Albert Hsiao, M.D., and his colleagues at the University of California, San Diego (USCD) health system had been working for 18 months on anartificial intelligence programdesigned to help doctors identify pneumonia on a chest X-ray. When thecoronavirushit the U.S., 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 its providing some value in diagnosis, said Hsiao, director of UCSDs 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.

ASCO Explained: Expert predictions and takeaways from the world's biggest cancer meeting

Join FiercePharma for our ASCO pre- and post-show webinar series. We'll bring together a panel of experts to preview what to watch for at ASCO. Cancer experts will highlight closely watched data sets to be unveiled at the virtual meeting--and discuss how they could change prescribing patterns. Following the meeting, well do a post-show wrap up to break down the biggest data that came out over the weekend, as well as the implications they could have for prescribers, patients and drugmakers.

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. So while they often appear helpful, rolling out the programs in the midst of a pandemic could be confusing to doctors or even dangerous for patients, some AI experts warn.

AI is being used for things that are questionable right now, said Eric Topol, M.D., director of the Scripps Research Translational Institute and author of several books on health IT.

Topol singled out a system created byEpic, a major vendor of electronic health record software, that predicts which coronavirus patients may become critically ill. Using the tool before it has been validated is pandemic exceptionalism, he said.

Epic said the companys model had been validated with data from more 16,000 hospitalized COVID-19 patients in 21 healthcare organizations. No research on the tool has been published, but, in any case, it was developed to help clinicians make treatment decisions and is not a substitute for their judgment, said James Hickman, a software developer on Epics cognitive computing team.

Others see the COVID-19 crisis as an opportunity to learn about the value of AI tools.

My intuition is its a little bit of the good, bad and ugly, said Eric Perakslis, Ph.D., a data science fellow at Duke University and former chief information officer at the FDA. Research in this setting is important.

Nearly $2 billion poured into companies touting advancements in healthcare AI in 2019. Investments in the first quarter of 2020 totaled $635 million, up from $155 million in the first quarter of 2019, according to digital health technology funderRock Health.

At least three healthcare AI technology companies have made funding deals specific to the COVID-19 crisis, includingVida Diagnostics, an AI-powered lung-imaging analysis company, according to Rock Health.

Overall, AIs implementation in everyday clinical care is less common than hype over the technology would suggest. Yet the coronavirus crisis has inspired some hospital systems to accelerate promising applications.

UCSD sped up its AI imaging project, rolling it out in only two weeks.

Hsiaos project, with research funding from Amazon Web Services, the UC system and the National Science Foundation (NSF), runs every chest X-ray taken at its hospital through an AI algorithm. While no data on the implementation have been published yet, doctors report that the tool influences their clinical decision-making about a third of the time, said Christopher Longhurst, M.D., UCSD Healths chief information officer.

The results to date are very encouraging, and were not seeing any unintended consequences, he said. Anecdotally, were feeling like its helpful, not hurtful.

AI has advanced further in imaging than other areas of clinical medicine because radiological images have tons of data for algorithms to process, and more data make the programs more effective, said Longhurst.

But while AI specialists have tried to get AI to do things like predict sepsis and acute respiratory distressresearchers at Johns Hopkins Universityrecently won a NSF grantto use it to predict heart damage in COVID-19 patientsit has been easier to plug it into less risky areas such as hospital logistics.

In New York City, two major hospital systems are using AI-enabled algorithms to help them decide when and how patients should move into another phase of care or be sent home.

AtMount Sinai Health System, an artificial intelligence algorithm pinpoints which patients might be ready to be discharged from the hospital within 72 hours, said Robbie Freeman, vice president of clinical innovation at Mount Sinai.

Freeman described the AIs suggestion as a conversation starter, meant to help assist clinicians working on patient cases decide what to do. AI isnt making the decisions.

NYU Langone Healthhas developed a similar AI model. It predicts whether a COVID-19 patient entering the hospital will suffer adverse events within the next four days, said Yindalon Aphinyanaphongs, M.D., Ph.D., who leads NYU Langones predictive analytics team.

The model will be run in a four- to six-week trial with patients randomized into two groups: one whose doctors will receive the alerts, and another whose doctors will not. The algorithm should help doctors generate a list of things that may predict whether patients are at risk for complications after theyre admitted to the hospital, Aphinyanaphongs said.

Some health systems are leery of rolling out a technology that requires clinical validation in the middle of a pandemic. Others say they didnt need AI to deal with the coronavirus.

Stanford Health Careis not using AI to manage hospitalized patients with COVID-19, saidRon Li, M.D., the centers medical informatics director for AI clinical integration. The San Francisco Bay Areahasnt seen the expected surge of patientswho would have provided the mass of data needed to make sure AI works on a population, he said.

Outside the hospital, AI-enabled risk factor modeling is being used to help health systems track patients who arent infected with the coronavirus but might be susceptible to complications if they contract COVID-19.

At Scripps Health in San Diego, clinicians are stratifying patients to assess their risk of getting COVID-19 and experiencing severe symptoms using a risk-scoring model that considers factors like age, chronic conditions and recent hospital visits. When a patient scores seven or higher, a triage nurse reaches out with information about the coronavirus and may schedule an appointment.

Though emergencies provide unique opportunities to try out advanced tools, its essential for health systems to ensure doctors are comfortable with them and to use the tools cautiously, with extensive testing and validation, Topol said.

When people are in the heat of battle and overstretched, it would be great to have an algorithm to support them, he said. We just have to make sure the algorithm and the AI tool isnt misleading, because lives are at stake here.

Kaiser Health News(KHN) is a national health policy news service. It is an editorially independent program of theHenry J. Kaiser Family Foundationwhich is not affiliated with Kaiser Permanente.

ThisKHNstory first published onCalifornia Healthline, a service of theCalifornia Health Care Foundation

Follow this link:

Coronavirus tests the value of artificial intelligence in medicine - FierceBiotech