University of Florida: Study reveals bias in AI tools when diagnosing women’s health issue. “Machine learning algorithms designed to diagnose a common infection that affects women showed a diagnostic bias among ethnic groups, University of Florida researchers found.”
Newswise: AI more accurately identifies patients with advanced lung cancer that respond to immunotherapy and helps doctors select treatments. “Treatment planning for lung cancer can often be complex due to variations in assessing immune biomarkers. In a new study, Yale Cancer Center researchers at Yale School of Medicine used artificial intelligence (AI) tools and digital pathology to improve the accuracy of this process.”
University of Pittsburgh: New AI Tool Beats Standard Approaches for Detecting Heart Attacks. “A new machine learning model uses electrocardiogram (ECG) readings to diagnose and classify heart attacks faster and more accurately than current approaches, according to a study led by University of Pittsburgh researchers that published today in Nature Medicine.”
MIT News: Gamifying medical data labeling to advance AI. “Through the app, users review anything from images of potentially cancerous skin lesions or audio clips of heart and lung sounds that could indicate a problem. If the users are accurate, Centaur uses their opinions and awards them small cash prizes. Those opinions, in turn, help medical AI companies train and improve their algorithms.”
Newswise: Researchers test AI-powered chatbot’s medical diagnostic ability. “In a recent experiment published in JAMA, physician-researchers at Beth Israel Deaconess Medical Center (BIDMC) tested one well-known publicly available chatbot’s ability to make accurate diagnoses in challenging medical cases. The team found that the generative AI, Chat-GPT 4, selected the correct diagnosis as its top diagnosis nearly 40 percent of the time and provided the correct diagnosis in its list of potential diagnoses in two-thirds of challenging cases.”
Ars Technica: Ultra low-cost smartphone attachment measures blood pressure at home. “Given that 47 percent of adults in the US alone have hypertension, keeping on top of your blood pressure readings is a smart thing to do. And doing so could become much more convenient, requiring nothing more than your phone and an $0.80 piece of plastic, thanks to new research from the University of California, San Diego.”
Purdue University: AI-driven mobile health algorithm uses phone camera to detect blood vessel oxygen levels . “You may already use your smartphone for remote medical appointments. Why not use some of the onboard sensors to gather medical data? That’s the idea behind AI-driven technology developed at Purdue University that could use a smartphone camera to detect and diagnose medical conditions like anemia faster and more accurately than highly specialized medical equipment being developed for the task.”
Drexel University: Q+A: How Can Artificial Intelligence Help Doctors Compare Notes To Improve Diagnoses? . “Spotting patterns among patient records can allow physicians to better diagnose and treat their patients. With access to more records, and more time to parse them, it’s possible that these health care providers could identify and provide better treatments for conditions that have been particularly elusive to diagnoses. Recent developments in artificial intelligence and natural language processing programs are making it possible to glean information from volumes of electronic health records — giving doctors an important new tool to help patients.”
Medical XPress: AI tool outperforms human emergency call handlers in identifying stroke, new study shows. “A team of researchers from Denmark has developed a new artificial intelligence (AI) framework to address the number of strokes that go unrecognized by human emergency call handlers. The framework outperformed emergency call handlers in recognizing stroke for both sexes and across all age groups studied, indicating its potential as a supplementary tool for early and precise stroke identification in the future.”
Cornell Chronicle: AI tool gains doctors’ trust by giving advice like a colleague. “A new study led by Qian Yang, assistant professor of information science in the Cornell Ann S. Bowers College of Computing and Information Science, suggests that if AI tools can counsel the doctor like a colleague – pointing out relevant biomedical research that supports the decision – then doctors can better weigh the merits of the recommendation.” As long as they don’t hallucinate the citations, I suppose…
University College London: Detecting anaemia earlier in children using a smartphone. “Researchers at UCL and University of Ghana have successfully predicted whether children have anaemia using only a set of smartphone images.”
Harvard Gazette: Using AI to target Alzheimer’s. “Although investigators have made strides in detecting signs of Alzheimer’s disease using high-quality brain imaging tests collected as part of research studies, a team at Massachusetts General Hospital recently developed an accurate method that relies on routinely collected clinical brain images. The advance could lead to more accurate diagnoses.”
ScienceDaily: Virtual reality game to objectively detect ADHD. “Researchers have used virtual reality games, eye tracking and machine learning to show that differences in eye movements can be used to detect ADHD, potentially providing a tool for more precise diagnosis of attention deficits. Their approach could also be used as the basis for an ADHD therapy, and with some modifications, to assess other conditions, such as autism.”
Newswise: Smartphone Video Motion Analysis Detected Narrowed Neck Arteries That May Lead to Stroke. “Motion analysis of video recorded on a smartphone accurately detected narrowed arteries in the neck, which are a risk factor for stroke, according to new research published today in the Journal of the American Heart Association, an open access, peer-reviewed journal of the American Heart Association.”
Johns Hopkins: AI Speeds Sepsis Detection To Prevent Hundreds Of Deaths. “Patients are 20% less likely to die of sepsis because of a new AI system developed at Johns Hopkins University that catches symptoms hours earlier than traditional methods, an extensive hospital study demonstrates.”
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