Diagnostics

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.

Screening for sepsis in children and babies has grown quickly over the past several years. As methods and approaches multiply, machine learning continues looking like an eventual first-line diagnostic option. 

Along with a curated and annotated image dataset, the share includes code, network architecture and trained model weights.

Teleophthalmology incorporating AI has a bright future in advanced vision care, and the potential indications branch out in many directions from screening for diabetic retinopathy. 

Going head-to-head against a small group of clinicians in 50 care episodes, an AI-based smartphone app has equaled or bested the humans at triaging patients to the most appropriate site of care.

Researchers have piloted a deep learning algorithm that can recognize visual cues of sickness, also known as “clinical gestalt,” in facial photos.

When a black-box algorithm guides a physician’s diagnostic or therapeutic judgments, its intrinsic opaqueness can confound subsequent steps toward clinical safety and efficacy—and that’s just for starters. 

Patients are strongly inclined to follow treatment instructions that combine innovative AI recommendations with a physician’s reassuring presence.

An AI tool for managing pediatric asthma shaved 7.8 minutes from the time pediatricians spent dealing with electronic health records in a randomized clinical trial conducted at the Mayo Clinic. 

Around the web

Two different companies announced that they are recalling all lots of the medication. 

CardioSmart, an online resource for both patients and clinicians, has a new editor. 

The funding includes $8.5 billion in American Rescue Plan resources for providers who treat Medicaid, Children's Health Insurance Program and Medicare patients.

   
   
   
   
   
Trimed Popup
Trimed Popup