Precision Medicine

Also called personalized medicine, this evolving field makes use of an individual’s genes, lifestyle, environment and other factors to identify unique disease risks and guide treatment decision-making.
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Ventilator support predictable by integrated, AI-inclusive diagramming

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.

Hospital-acquired bedsores avoidable with AI

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.

AI enables much faster pathology for life-or-death interventions

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.  

Steps taken toward smartphone app for automatically detecting Parkinson’s

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.

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Pediatric sepsis increasingly screenable by AI

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. 

AI charts course of care for chronic kidney disease

Researchers have used machine learning to accurately predict when a patient with chronic kidney disease will need dialysis. The technique may facilitate personalized care and optimized treatment planning.

5 ways AI stands to advance the state of burn care

AI has “remarkable potential” to improve diagnostic accuracy, care efficiency and workflow optimization in the surgical subspecialty of burn care.

AI innovators lauded for sharing data, questioned for making it open-access

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