WEBVTT

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People imagine a robot sprinting into

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combat , pulling out wounded soldiers ,

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or racing through a collapsed building

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to rescue civilians , but that's really

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in the far future . Today we're focused

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on the first step , teaching AI to find

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casualties and assess their status ,

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always under meaningful human control .

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That's what the DARPA triage challenge

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is really about , not robots , but

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creating life saving tools that work in

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chaos . Communication is one of the

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hardest things in an emergency response .

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And if the robots are not able to

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communicate well and clearly to the

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medic , the information that they need .

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It will not help them at all . The

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technology being developed here , the

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AI that can rapidly assess injuries ,

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prioritize patients , and support

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overwhelmed medics is already

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transformative . Instead of making

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those quick decisions on the fly ,

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especially when you know , like if you

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have patients that are already deceased ,

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you'll be able to do a quick check on

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them and be like , OK , yeah , that

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category is correct . The robot

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definitely pulled me out of tunnel

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vision at one point . It would call out

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to us like , medic , medic , I have a

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critically injured patient . So at one

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point , I'm treating and then I hear it

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say some words and I'll just look up

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real quick like I'm still working on

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this guy , but I have someone else that

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I'm going to have to go to immediately

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afterwards . They were very accurate in

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pinpointing where our patients were .

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They were very accurate in calling out

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like if this person needed help . Today ,

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there's virtually no artificial

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intelligence designed for frontline

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medical triage . The DARPA triage

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challenge is closing that gap ,

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building AI systems that can process

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vital signs , spot critical injuries ,

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and give medics decision support when

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every second counts . So we really

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think that one of the benefits of using

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AI in an austere environment and you

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know , potentially a mass casualty

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incident is that it's not going to get

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overwhelmed and it'll give the same

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level of fidelity of prediction each

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time . And we hope that some of the

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interpretive tools we develop will help

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the medics more consistently ,

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efficiently , and to save more lives .

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We're looking at , you know , being

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able to actually point the medics

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towards where they're most needed .

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We're looking at it from a very removed

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and mechanical way , uh , which is

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extraordinarily hard to do , uh ,

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during a mass casualty event , and that

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kind of makes these systems perfect at

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this particular job . In healthcare

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today , AI is helping to read X-rays ,

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pathology reports , and analyze

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hospital data . But when it comes to

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the chaotic mass casualty of triage ,

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there's a void . We're building

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technologies for the world's most

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challenging casualty events , and those

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technologies are going to accelerate

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progress for the entire field of

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disaster medicine .

