An AI Tool Grounded in Evidence-Based Medicine Outperformed Other AI Tools
A powerful clinical artificial intelligence tool developed by University at Buffalo biomedical informatics researchers has demonstrated remarkable accuracy on all three parts of the United States Medical Licensing Exam (Step exams), according to a paper published in JAMA Network Open.
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Generative AI's Diagnostic Capabilities Comparable to Non-Dpecialist Doctors
The use of generative AI for diagnostics has attracted attention in the medical field and many research papers have been published on this topic. However, because the evaluation criteria were different for each study, a comprehensive analysis was needed to determine the extent AI could be used in actual medical settings and what advantages it featured in comparison to doctors.
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Diagnoses and Treatment Recommendations Given by AI were More Accurate than those of Physicians
A new study led by Prof. Dan Zeltzer, a digital health expert from the Berglas School of Economics at Tel Aviv University, compared the quality of diagnostic and treatment recommendations made by artificial intelligence (AI) and physicians at Cedars-Sinai Connect, a virtual urgent care clinic in Los Angeles, operated in collaboration with Israeli startup K Health. The paper was published in Annals of Internal Medicine and presented at the annual conference of the American College of Physicians (ACP).
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New System for the Early Detection of Autism
A team from the Human-Tech Institute-Universitat Politècnica de València has developed a new system for the early detection of Autism Spectrum Disorder (ASD) using virtual reality and artificial intelligence. The system has achieved an accuracy of over 85%, thus surpassing traditional methods of detecting autism in early childhood, which are usually based on psychological tests and interviews carried out manually.
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AI Tool can Track Effectiveness of Multiple Sclerosis Treatments
A new artificial intelligence (AI) tool that can help interpret and assess how well treatments are working for patients with multiple sclerosis (MS) has been developed by UCL researchers.
AI uses mathematical models to train computers using massive amounts of data to learn and solve problems in ways that can seem human, including to perform complex tasks like image recognition.
As artificial intelligence (AI) rapidly integrates into health care, a new study by researchers at the Icahn School of Medicine at Mount Sinai reveals that all generative AI models may recommend different treatments for the same medical condition based solely on a patient's socioeconomic and demographic background.
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Multi-Resistance in Bacteria Predicted by AI Model
An AI model trained on large amounts of genetic data can predict whether bacteria will become antibiotic-resistant. The new study shows that antibiotic resistance is more easily transmitted between genetically similar bacteria and mainly occurs in wastewater treatment plants and inside the human body.
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