Dr. Atul Butte describes how AI tools can help ID drugs and treatments that fight cancer and empower patients–and cautions that we aren’t quite there yet.

CPH’s Ida Sim highlights the ways that wearable devices like the Apple Watch focus user attention–on what the company (rather than clinicians) decide is most measurable/important.

Researchers at UCSF are using AI to help read MRIs for common but complex conditions like lower back pain, and see improvements in diagnosis, better identification of links between symptoms, and reduced patient time spent in claustrophobic scanners.

Berkeley News highlights new faculty arrivals–including our own Irene Chen.

NPR podcast The Pulse talks to Niloufar Salehi (from 32:08) about how machine translation can make care more accessible–and ongoing research to improve function for patients and clinicans alike.

CPH co-director Ida Sim, Berkeley Institute for Data Science director Fernando Pérez, and colleagues have launched Agile Metabolic Health--and a new paradigm for health algorithm development and diabetes management. The inaugural effort of the Open Platforms for Powering Science and Society initiative at the UC Berkeley College of Computing, Data Science, and Society, Agile Metabolic Health will pilot a new approach to the development of algorithms and clinical decision support using data from wearable devices, such as blood pressure cuffs and continuous glucose monitoring.

Sergio Baranzini describes how use of artificial intelligence and machine learning models–coupled with biomedical knowledge graphs–can identify subtle yet potentially significant signs and symptoms in the medical record, improving diagnosis of MS and other conditions.

Adam Yala talks machine learning, cancer risk prediction and the future transformation of medicine with The Brain Surgeon’s Take Podcast.