Years ago, as she sat in waiting rooms, Maytal Saar-Tsechansky began to wonder how people chose a good doctor when they had no way of knowing a doctor's track record on accurate diagnoses. Talking to other patients, she found they sometimes based choices on a physician’s personality or even the quality of their office furniture.

Across the United States, no hospital is the same. Equipment, staffing, technical capabilities, and patient populations can all differ. So, while the profiles developed for people with common conditions may seem universal, the reality is that there are nuances that require individual attention, both in the make-up of the patients being seen and the situations of the hospitals providing their care.

Autoimmune diseases, where the immune system mistakenly attacks the body's own healthy cells and tissues, often have a preclinical stage before diagnosis that’s characterized by mild symptoms or certain antibodies in the blood. However, in some people, these symptoms may resolve before culminating in the full disease stage.

Knowing who may progress along the disease pathway is critical for early diagnosis and intervention, improved treatment and better disease management,

Doctors around the world may soon have access to a new tool that could better predict whether individual cancer patients will benefit from immune checkpoint inhibitors - a type of immunotherapy - using only routine blood tests and clinical data.

The artificial intelligence–based model, dubbed SCORPIO, was developed by a team of researchers from Memorial Sloan Kettering Cancer Center (MSK) and the Tisch Cancer Institute at Mount Sinai.

Introducing Annotatability - a powerful new framework to address a major challenge in biological research by examining how artificial neural networks learn to label genomic data. Genomic datasets often contain vast amounts of annotated samples, but many of these samples are annotated either incorrectly or ambiguously.

A new study from Vanderbilt University Medical Center shows that clinical alerts driven by artificial intelligence (AI) can help doctors identify patients at risk for suicide, potentially improving prevention efforts in routine medical settings.

A new international study led by researchers at Karolinska Institutet in Sweden shows that AI-based models can outperform human experts at identifying ovarian cancer in ultrasound images. The study is published in Nature Medicine.