at risk. The scientists published their study ("Swarm learning for decentralized artificial intelligence in cancer histopathology") in Nature Medicine. "Artificial intelligence (AI) can predict the presence of molecular alterations directly from routine histopathology slides. However, training robust AI systems requires large datasets for which data collection faces practical, ethical, and legal obstacles. These obstacles could be overcome with swarm learning (SL), in which partners jointly train AI models while avoiding data transfer and monopolistic data governance," write the investigators.

In near future computers will learn to recognize cancer. To achieve this they will need huge amounts of patient data. Prostate cancer is the most common cancer among Norwegian men. Fortunately, not everybody becomes ill or develops cancer. When something in the body can develop into cancer, it is natural that we will wish to remove it.

By beating humans at games of Go and Jeopardy, artificial intelligence engines like Google's DeepMind and IBM's Watson have captured attention for their promise of solving bigger human problems. Watson, for example, is being enlisted to help doctors predict cancer in patients. The American internet pioneer Douglas Engelbart suggests that AI's grandest promise is the amplification of human ability. Whether it's automating rote cognitive tasks like tagging people in photos or assisting in complex work flows like cancer treatment, the human-augmentation promise feels almost inevitable in every product and domain. Self-driving cars rely on massive amounts of data collected over several years from efforts like Google's people-powered street canvassing, which provides the ability to "see" roads.