Artificial intelligence could unlock the full potential of therapeutic and assistive exoskeletons by enabling autonomous operation, reducing dependence on therapists and caregivers. However, researchers emphasise that richer patient data and more accurate digital twins are essential before these technologies can achieve widespread clinical deployment and revolutionise rehabilitation outcomes.
Google DeepMind researchers have developed an asynchronous oversight system enabling AI to conduct diagnostic consultations whilst maintaining physician accountability. The guardrailed AMIE system demonstrated superior performance to nurse practitioners and primary care physicians in virtual clinical examinations whilst strictly deferring medical advice to supervising doctors.
Researchers have developed an artificial intelligence system that can estimate bone mineral density and detect osteoporosis from standard lumbar X-ray images, achieving sensitivity rates of 86.4% for osteopenia detection. The technology could transform routine clinical X-rays into powerful screening tools for bone health assessment.
Large Language Model (LLM) artificial intelligence systems increasingly deployed across healthcare settings pose serious risks to patient safety, with new research revealing these tools systematically recommend reduced medical care for women and vulnerable populations based on irrelevant factors such as typing errors and communication style rather than clinical need.
A comprehensive global survey commissioned by Philips has identified substantial trust disparities between healthcare professionals and patients regarding artificial intelligence implementation in medical care, with professionals showing significantly greater optimism about AI’s potential to improve patient outcomes.
Researchers from the VIB-KU Leuven Center for Brain & Disease Research have unveiled a predictive model that could revolutionise Alzheimer’s disease diagnosis and treatment strategies. Their study, published in Molecular Neurodegeneration, reveals how specific genetic mutations in three causal genes create a ‘molecular clock’ that accurately forecasts disease onset, potentially enabling clinicians to provide personalised […]
As artificial intelligence becomes increasingly sophisticated in mimicking human interaction, psychologists warn that intimate relationships with AI companions present significant ethical challenges ranging from disruption of human bonds to potential manipulation and exploitation of vulnerable users.
Biomedical engineers at Duke University have developed an artificial intelligence platform that can design short proteins capable of binding to and destroying previously untreatable disease-causing proteins. The breakthrough could revolutionise drug development for currently intractable diseases.
Vanderbilt researchers demonstrate effectiveness of automated alerts in prompting suicide risk screening, with interruptive alerts proving significantly more successful than passive notifications.
A multi-institutional team has developed a comprehensive suite of machine learning models that can accurately classify cancer samples into molecular subtypes, bridging a critical gap between genomic research discoveries and clinical implementation.
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