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. 

Patients whose blood glucose levels spike during surgery are at heightened risk for poor overall outcomes. A new AI tool has proven effective at predicting, prior to surgery, which patients will have the problem while under the knife.

Virtual reality isn’t quite there yet as a go-to screening tool for cognitive decline, but it can augment conventional methods. And senior citizens are open to its use for that purpose when it’s administered by their primary care doctor.

If geriatricians and primary care doctors could know which of their aging patients are at risk for Alzheimer’s disease, they could help these patients and their families prepare for what’s to come.

Allowing natural language processing to pore over disparate data stored in electronic health records, researchers in Canada have shown the AI-based technology can reveal real-world experiences and outcomes of patients with stage III breast cancer.

A Harvard-affiliated academic data science center is partnering with a major manufacturer of portable ultrasound systems to boost the diagnostic powers of point-of-care ultrasound, aka “POCUS,” using AI.