The first fully human-capable AI agents for healthcare are now being used across the country. Artificial intelligence is gaining more of parents' trust than actual doctors. That's according to a new study from the University of Kansas Life Span Institute, which found that parents seeking information on their children's health are turning to AI more than human health care professionals. The research, published in the Journal of Pediatric Psychology, also revealed that parents rate AI-generated text as "credible, moral and trustworthy." More than 100 parents ranging from 18 to 65 years old were asked to rate text generated by either a human doctor or ChatGPT (the AI chatbot made by OpenAI) under the supervision of an expert.

Jack Krawczyk discusses how Google Bard helps users connect and communicate -- and what the future holds for the platform. ChatGPT has been found to have shared inaccurate information regarding drug usage, according to new research. In a study led by Long Island University (LIU) in Brooklyn, New York, nearly 75% of drug-related, pharmacist-reviewed responses from the generative AI chatbot were found to be incomplete or wrong. In some cases, ChatGPT, which was developed by OpenAI in San Francisco and released in late 2022, provided "inaccurate responses that could endanger patients," the American Society of Health System Pharmacists (ASHP), headquartered in Bethesda, Maryland, stated in a press release. ChatGPT also generated "fake citations" when asked to cite references to support some responses, the same study also found.

Chris Winfield, founder of Understanding A.I., tells'Fox & Friends Weekend' host Will Cain about a study showing patients preferred medical answers from artificial intelligence over doctors. When it comes to answering medical questions, can ChatGPT do a better job than human doctors? It appears to be possible, according to the results of a new study published in JAMA Internal Medicine, led by researchers from the University of California San Diego. The researchers compiled a random sample of nearly 200 medical questions that patients posted on Reddit, a popular social discussion website, for doctors to answer. Next, they entered the questions into ChatGPT (OpenAI's artificial intelligence chatbot) and recorded its response.

Consumer Financial Protection Bureau (CFPB) issues policy statement on credit decisions based on complex algorithms. On May 26, the CFPB issued Circular 2022-03, which addresses an important question about algorithmic decision-making: "When creditors make credit decisions based on complex algorithms that prevent creditors from accurately identifying the specific reasons for denying credit or taking other adverse actions, do these creditors need to comply with the Equal Credit Opportunity Act's requirement to provide a statement of specific reasons to applicants against whom adverse action is taken?" The Circular says yes, compliance with ECOA and Regulation B is required even if complex algorithms (including AI and machine learning) make it difficult to accurately identify the specific reasons for taking the adverse action. Further, the Circular makes clear that those laws "do not permit creditors to use complex algorithms when doing so means they cannot provide the specific and accurate reasons for adverse actions." White House executive order calls for study of predictive algorithms used by law enforcement agencies.

It took Michelle Anderson a month to create her E3 2019 outfit. It took her another hour to put it on. She wore a wig with red Afro puffs, an army-green tactical vest and fake bloodstained bandage. She completed the look with medical gloves and a mask looped around her neck, then took one last look in the mirror before she headed out the door. She was dressed as Lifeline, a playable combat medic from the video game "Apex Legends."

DarwinAI, the explainable artificial intelligence (XAI) company, and Red Hat, the world's leading provider of open source solutions, announced a collaboration to accelerate the deployment of COVID-Net--a suite of deep neural networks for COVID-19 detection and risk stratification via chest radiography--to hospitals and other healthcare facilities. DarwinAI and Red Hat are also leveraging the expertise of a computation research group, the Fetal Neonatal Neuroimaging and Developmental Science Center (FNNDSC) at Boston Children's Hospital to better focus the software for real world clinical and research use. "The COVID-Net system is a promising tool, but needs to be coupled with a compelling GUI to be effective -- Boston Children's ChRIS framework and the Red Hat OpenShift platform provides an effective way to get COVID-Net into the hands of health care professionals on the front lines." Since the launch of COVID-Net by DarwinAI and the University of Waterloo's Vision and Imaging Processing (VIP) Lab, the project has continued to evolve with assistance, participation and collaboration from researchers and clinicians around the world. The initiative eventually led to a collaboration between DarwinAI and Red Hat, using underlying technology from Boston Children's, the number one pediatric hospital in the nation.

Their most recent research results, published in the journal Nature Machine Intelligence, suggest that autonomous systems like the image-guided robotic device could outperform people on some complex medical tasks. Medical robots could reduce injuries and improve the efficiency and outcomes of procedures, as well as carry out tasks with minimal supervision when resources are limited. This would allow health care professionals to focus more on other critical aspects of medical care and enable emergency medical providers to bring advanced interventions and resuscitation efforts to remote and resource-limited areas. "Using volunteers, models and animals, our team showed that the device can accurately pinpoint blood vessels, improving success rates and procedure times compared with expert health care professionals, especially with difficult to access blood vessels," said senior author Martin L. Yarmush, Paul & Mary Monroe Chair & Distinguished Professor in the Department of Biomedical Engineering in the School of Engineering at Rutgers University-New Brunswick. Getting access to veins, arteries and other blood vessels is a critical first step in many diagnostic and therapeutic procedures.

Rutgers engineers have created a tabletop device that combines a robot, artificial intelligence and near-infrared and ultrasound imaging to draw blood or insert catheters to deliver fluids and drugs. Their most recent research results, published in the journal Nature Machine Intelligence, suggest that autonomous systems like the image-guided robotic device could outperform people on some complex medical tasks. Medical robots could reduce injuries and improve the efficiency and outcomes of procedures, as well as carry out tasks with minimal supervision when resources are limited. This would allow health care professionals to focus more on other critical aspects of medical care and enable emergency medical providers to bring advanced interventions and resuscitation efforts to remote and resource-limited areas. "Using volunteers, models and animals, our team showed that the device can accurately pinpoint blood vessels, improving success rates and procedure times compared with expert health care professionals, especially with difficult to access blood vessels," said senior author Martin L. Yarmush, Paul and Mary Monroe Chair and Distinguished Professor in the Department of Biomedical Engineering in the School of Engineering at Rutgers University-New Brunswick.

Over the last decade we have seen a transformation in global pharma's commercial model as the industry rightsized itself from the blockbuster era of the '90s and early millennia. The era of competing on share of voice enabled by armies of sales representatives calling on health care professionals evolved into smarter and more sophisticated strategies for deploying sales and marketing resources--and doing more with less. Over the same period, we have witnessed a significant evolution in the rise of advanced analytics and especially the talk and the promise of Artificial Intelligence (AI) in commercial pharma, so it's time to ask the question--Can AI really help pharma proposer? McKinsey tends to think so. In a report entitled "Artificial Intelligence in Business", they concluded that AI and analytics would contribute $440 Billion in potential annual value in the pharmaceutical and medical device sector, of which the major share of over $200B resulted in value released from marketing and sales.

THURSDAY, Sept. 26, 2019 (HealthDay News) -- Early evidence suggests that diagnostic performance of deep learning models is equivalent to that of health care professionals for interpreting medical imaging, according to a study published online Sept. 25 in The Lancet Digital Health. Xiaoxuan Liu, M.B.Ch.B., from the University Hospitals Birmingham NHS Foundation Trust in the United Kingdom, and colleagues conducted a systematic review and meta-analysis to assess the diagnostic accuracy of deep learning algorithms versus health care professionals in classifying disease using medical imaging. Binary diagnostic accuracy data were extracted and contingency tables were constructed to derive the outcomes of interest: sensitivity and specificity. Data from 82 studies, describing 147 patient cohorts were included. The researchers found that based on 69 studies, sensitivity ranged from 9.7 to 100 percent and specificity ranged from 38.9 to 100 percent.