Digital Health Frontier Blog
By John Halamka and Paul Cerrato - In the second installment in the series, we explain how gradient boosting can help make patient care more precise and cost effective.
By John Halamka and Paul Cerrato - Many physicians ignore the recommendations provided by machine learning algorithms because they don’t trust them. A few imaginative software enhancements may give them more confidence in an algorithm’s diagnostic and therapeutic suggestions.
By John Halamka and Paul Cerrato - The potential benefits of AI-enabled algorithms have to be weighed against the risk of dataset shift, which can compromise their accuracy in ways that developers never anticipated.
By John Halamka and Paul Cerrato - Our vision for health care technology includes a steadfast determination to use AI to augment human intelligence — not contribute to a dystopian future some fear is already upon us.
By John Halamka and Paul Cerrato - One of the privileges we enjoy working in digital health is the opportunity to use state-of-the-art AI tools to unearth actionable insights from millions of patient records, without compromising their privacy.
By John Halamka and Paul Cerrato - Machine learning-enhanced algorithms may help clinicians select the best medications for each patient, speeding up the long journey to find effective relief for a crippling disorder.
By John Halamka and Paul Cerrato - Some clinicians reject any AI-infused recommendations, while others are so confident in these tools that they forfeit their independent diagnostic skills. A closer look at the research can turn these mistakes into insights.
By John Halamka and Paul Cerrato - Randomized controlled trials (RCTs), the gold standard in clinical medicine, only tell us how the average patient responds to a treatment or an AI-based algorithm. N of 1 trials provide a unique opportunity to individualize patient care.
By John Halamka and Paul Cerrato - Predicting cardiovascular disease and death is an imperfect science; the right AI algorithms can make these estimates more accurate.
By John Halamka and Paul Cerrato - Affective computing and sentiment analysis can help clinicians read between the lines, allowing them to detect patients’ unexpressed feelings and subtle emotional cues that may signal subclinical disease—and much more.