The Trump administration is pushing an initiative for millions of Americans to upload personal health data and medical records on new apps and systems run by private tech companies, promising easier to access health records and wellness monitoring. Donald Trump is expected to deliver remarks on the initiative on Wednesday afternoon in the East Room. The event is expected to involve leaders from more than 60 companies, including major tech companies such as Google and Amazon, as well as prominent hospital systems like the Cleveland clinic. The new system will focus on diabetes and weight management, conversational artificial intelligence that helps patients, and digital tools such as QR codes and apps that register patients for check-ins or track medications. The initiative, spearheaded by an administration that has already freely shared highly personal data about Americans in ways that have tested legal bounds, could put patients' desires for more convenience at their doctor's office on a collision course with their expectations that their medical information be kept private.

Large language models with a transformer-based encoder/decoder architecture, such as T5, have become standard platforms for supervised tasks. To bring these technologies to the clinical domain, recent work has trained new or adapted existing models to clinical data. However, the evaluation of these clinical T5 models and comparison to other models has been limited. Are the clinical T5 models better choices than FLAN-tuned generic T5 models? Do they generalize better to new clinical domains that differ from the training sets? We comprehensively evaluate these models across several clinical tasks and domains. We find that clinical T5 models provide marginal improvements over existing models, and perform worse when evaluated on different domains. Our results inform future choices in developing clinical LLMs.

What use could healthcare have for someone who makes things up, can't keep a secret, doesn't really know anything, and, when speaking, simply fills in the next word based on what's come before? Lots, if that individual is the newest form of artificial intelligence, according to some of the biggest companies out there. Companies pushing the latest AI technology -- known as "generative AI" -- are piling on: Google and Microsoft want to bring types of so-called large language models to healthcare. Big firms that are familiar to folks in white coats -- but maybe less so to your average Joe and Jane -- are equally enthusiastic: Electronic medical records giants Epic and Oracle Cerner aren't far behind. The space is crowded with startups, too.

At the latest Fortune Brainstorm Health virtual discussion on Wednesday, experts from various parts of the medical field said that once these impediments are overcome, A.I. could be the key to improving patient outcomes, lowering overall costs, and reducing burnout and stress on overworked caregivers. One of the first steps, they agreed, is breaking down the barriers that prevent the collection and sharing of accurate, unbiased data. "It's perhaps the most important question of the day: how do we get systems to talk with each other?" said Dr. David Gruen, the chief medical officer of imaging at Merative. "[A.I.] has a broad concept of interoperability. How do we trust the data? How do we get unbiased data? How do we pull together the data that we have in our arms or in the apps on our phones into our health system's record so that we really get a comprehensive picture? We believe that that's going to be a huge hurdle [overcome] when we convince people that this is cost-saving, data-enhancing, and outcome-improving."

Since the earliest days of the COVID-19 pandemic, one of the biggest challenges for health systems has been to gain an understanding of the community spread of this virus and to determine how likely is it that a person walking through the doors of a facility is at a higher risk of being COVID-19 positive. Without adequate access to testing data, health systems early-on were often forced to rely on individuals to answer questions such as whether they had traveled to certain high-risk regions. Even that unreliable method of assessing risk started becoming meaningless as local community spread took hold. Parkland Health & Hospital System, the safety net health system for Dallas County, Texas, and PCCI, a Dallas-based non-profit with expertise in the practical applications of advanced data science and social determinants of health, had a better idea. Community spread of an infectious disease is made possible through physical proximity and density of active carriers and non-infected individuals.

TechRepublic's Karen Roby spoke with Jay Roszhart of Memorial Health Center's Systems Ambulatory Group in Illinois about artificial intelligence (AI) in hospitals. The following is an edited transcript of their conversation. Karen Roby: The American Hospital Association estimates that hospitals have lost more than $200 billion because of the COVID-19 pandemic. Hospital leaders are always looking for ways to get patients back into doctors' offices and the hospitals in a safe and secure way. Talk a little bit just to start us off here about the population that you serve there in Illinois.

This article was first published on Friday, May 22, 2020 in Kaiser Health News. Dr. Albert Hsiao and his colleagues at the University of California-San Diego health system had been working for 18 months on an artificial intelligence program designed to help doctors identify pneumonia on a chest X-ray. When the coronavirus hit the United States, they decided to see what it could do. The researchers quickly deployed the application, which dots X-ray images with spots of color where there may be lung damage or other signs of pneumonia. It has now been applied to more than 6,000 chest X-rays, and it's providing some value in diagnosis, said Hsiao, the director of UCSD's augmented imaging and artificial intelligence data analytics laboratory.

Dr Albert Hsiao and his colleagues at the UC San Diego health system in the United States had been working for 18 months on an artificial intelligence (AI) program designed to help doctors identify pneumonia on a chest X-ray. When the coronavirus hit the United States, they decided to see what it could do. The researchers quickly deployed their program, which dots X-ray images with spots of colour where there may be lung damage or other signs of pneumonia. It has now been applied to more than 6,000 chest X-rays, and it's providing some value in diagnosis, said Dr Hsiao, the director of UCSD's augmented imaging and artificial intelligence data analytics laboratory. His team is one of several around the country that has pushed AI programs into the Covid-19 crisis to perform tasks like deciding which patients face the greatest risk of complications and which can be safely channeled into lower-intensity care.

Dr. Albert Hsiao and his colleagues at the University of California-San Diego health system had been working for 18 months on an artificial intelligence program designed to help doctors identify pneumonia on a chest X-ray. When the coronavirus hit the United States, they decided to see what it could do. His team is one of several around the country that has pushed AI programs developed in a calmer time into the COVID-19 crisis to perform tasks like deciding which patients face the greatest risk of complications and which can be safely channeled into lower-intensity care. The machine-learning programs scroll through millions of pieces of data to detect patterns that may be hard for clinicians to discern. Yet few of the algorithms have been rigorously tested against standard procedures.

Dr. Albert Hsiao and his colleagues at the University of California–San Diego health system had been working for 18 months on an artificial intelligence program designed to help doctors identify pneumonia on a chest X-ray. When the coronavirus hit the United States, they decided to see what it could do. The researchers quickly deployed the application, which dots X-ray images with spots of color where there may be lung damage or other signs of pneumonia. It has now been applied to more than 6,000 chest X-rays, and it's providing some value in diagnosis, said Hsiao, the director of UCSD's augmented imaging and artificial intelligence data analytics laboratory. His team is one of several around the country that has pushed AI programs developed in a calmer time into the COVID-19 crisis to perform tasks like deciding which patients face the greatest risk of complications and which can be safely channeled into lower-intensity care.