Diagnostics

Fifty-three percent of physicians say they are optimistic about AI’s potential effect on healthcare, according to a new survey of more than 1,700 physicians published by the Doctors Company.

Machine learning can help electrophysiologists or other heart specialists decide whether a patient is a good candidate for a pacemaker or implantable cardioverter defibrillator, according to a study published Oct. 3 in PLOS One.

If a newly tested AI system for reading chest X-rays achieves widespread adoption, patients presenting in the ER with symptoms of pneumonia can expect an up or down diagnosis—and with it the start of a treatment plan—in 10 seconds.

Researchers in Germany have developed an AI-based method for identifying pediatric acute appendicitis using biomarkers like blood and protein readings obtained in routine lab tests.

Kheiron Medical Technologies, a London-based machine learning startup focused on helping radiologists detect cancer at an early stage, has raised $22 million in a Series A funding round.

Machine learning analysis of Raman hyperspectroscopy—a technology used to measure the intensity of scattered laser light—has shown strong potential as a screening tool for Alzheimer’s disease when applied to an easily obtainable lab specimen: saliva.

Emerging technologies like AI and robotics have vast potential to improve healthcare. Few question this. What remains unclear is how meaningful the advances will be to healthcare providers and, more to the point, the patients they serve.

Geisinger has tapped IBM’s AI expertise and come up with a way to predict hospital patients’ risk of sepsis. In the process, the method can increase chances of survival in those who have the tricky and potentially life-threatening condition.

A single heartbeat is all a new neural-network technique needs to detect heart failure with 100% accuracy, according to a study slated for January 2020 publication in Biomedical Signal Processing and Control Journal.

Machine learning algorithms can comb population-level patient databases to find individuals who might benefit by treatment for depression.

Machine learning is no better than physicians at predicting acute kidney injury (AKI) in the ICU, where it’s a sign of poor outcomes ahead as soon as it appears. However, the AI approach can help mitigate physicians’ tendency to overestimate risks and overtreat low-risk patients.

One of the medical specialties highly hopeful in AI’s potential to guide care is neurosurgery. That’s because patients with traumatic brain injuries often present care teams and family members with an especially thorny decision.