Complex digital images of tissue samples that can take an experienced pathologist up to 20 minutes to annotate could be analysed in just one minute using a new AI tool developed by researchers at the University of Cambridge.

SMMILe, a machine learning algorithm, is able not only to correctly detect the presence of cancer cells on slides taken from biopsies and surgical sections,

A new heart monitoring system combining 3D printing and artificial intelligence (AI) could transform the way doctors measure and diagnose patients' heart health.

Developed at SFU’s School of Mechatronic Systems Engineering, the system features reusable dry 3D-printed electrodes embedded in a soft chest belt - the folding origami-shaped design uses gentle suction to stick to the skin.

A study published in the Journal of Critical Care, conducted with the participation of the D’Or Institute for Research and Education (IDOR), investigated how to measure efficiency in the use of resources for patients with severe community-acquired pneumonia (CAP), an illness contracted outside hospital settings and most common among older adults.

Large language models can help improve questionnaires used to diagnose mental illness by optimizing symptom generalizability and reducing redundancy. They can even contribute to new conceptualizations of mental disorders. That is the result of an international study led by Professor Dr Joseph Kambeitz and Professor Dr Kai Vogeley from the University of Cologne’s Faculty of Medicine and University Hospital Cologne.

Driven by the rapid advancements in artificial intelligence, computational pathology is emerging as a critical engine in the era of precision oncology. Traditional computational pathology primarily relies on task-specific models, which require the development of independent models for each distinct task.

In the UK, there was a case where TGN1412, an immunotherapy under development, triggered a cytokine storm within hours of administration to humans, leading to multiple organ failure. Another example, Aptiganel, a stroke drug candidate, was also highly effective in animals but was discontinued in humans due to side effects such as hallucinations and sedation.

Artificial intelligence (AI) and "protein language" models can speed the design of monoclonal antibodies that prevent or reduce the severity of potentially life-threatening viral infections, according to a multi-institutional study led by researchers at Vanderbilt University Medical Center.