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Coronavirus puts artificial mind to the test

medical professional. Albert Hsiao and his colleagues at the UC 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 country, They decided to see what it could do.

the researchers quickly deployed their program, Which dots X ray images with spots of color where there exists lung damage or other signs of pneumonia. It has now been given to more than 6,000 chest muscles X rays, And it's leaving some value in diagnosis, considered Hsiao, The director of UCSD's augmented imaging and artificial learning ability 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.

The machine learning programs scroll through millions of pieces of data to detect patterns which really can be hard for clinicians to discern. Yet few of the algorithms have been carefully tested against standard procedures. So while they often appear helpful, Rolling out the programs accompanied by a pandemic could be confusing to doctors and dangerous for patients, Some AI mavens warn.

Topol designated a system created by Epic, A major vendor of virtual health records software, That predicts which coronavirus patients becomes critically ill. Using the tool before it will be validated is "Pandemic exceptionalism, he said.

Epic said the business's model had been validated with data from more 16,000 hospitalized COVID 19 patients in 21 healthcare groupings. No research on the tool has been published for independent study to assess, But nevertheless, previously it was "Developed to help clinicians make treatment decisions and is not an alternative to their judgment, Said harry Hickman, A software construtor on Epic's cognitive computing team.

At least three healthcare AI equipment companies have made funding deals specific to the COVID 19 crisis, plus Vida Diagnostics, An AI powered lung imaging investigating company, in Rock Health.

all in all, AI's implementation in everyday clinical care is less common than hype over _a href= a latina_/a_ the technology would suggest. Yet the coronavirus has inspired some hospital systems to accelerate promising guidelines.

UCSD increased its AI imaging project, in business it out in only two weeks.

Hsiao's installation, With research funding from Amazon Web treatment, The University of California and the nation's Science Foundation, Runs every chest X ray taken at its hospital via a AI algorithm. While no data on the addition has been published yet, Doctors report that the tool influences their clinical making decisions about a third of the time, talked about Dr. christopher Longhurst, UCSD Health's chief expertise officer.

"outcomes to date are very encouraging, And we're not seeing any unintended end result, he said. "Anecdotally, We're feeling like it's recommended, Not aggravating,

AI has advanced further in imaging than in areas of clinical medicine because radiological images have tons of data for algorithms to process, And more data makes the programs easier, Longhurst wanted to say.

But while AI specialists have tried to get AI to do questions like predict sepsis and acute respiratory distress researchers at Johns Hopkins University recently won a National Science Foundation grant to use it to predict heart damage in COVID 19 patients it has been easier to plug it into less risky areas such as hospital logistics.

In ny, 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.

At Mount Sinai Health kit, synthetic intelligence algorithm pinpoints which patients might be ready to be discharged from the hospital within 72 hours, told me Robbie Freeman, vp of clinical innovation at Mount Sinai.

Freeman described the AI's word of advice as a "gossip starter, Meant to help assist clinicians using patient cases decide what to do. AI isn't making the activities.

NYU Langone Health has become incredible a similar AI model. It predicts whether a COVID 19 patient entering the hospital will suffer adverse events within the next four days, had to talk about Dr. Yindalon Aphinyanaphongs, Who leads NYU Langone's predictive statistics team.

the model will be run in a four to six week trial with patients randomized into two groups: One whose doctors will obtain the alerts, And another whose doctors usually do not. The algorithm should help doctors generate a list of things that may predict whether patients are at risk for risks after they're admitted to the hospital, Aphinyanaphongs claims.

Some health systems are leery of rolling out a technology that requires clinical validation involved with a pandemic. Others say they didn't need AI to handle the coronavirus.

Stanford Health Care is not using AI to manage put in the hospital patients with COVID 19, agreed Ron Li, The center's medical informatics director for AI clinical intergrated,is intergrated. The frisco Bay Area hasn't seen the expected surge of patients who would have provided the mass of data needed to make sure AI works on a population, he explained.

Outside a healthcare facility, AI enabled risk factor modeling is being used to help health systems track patients who aren't infected with the coronavirus but might be vunerable to complications if they contract COVID 19.

At Scripps becoming, Clinicians are stratifying patients to assess their risk of getting COVID 19 and experiencing severe symptoms using a risk scoring model that considers factors such as age, Chronic state and recent hospital visits. When a patient scores 7 or higher, A triage nurse reaches out with regarding the coronavirus and may schedule an appointment.

Though emergencies provide unique opportunities to try out advanced tools, It's essential for health systems to ensure doctors are comfortable with them, And to use the tools very carefully, With extensive testing and consent, Topol had said.

"when individuals are in the heat of battle and overstretched, it would be great to have an algorithm to support them, he explained. "We just have to make sure the algorithm and the AI tool isn't misleading, Because lives are at stake here.
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