Research

New Tool Overcomes Major Hurdle in Clinical AI Design

Harvard Medical School scientists and colleagues at Stanford University have developed an artificial intelligence (AI) diagnostic tool that can detect diseases on chest X-rays directly from natural-language descriptions contained in accompanying clinical reports.

The step is deemed a major advance in clinical AI design because most current AI models require laborious human annotation of vast reams of data before the labeled data are fed into the model to train it.

AI Tool could Reduce Common Drug Side Effects

Research led by the University of Exeter and Kent and Medway NHS and Social Care Partnership Trust, published in Age and Ageing, assessed a new tool designed to calculate which medicines are more likely to experience adverse anticholinergic effects on the body and brain. These complications can occur from many -prescription and over-the-counter drugs which affects the brain by blocking a key neurotransmitter called acetylcholine.

AI Model Outperforms Clinicians in Diagnosing Pediatric Ear Infections

An artificial-intelligence (AI) model built at Mass Eye and Ear was shown to be significantly more accurate than doctors at diagnosing pediatric ear infections in the first head-to-head evaluation of its kind, a research team working to develop the model for clinical use reported.

AI Improves Treatment in Women with Heart Attacks

Heart attacks are one of the leading causes of death worldwide, and women who suffer a heart attack have a higher mortality rate than men. This has been a matter of concern to cardiologists for decades and has led to controversy in the medical field about the causes and effects of possible gaps in treatment.

AI Model can Detect Parkinson's from Breathing Patterns

Parkinson's disease is notoriously difficult to diagnose as it relies primarily on the appearance of motor symptoms such as tremors, stiffness, and slowness, but these symptoms often appear several years after the disease onset. Now, Dina Katabi, the Thuan (1990) and Nicole Pham Professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT and principal investigator at MIT Jameel Clinic

AI + ECG Heart Trace can Accurately Predict Diabetes and pre-Diabetes

An artificial intelligence (AI) algorithm, derived from the features of individual heartbeats recorded on an ECG (electrocardiogram), can accurately predict diabetes and pre-diabetes, suggests preliminary research published in the online journal BMJ Innovations.

If validated in larger studies, the approach could be used to screen for the disease in low resource settings, say the researchers.

No One-Size-Fits-All AI Approach Works for Prevention, Diagnosis or Treatment Using Precision Medicine

A Rutgers analysis of dozens of artificial intelligence (AI) software programs used in precision, or personalized, medicine to prevent, diagnose and treat disease found that no program exists that can be used for all treatments.

Precision medicine, a technology still in its infancy, is an approach to treatment that uses information about an individual's medical history and genetic profile and relates it to the information of many others to find patterns that can help prevent, diagnose or treat a disease.

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