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Smart Device Uses AI and Bioelectronics to Speed Up Wound Healing Process

As a wound heals, it goes through several stages: clotting to stop bleeding, immune system response, scabbing, and scarring.

A wearable device called "a-Heal," designed by engineers at the University of California, Santa Cruz, aims to optimize each stage of the process. The system uses a tiny camera and AI to detect the stage of healing and deliver a treatment in the form of medication or an electric field.

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Dartford and Gravesham Implements Clinisys ICE for Radiology and Pathology Orders

Dartford and Gravesham NHS Trust has taken a significant step towards a more digital future by rolling out electronic test ordering using Clinisys ICE.

The trust deployed the order communications system to around 2,000 clinical users across all of its wards, the maternity and paediatrics units, the emergency and same day emergency care departments, and outpatients in June.

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ChatGPT 4o Therapeutic Chatbot 'Amanda' as Effective as Jurnaling for Relationship Support

One of the first randomized controlled trials assessing the effectiveness of a large language model (LLM) chatbot 'Amanda' for relationship support shows that a single session of chatbot therapy can be as beneficial as a evidence-based journaling in assisting with relationship conflict resolution, according to a study published in the open-access journal PLOS Mental Health

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AI Tools Help Predict Severe Asthma Risks in Young Children

Mayo Clinic researchers have developed artificial intelligence (AI) tools that help identify which children with asthma face the highest risk of serious asthma exacerbation and acute respiratory infections. The study, published in the Journal of Allergy and Clinical Immunology, found the tools can detect those risks as early as age 3.

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AI Model Forecasts Disease Risk Decades in Advance

Imagine a future where your medical history could help predict what health conditions you might face in the next two decades. Researchers have developed a generative AI model that uses large-scale health records to estimate how human health may change over time. It can forecast the risk and timing of over 1,000 diseases and predict health outcomes over a decade in advance.

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AI Model Indicates Four out of Ten Breast Cancer Patients could Avoid Axillary Surgery

A project at Lund University in Sweden has trained an AI model to identify breast cancer patients who could be spared from axillary surgery. The model analyses previously unutilised information in mammograms and pinpoints with high accuracy the individual risk of metastasis in the armpit. A newly completed study shows that the model indicates that just over 40 per cent of today’s axillary surgery procedures could be avoided.

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AI-Powered CRISPR could Lead to Faster Gene Therapies

Stanford Medicine researchers have developed an artificial intelligence (AI) tool to help scientists better plan gene-editing experiments. The technology, CRISPR-GPT, acts as a gene-editing “copilot” supported by AI to help researchers - even those unfamiliar with gene editing - generate designs, analyze data and troubleshoot design flaws.

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Groundbreaking AI Aims to Speed Lifesaving Therapies

To solve a problem, we have to see it clearly.

Whether it’s an infection by a novel virus or memory-stealing plaques forming in the brains of Alzheimer’s patients, visualizing disease processes in the body is the first step toward alleviating human suffering.

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AI Spots Hidden Signs of Depression in Students' Facial Expressions

Depression is one of the most common mental health challenges, but its early signs are often overlooked. It is often linked to reduced facial expressivity. However, whether mild depression or subthreshold depression (StD) (a mild state of depressive symptoms that does not meet the criteria for diagnosis but is a risk factor for developing depression) is associated with changes in facial expressions remains unknown.

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Using Deep Learning for Precision Cancer Therapy

Altuna Akalin and his team at the Max Delbrück Center have developed a new tool to more precisely guide cancer treatment. Described in a paper published in Nature Communications, the tool, called Flexynesis, uses deep neural networks and evaluates multi modal data.

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