The AI development team was guided by a sports-medicine specialist dubbed “the go-to orthopedic surgeon for many of the greatest athletes on the planet.”

More than one-quarter of the U.S. adult population has Gastroesophageal Reflux Disease, or GERD, and the condition saddles as many as 20% of its sufferers with Barrett’s esophagus. The latter is a serious risk factor for esophageal cancer.

A convolutional neural network has proven adept at predicting the spread of colorectal cancer to the lymph nodes using automated analysis of histology slides.

Harvard researchers have demonstrated a web-based program that allows cognitively normal individuals to screen themselves, unsupervised, for the type of memory decline that may signal encroaching Alzheimer’s disease.

Is the world ready for AI-equipped smartphones that can reliably identify or rule out COVID-19 based on audio data from coughing and speaking?

In cancer research settings, AI has shown strong capabilities for predicting risk, recurrence and survivability over the past 10-plus years. Yet real-world cancer mortality remains largely unchanged to the present day. Why is that?

If AI for medical diagnostics is to lift the health status of populations—and thus fulfill its implicit global promise—it’s going to need stronger regulatory guidance than it’s gotten to date. 

AI should not be used to predict the course of clinical depression if all it has to work with are new patients’ medical records of past diagnoses, medications, encounters and patient-reported outcomes.

Researchers in the U.S. and China have meshed AI with blood testing and CT lung imaging to accurately predict which newly diagnosed COVID-19 patients will need a mechanical ventilator.

AI has shown strong potential for predicting which recently hospitalized patients will develop pressure injuries (PIs), also known as pressure ulcers or bedsores, if they aren’t treated early with preventive medicine.

After training deep neural networks on around 4,000 slide images from around 40 biopsied kidney patients, UCLA engineers have virtually re-stained tissue images for speedier high-accuracy diagnostics than a human histotechnologist could support.  

Researchers have achieved accuracies of 99.4% and 94.3% in two algorithmic methods for monitoring, diagnosing or ruling out Parkinson’s disease going only by individuals’ spoken words.

Around the web

Claritas claims its product can “significantly” bolster quality without altering images, helping docs diagnose faster and reduce bottlenecks

An average POCUS encounter cost about $121 compared to $339 for formal sonographic evaluation, NYU ortho experts reportedly recently. 

More than 40% surveyed said the cash card played an important role in their decision, experts explained in JAMA Internal Medicine.

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