Researchers at the Francis Crick Institute and UCL Queen Square Institute of Neurology, working with technology company Faculty AI, have shown that machine learning can accurately predict subtypes of Parkinson’s disease using images of patient-derived stem cells.

Their work, published today in Nature Machine Intelligence, has shown that computer models can accurately classify four subtypes of Parkinson’s disease, with one reaching an accuracy of 95%.

Deep-learning technology developed by a team of Johns Hopkins engineers and cancer researchers can accurately predict cancer-related protein fragments that may trigger an immune system response. If validated in clinical trials, the technology could help scientists overcome a major hurdle to developing personalized immunotherapies and vaccines.

Through the use of eHealth application ikHerstel, patients recover from major abdominal operations 30% faster than patients who do not use the app. That is the main conclusion of research led by Amsterdam UMC across eleven Dutch hospitals. The app aims to empower patients to feel more in control of their recovery process. The results were published today in Lancet Digital Health.

New research led by scientists working with Georgia State University's TReNDS Center has identified age-related changes in brain patterns associated with the risk for developing schizophrenia.

The discovery could help clinicians identify the risk for developing mental illness earlier and improve treatment options. The study is published in the Proceedings of the National Academy of Sciences (PNAS).

When you need accurate information about a serious illness, should you go to Google or ChatGPT?

An interdisciplinary study led by University of California, Riverside, computer scientists found that both internet information gathering services have strengths and weaknesses for people seeking information about Alzheimer's disease and other forms of dementia.

It is possible to accurately predict hospital admission numbers due to COVID-19 up to four weeks in advance using an Artificial Intelligence (AI) based system together with COVID wastewater sampling, new research shows.

The study, published in the journal Nature Communications, used wastewater data from 159 counties in the US, covering nearly 100 million Americans, along with US hospital admission records, to develop the prediction model.

Artificial intelligence (AI) is already being used to diagnose skin cancer, but it cannot (yet) keep pace with the complex decision-making of doctors in practice. An international research team led by Harald Kittler of MedUni Vienna has now explored a learning method in which greater accuracy in AI results can be achieved by incorporating human decision-making criteria.