AI in Medicine
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More efficient regulatory reviews could help bridge the gap in healthcare access by bringing essential medications to market
Artificial intelligence can accurately assess atopic dermatitis severity using patient-uploaded smartphone photos.
These talks could represent a significant step toward modernizing regulatory science.
An AI-based digital tool provided effective insulin dosage titration recommendations for inpatients with type 2 diabetes.
AI can now detect the transition from relapsing-remitting multiple sclerosis to secondary progressive multiple sclerosis.
An innovative mobile health application may help adults with Sickle Cell Disease manage their pain and reduce opioid use.
There is a revolutionary force in medicine that has the power to transform outcomes: artificial intelligence (AI).
A team trained a deep learning pose-recognition algorithm on video of infants in the NICU to identify key neurologic metrics.
Despite the recent advancements in artificial intelligence in medicine, it has yet to reduce physician workload and burnout.
Patients may be at risk if their phone settings change following updates to software or hardware.
“AI can offer a new paradigm in how we deliver IVF treatment and could lead to better outcomes for patients.”
For years, the industry has used machine learning — a form of AI — to predict which patients are most likely to have a ...
Dr. Von Nguyen on the Use of AI in Medicine.
Dr. Von Nguyen, Chief Clinical Officer at Evolent, discusses the use of AI and technology in medicine and population health.
In this analysis, the model performed best for predicting survival Asian patients and worst for Non-Hispanic Black patients.
Early detection of Alzheimer’s disease is important because it is when treatment is most effective.
Dr. Shamith Samarajiwa and his team developed a tool capable of identifying 13 cancer types, including breast, liver
According to the survey, 84.8% of US oncologists reported that they should be able to explain AI to use it.
The algorithm is a prediction model that pulls data such as vital signs, information from electronic health records and labs.
Assessing the rate of depression and anxiety using artificial intelligence (AI) and social media may produce more reliable
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