• Mashup Score: 0

    Objective To compare evaluations of depressive episodes and suggested treatment protocols generated by Chat Generative Pretrained Transformer (ChatGPT)-3 and ChatGPT-4 with the recommendations of primary care physicians. Methods Vignettes were input to the ChatGPT interface. These vignettes focused primarily on hypothetical patients with symptoms of depression during initial consultations. The creators of these vignettes meticulously designed eight distinct versions in which they systematically varied patient attributes (sex, socioeconomic status (blue collar worker or white collar worker) and depression severity (mild or severe)). Each variant was subsequently introduced into ChatGPT-3.5 and ChatGPT-4. Each vignette was repeated 10 times to ensure consistency and reliability of the ChatGPT responses. Results For mild depression, ChatGPT-3.5 and ChatGPT-4 recommended psychotherapy in 95.0% and 97.5% of cases, respectively. Primary care physicians, however, recommended psychotherapy in

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    • New research in @FMCHJournal suggests ChatGPT might outperform doctors in managing clinical depression by adhering to evidence-based guidelines, and avoiding gender and social class biases #AIinHealthcare #MentalHealth Read the full study here: https://t.co/FinMlikWz3 https://t.co/3mF05Jlrvk

  • Mashup Score: 4

    BY KIM BELLARD You may have seen that last week the California Public Utilities Commission (CPUC) gave approval for two companies to operate self-driving taxicabs (“robotaxis”) in San Francisco,…

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    • Both #SelfDrivingCars and #AIinHealthcare have a way to go, says @kimbbellard. But the future comes at us fast, he writes, so if we’re not preparing now we’ll be too late. Check out his thoughts on #Robotaxis now on @THCBstaff! https://t.co/LCBE079UB2

  • Mashup Score: 0
    Wolters Kluwer Health - 10 month(s) ago

    JavaScript Error JavaScript has been disabled on your browser. You must enable it to continue. Here’s how to enable JavaScript in the following browsers: Internet Explorer From the Tools menu, select Options Click the Content tab Select Enable JavaScript Firefox From the Tools…

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    • PODCAST: Dr. Ravi Parikh explores the potential for AI to enhance physician-patient communication during end-of-life care. Discover how this innovation can improve patient experience and reduce costs. #AIinhealthcare #endoflifecare https://t.co/VzCUjUhT7D https://t.co/mbl3ONx8DF

  • Mashup Score: 2

    The generalizability of predictive algorithms is of key relevance to application in clinical practice. We provide an overview of three types of generalizability, based on existing literature: temporal, geographical, and domain generalizability. These generalizability types are linked to their associated goals, methodology, and stakeholders.

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    • New research in npj Digital Medicine: Perspectives on validation of clinical predictive algorithms #digitalhealth #algorithms #aiinhealthcare #machinelearning #ai https://t.co/FGnlZnExN5

  • Mashup Score: 1
    Post | LinkedIn - 1 year(s) ago

    Worth a read. For those of us who have been in healthcare, medical informatics, and public sector health for 2+ decades and more, this is the most exciting time of our careers. If you are looking to break into healthcare products and health tech, or are getting started in your career – come on in. There’s much to do and we’re just getting started. Please follow the following leaders on LinkedIn…

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    • Leaders to follow for their unique perspectives on Healthcare and Informatics- compiled by the wonderful @natarpr https://t.co/rIxKQxE8eU #healthinformatics #publichealth #LifeSciences #AIinhealthcare #Physicianleaders