Researchers in Denmark and the U.S. have used deep neural networks to develop complementing models for predicting complications likely to arise in patients who’ve had surgeries of all kinds.
When assisted by an AI tool designed to organize and display digitized patient referral records, gastroenterologists cut their time to answer relevant clinical questions by 2.3 minutes.
Beginning around 2031, autonomous virtual assistants will deliver precision preventive medicine while networked provider orgs offer closely connected care via single, shared digital infrastructures.
Psychology researchers have used machine learning to wring useful two-year dementia trajectory predictions from more than 500 potentially contributing risk factors.
Emergency physicians have a tough time identifying patients who have Crohn’s disease and truly need a CT scan to pinpoint the cause of acute abdominal distress.
Comparing four methods for predicting septic shock in children hospitalized with sepsis, Johns Hopkins researchers have found a newer machine-learning approach superior to an older one as well as to two conventional methods.
Psychology researchers have demonstrated a way to finetune diagnoses of major depression and generalized anxiety disorder by analyzing freely elaborated thoughts and feelings using machine learning and natural language processing.
Clinical nutritionists won’t be left out of the medical AI revolution, as researchers are exploring use cases for augmented diet optimization, food image recognition, risk prediction and diet pattern analysis.
Healthcare AI has potential not only for neutralizing its inherent algorithmic bias but also for personalizing its outputs to help humans address health inequities.
The system hit 88% accuracy at optimizing stimulation settings, as confirmed by brain-response patterns on neuroimaging as well as visibly observable symptom improvement in patients with Parkinson’s disease.
