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New Video Test for Parkinson's Uses AI to Track How the Disease is Progressing

A video-processing technique developed at the University of Florida that uses artificial intelligence will help neurologists better track the progression of Parkinson's disease in patients, ultimately enhancing their care and quality of life.

The system, developed by Diego Guarin, Ph.D., an assistant professor of applied physiology and kinesiology in the UF College of Health and Human Performance, applies machine learning to analyze video recordings of patients performing the finger-tapping test, a standard test for Parkinson's disease that involves quickly tapping the thumb and index finger 10 times.

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NIH Findings Shed Light on Risks and Benefits of Integrating AI into Medical Decision-Making

Researchers at the National Institutes of Health (NIH) found that an artificial intelligence (AI) model solved medical quiz questions - designed to test health professionals’ ability to diagnose patients based on clinical images and a brief text summary - with high accuracy. However, physician-graders found the AI model made mistakes when describing images and explaining how its decision-making led to the correct answer.

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Are AI-Chatbots Suitable for Hospitals?

Large language models may pass medical exams with flying colors but using them for diagnoses would currently be grossly negligent. Medical chatbots make hasty diagnoses, do not adhere to guidelines, and would put patients' lives at risk. This is the conclusion reached by a team from the Technical University of Munich (TUM). For the first time, the team has systematically investigated whether this form of artificial intelligence (AI) would be suitable for everyday clinical practice.

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Chatbot Iris Offers Individual Support

A packed auditorium with over 1000 students. This is not a rare sight in introductory informatics lectures. To meet the needs of each individual student under these conditions, Stephan Krusche, professor of Software Engineering, and his team have been building the Artemis learning platform since 2016. It resembles well-known learning platforms, but offers more possibilities.

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AI-Powered Oracle Clinical Digital Assistant Transforms Interactions Between Practitioners and Patients

Oracle announced general availability of the Oracle Clinical Digital Assistant for ambulatory clinics in the US. The mobile offering combines generative AI, clinical intelligence, multimodal voice and screen driven assistance, and simplified workflows into a single, unified solution that enables providers to give their full attention to the needs of their patients.

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Machine Learning Helps Define New Subtypes of Parkinson's Disease

Researchers at Weill Cornell Medicine have used machine learning to define three subtypes of Parkinson's disease based on the pace at which the disease progresses. In addition to having the potential to become an important diagnostic and prognostic tool, these subtypes are marked by distinct driver genes. If validated, these markers could also suggest ways the subtypes can be targeted with new and existing drugs.

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Beyond Algorithms: The Role of Human Empathy in AI-Enhanced Therapy

A new study by researchers from the Psychology Department at the Hebrew University have made significant strides in understanding the role of artificial intelligence (AI) in mental health therapy. Their research focuses on the delicate balance between AI-driven interactions and the irreplaceable human touch in therapeutic settings, addressing critical questions about when AI might effectively replace human therapists and when the human connection remains indispensable.

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AI Tool Successfully Responds to Patient Questions in Electronic Health Record

As part of a nationwide trend, many more of NYU Langone Health's patients during the pandemic started using electronic health record tools to ask their doctors questions, refill prescriptions, and review test results. Many patients’ digital inquiries arrived via a communications tool called In Basket, which is built into NYU Langone’s electronic health record (EHR) system, EPIC.

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Off-the-Shelf Wearable Trackers Provide Clinically-Useful Information for Patients with Heart Disease

Monitoring of heart rate and physical activity using consumer wearable devices was found to have clinical value for comparing the response to two treatments for atrial fibrillation and heart failure.

The study published in Nature Medicine examined if a commercially-available fitness tracker and smartphone could continuously monitor the response to medications, and provide clinical information similar to in-person hospital assessment.

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AI Outperforms Clinical Tests at Predicting Progress of Alzheimer's Disease

Cambridge scientists have developed an artificially intelligent (AI) tool capable of predicting in four cases out of five whether people with early signs of dementia will remain stable or develop Alzheimer's disease.

The team say this new approach could reduce the need for invasive and costly diagnostic tests while improving treatment outcomes early when interventions such as lifestyle changes or new medicines may have a chance to work best.

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