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Contactless Pulse Measurement Falters at High Heart Rates

Researchers at Bielefeld University have analyzed how reliably AI methods can detect pulse rates from simple video recordings. The technique, known as rPPG - remote photoplethysmography, an optical form of contactless pulse measurement - is considered a future tool of telemedicine. But a new study in the journal npj Digital Medicine reveals clear weaknesses as soon as heart rates rise.

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Bayer and Cradle Enter Collaboration to Enhance AI-Enabled Antibody Discovery and Optimization

Bayer and Cradle announced that they have entered a three-year strategic collaboration to deploy Cradle’s scientific AI software platform for protein engineering. As part of the collaboration, Bayer will integrate the generative AI platform into existing R&D workflows to enhance lead generation and optimization across its therapeutic antibody pipeline.

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New AI Model Predicts Disease Risk while You Sleep

A poor night’s sleep portends a bleary-eyed next day, but it could also hint at diseases that will strike years down the road. A new artificial intelligence model developed by Stanford Medicine researchers and their colleagues can use physiological recordings from one night’s sleep to predict a person’s risk of developing more than 100 health conditions.

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AI Gives a Clearer Picture of Functional MRI Brain Data

Obtaining clearer functional MRI data about the brain and its disorders is possible using artificial intelligence (AI), according to Boston College researchers who reported recently in Nature Methods that they developed an AI-assisted method to remove “noise”, or image distortions, caused by movement, heartbeat, and other factors.

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Machine Learning Drives Drug Repurposing for Neuroblastoma

Using machine learning and a large volume of data on genes and existing drugs, researchers at Lund University in Sweden have identified a combination of statins and phenothiazines that is particularly promising in the treatment of the aggressive form of neuroblastoma. The results from experimental trials showed slowing of tumour growth and higher survival rates.

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Novel AI Tool Offers Prognosis for Patients with Head and Neck Cancer

A team led by investigators at Mass General Brigham and Dana-Farber Cancer Institute has developed and validated an artificial intelligence (AI)-based noninvasive tool that can predict the likelihood that a patient’s oropharyngeal cancer - a type of head and neck cancer that develops in the throat - will spread, thereby signaling which patients should receive aggressive treatment.

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New EPIVINF Study Highlights a Promising Model for Long COVID Research

Researchers from IRTA and IrsiCaixa, partners in the EPIVINF project, have identified the golden Syrian hamster as a potential relevant model for studying the biology of long COVID, also known as post-COVID-19 condition (PCC).

Long COVID is not a single disease but a complex syndrome involving a range of persistent symptoms. People suffering from it report overwhelming fatigue, memory lapses, "brain fog,"

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AI-Powered Wearable Boosts Preventative Care for Elderly

University of Arizona researchers in the Gutruf Lab have developed a comfortable, easy-to-use wearable device that incorporates artificial intelligence (AI) to detect subtle warning signs of frailty, signifying a leap forward in elderly care.

"The current model of care is lagging behind," said Philipp Gutruf, associate department head of biomedical engineering and senior author on the study.

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AI could Help Predict Nutrition Risks in ICU Patients

A new study by researchers at the Icahn School of Medicine at Mount Sinai suggests that artificial intelligence (AI) could help predict which critically ill patients on ventilators are at risk of underfeeding, potentially enabling clinicians to adjust nutrition early and improve patient care. Details of the study were published in the online issue of Nature Communications.

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Open Call DIGITAL-2026-AI-09-SOLUTIONS-CANCER-STEP: Deployment of Cutting-Edge Multi-Modal AI-Based Solutions in Medical Imaging

By building on, extending and leveraging the Cancer Image Europe platform, this action is expected to facilitate the uptake of EU AI-driven solutions in medical imaging (including Machine Learning and Generative AI), towards their deployment in clinical settings. It should also include upskilling of healthcare professionals and evidence generation to evaluate the performance of the deployed AI-driven solutions and engage patients.

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