Innovations

Using an electronic health record-based intervention has been shown to increase pre-exposure prophylaxis use among patients of primary care physicians caring for people with HIV.

The future of gaining clinical experience may change as virtual reality-based training could improve the ability of paediatric nurses to recognise impending respiratory failure.

New research has shown that a deep learning model may be able to predict disability and pain in patients up to 1 year after lumbar disc herniation surgery and to aid decision-making for patients and surgeons.

Brain scans of adolescents with and without ADHD were scrutinised through AI, uncovering disparities in the white matter tracts that could redefine our understanding of this prevalent neurodevelopmental condition.

Researchers have recently demonstrated that a fully automated AI approach can predict post-nephrectomy glomerular filtration rate with a level of accuracy comparable to validated clinical prediction models.

According to a recent study, artificial intelligence chatbots outperformed human clinicians in probabilistic reasoning when estimating the probability of a diagnosis following a negative test result.

Adding to a growing body of data regarding the use of AI in imaging, a recent study has measured Cobb angles accurately with a convolutional neural network in patients with idiopathic scoliosis.

The performance of a machine learning based model for diagnosing coronary artery disease has been investigated to improve recognition of risk factors for one of the most lethal cardiovascular diseases in the world.