New Method for Intentional Control of Bionic Prostheses

Despite enormous progress in the past two decades, the intentional control of bionic prostheses remains a challenge and the subject of intensive research. Now, scientists at the Medical University of Vienna and Imperial College London have developed a new method for precisely detecting the nerve signals remaining after an arm amputation and utilising them to control an artificial arm.

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Generative AI could be Transformative in Mental Health Care

New work by a University of Illinois Urbana-Champaign scholar harnesses the power of generative artificial intelligence, using it in tandem with measurement-based care and access-to-care models in a simulated case study, creating a novel framework that promotes personalized mental health treatment, addresses common access barriers and improves outcomes for diverse individuals.

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The First App for Selecting the Most Effective AI Models for Treatment of an Individual Organ

A team from the Department of Electronics, Information and Bioengineering of the Politecnico di Milano, led by dr. Andrea Moglia, has developed the first online application that helps us understand which Artificial Intelligence model is best suited to create 3D images of every individual organ. This makes treatment of the patient more accurate and reliable.

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Making Cancer Vaccines More Personal

In a new study, University of Arizona researchers created a model for cutaneous squamous cell carcinoma, a type of skin cancer, and identified two mutated tumor proteins, or neoantigens, that contain features of good candidates for a vaccine. At the same time, they used artificial intelligence to create 3D models to help them understand and predict which neoantigens could provoke T cells, a type of white blood cell critical to the immune system, to attack the cancer.

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AI Tool Beats Humans at Detecting Parasites in Stool Samples

Scientists at ARUP Laboratories have developed an artificial intelligence (AI) tool that detects intestinal parasites in stool samples more quickly and accurately than traditional methods, potentially transforming how labs diagnose parasitic infections around the world.

Identifying parasites under the microscope has long been a painstaking task requiring highly trained experts to manually scour each sample for telltale cysts, eggs or larva.

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Do Fitness Apps do More Harm than Good?

A study published in the British Journal of Health Psychology reveals the negative behavioral and psychological consequences of commercial fitness apps reported by users on social media. These impacts may undermine the potential of apps to promote health and wellbeing.

When investigators used artificial intelligence (AI) using a method called Machine-Assisted Topic Analysis (MATA), which combines AI-powered topic modelling with human qualitative analysis,

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A New AI Model Improves the Prediction of Breast Cancer Recurrence

Breast cancer is the most commonly diagnosed form of cancer in the world among women, with more than 2.3 million cases a year, and continues to be one of the main causes of cancer-related mortality. Precisely predicting whether this type of tumour will reappear remains one of the key challenges in oncology. To try and make progress in this field, an international team led by the Universitat Rovira i Virgili has developed an artificial intelligence model that brings together medical imaging data and clinical information to calculate the risk of tumour recurrence in a much more accurate and interpretative way.

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AI can Better Predict Future Risk for Heart Attack Patients

A landmark study led by University' experts has shown that artificial intelligence can better predict how doctors should treat patients following a heart attack.

The study, conducted by an international team of researchers, led by the University of Leicester’s Honorary fellow, Doctor Florian Wenzl working closely with Professor David Adlam, both from the Department of Cardiovascular Sciences, has been published in The Lancet Digital Health.

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AI System Finds Crucial Clues for Diagnoses in Electronic Health Records

Doctors often must make critical decisions in minutes, relying on incomplete information. While electronic health records contain vast amounts of patient data, much of it remains difficult to interpret quickly - especially for patients with rare diseases or unusual symptoms.

Now, researchers at the Icahn School of Medicine at Mount Sinai and collaborators have developed an artificial intelligence system, called InfEHR, that links unconnected medical events over time, creating a diagnostic web that reveals hidden patterns.

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New AI Tool Makes Medical Imaging Process 90% more Efficient

When doctors analyze a medical scan of an organ or area in the body, each part of the image has to be assigned an anatomical label. If the brain is under scrutiny for instance, its different parts have to be labeled as such, pixel by pixel: cerebral cortex, brain stem, cerebellum, etc. The process, called medical image segmentation, guides diagnosis, surgery planning and research.

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