A deep-learning system detecting fractures across the musculoskeletal system can help reduce common diagnostic errors and improve clinical outcomes
Gun violence is a major cause of death in the United States, yet the data necessary for informing the gun policy debate is woefully incomplete. Internet search patterns have the potential to serve as a valuable complementary information source to existing approaches.
Growing evidence suggests that consumer wearable and smartphone sensor data are related to symptoms, quality of life, and risk for adverse outcomes in cancer. Larger studies that use sensor data to inform and personalize clinical care are needed.
The rate of disability accumulation varies across multiple sclerosis (MS) patients. Machine learning techniques may offer more powerful means to predict disease course in MS patients.
Digital treatments for youth with mental health problems: Developing good quality evidence takes time
The demand for digital mental health care is high, even more so since Covid-19. However, proper development and testing according to robust standards takes time, as we can show for the development of a digital psychological intervention for youth with OCD.
RCTs and variance in clinical outcomes and patient preferences
Towards the clinical integration of digital health metrics: a data-driven framework enabling their automated selection and validation