Our systematic review and meta-analysis provides a critical assessment of the deep learning literature to date and proposes recommendations to improve the quality of deep learning research in the future.
Our recent publication, Machine Learning for Patient Risk Stratification: Standing on, or looking over, the shoulders of clinicians?, in npj Digital Medicine examines the question of whether clinical machine learning models truly extend beyond what clinicians already suspect.
The COVID-19 pandemic has disrupted people daily life, digital health solutions empowered with AI could become critical and conducted in a safe way. To develop robust and reliable clinical tools capturing large variety and heterogeneity, a privacy-preserving learning strategy is worthwhile to study.
In spite of dozens of existing treatment options, children with Attention Deficit Hyperactivity Disorder (ADHD) still fare poorly across many domains. We explore a new frontier in which digital therapeutics could help address these challenges for ADHD specifically and mental health more broadly.
Smartphone apps can reduce symptoms of depression and anxiety, but real-world users rarely use them for more than a few days. Our review aimed to identify features that make these apps more engaging and effective, which is especially crucial amidst the stress and isolation of the COVID-19 crisis.
The COVID-19 pandemic has disrupted many industries, and healthcare is no exception. Digital health solutions deployed globally to facilitate extensive re-organisation of healthcare services and de-centralised care in the first 6 months of COVID-19 are presented with an overview of the road ahead.
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