#AIMI23 | Panel: Industry Perspectives of Health AI in 2023

00:35:18
https://www.youtube.com/watch?v=zvmrSS1X910

概要

TLDRIn this video, experts from various fields discuss the integration of AI in healthcare, focusing on the enhancements it can bring to patient care and clinical workflows. Key speakers emphasize the importance of collaboration among technology, life sciences, and healthcare practitioners to overcome challenges in AI implementation. Topics include regulatory hurdles, business models for sustainable AI solutions, and the potential of open-source frameworks in fostering innovation. The discussion highlights the need for robust algorithms that can handle diverse medical data and the future of multimodal systems in improving clinical outcomes.

収穫

  • 👩‍⚕️ AI is set to significantly improve patient outcomes in healthcare.
  • 📈 Effective business models are crucial for AI solutions to thrive in a B2B environment.
  • 🔍 Regulatory compliance remains a critical challenge in deploying AI technologies.
  • 🌍 Open source can accelerate medical AI research and innovation.
  • 🤝 Collaboration between academia and healthcare practitioners enhances algorithm validation.
  • 🚀 Multimodal systems show promise in integrating various data types for comprehensive clinical support.
  • 💡 AI deployment requires understanding user needs and minimizing implementation challenges.
  • 🧠 Sustainable AI solutions can generate financial value for healthcare providers.
  • 🎯 The future of AI in healthcare includes opportunities in developing regions and underserved markets.
  • 🔄 Continuous algorithm validation is necessary to ensure efficacy as new data emerges.

タイムライン

  • 00:00:00 - 00:05:00

    Jessica Mega, a cardiologist at Stanford and co-founder of Verily, highlights the intersection of technology and life sciences to improve patient outcomes, reminiscent of the excitement during the human genome project, and emphasizes the need for collaboration to bridge the gap between advancements and practical applications in healthcare.

  • 00:05:00 - 00:10:00

    Rohit Takar from Carpool discusses the challenges in implementing AI in medical imaging, especially regarding presenting solutions to radiologists and monitoring deployed solutions for model drift and data drift over time, emphasizing the need for collaborative efforts to overcome these challenges.

  • 00:10:00 - 00:15:00

    Tanishq Abraham, a healthcare investor at F Prime, stresses the focus on early-stage opportunities that improve clinical processes and enable tasks currently beyond clinician capabilities, highlighting the transformative potential of AI in healthcare.

  • 00:15:00 - 00:20:00

    Stephen Drew of Wing Brain describes the mission of his company to improve radiology workflows through AI, while also addressing significant public health issues like tuberculosis and emphasizing the importance of data integration across healthcare systems to enhance diagnostic accuracy.

  • 00:20:00 - 00:25:00

    Nish from Bunker Hill shares how they aim to facilitate the transition of algorithms from research to clinical practice by creating a consortium of research institutions to validate, commercialize, and share revenue from successful algorithms, addressing the gap between research innovations and real-world applications.

  • 00:25:00 - 00:30:00

    Tanishq from Stability AI expresses excitement over generative AI's applications in medicine, particularly in creating models that enhance research and practice through the exchange of ideas and collaborations between general AI and medical AI fields.

  • 00:30:00 - 00:35:18

    Panel discussion reveals critical reflections on sustainable business models in healthcare AI, focusing on demonstrable clinical value for providers, operational efficiency improvements, and the need for AI solutions to be embedded in existing processes to gain acceptance and financial backing from healthcare stakeholders.

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ビデオQ&A

  • What is the current state of AI in healthcare?

    AI in healthcare is at a critical juncture, with many technical advancements leading to applications aimed at improving patient outcomes.

  • What challenges exist in deploying AI in clinical workflows?

    Challenges include implementation strategies to fit user needs, monitoring AI models for drift, and addressing regulatory requirements.

  • How do companies plan to monetize their AI solutions in healthcare?

    Monetization strategies include getting reimbursement from payers, improving clinical value, and tapping into healthcare data markets.

  • What role does regulatory compliance play in AI healthcare solutions?

    Regulatory compliance is essential for AI deployment in healthcare, ensuring safety and efficacy while navigating processes like FDA approvals.

  • What potential does open source have in medical AI?

    Open source can accelerate research and allow collaboration, facilitating the development of models tailored to specific healthcare applications.

  • What is the importance of multimodal systems in medical AI?

    Multimodal systems integrate various data types to provide comprehensive support to clinicians, enhancing decision-making processes.

  • How can AI be scaled across diverse medical findings?

    Scaling AI across multiple findings requires effective regulatory processes, minimizing redundancy in approvals and ensuring comprehensive coverage.

  • What business models are proving effective for AI in healthcare?

    Effective models include software as a service, generating direct financial value for providers, and integrating AI into existing healthcare workflows.

  • What are the future prospects for AI in healthcare?

    Future prospects include advancements in multimodal AI systems and opportunities in underserved regions, focusing on AI's applicability to diverse healthcare scenarios.

  • How do researchers approach validation for AI algorithms?

    Validation involves external testing, clinical ROI assessment, and establishing partnerships across academic institutions for data sharing.

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  • 00:00:04
    my name is Jessica Mega I'm a
  • 00:00:07
    cardiologist at Stanford I'm on the
  • 00:00:10
    board of directors at danaher
  • 00:00:11
    Corporation and one of the co-founders
  • 00:00:12
    of verily which is the branch of
  • 00:00:15
    alphabet really focused on life science
  • 00:00:17
    and Healthcare
  • 00:00:18
    and personally my passion has always
  • 00:00:21
    been thinking about the intersection of
  • 00:00:23
    Technology life science and the
  • 00:00:25
    applications to improve patient outcomes
  • 00:00:27
    and I think we're in a moment that
  • 00:00:28
    Harkens back in my mind to when the
  • 00:00:31
    human genome was sequenced and if you
  • 00:00:33
    think about all of the Decades of
  • 00:00:35
    research and all of the deep insights
  • 00:00:38
    that it took to sequence the human
  • 00:00:40
    genome there were many many people many
  • 00:00:43
    tools but it was then the next several
  • 00:00:45
    decades of applications the
  • 00:00:47
    understanding of what genes do we
  • 00:00:49
    actually have to measure when we think
  • 00:00:50
    about oncology and Diagnostics what does
  • 00:00:53
    it translate now we're applying direct
  • 00:00:55
    crispr therapies but we're in that same
  • 00:00:57
    moment of excitement where there have
  • 00:00:59
    been Decades of technical advancements
  • 00:01:01
    and we have some of the world's experts
  • 00:01:03
    with us today to talk about that but now
  • 00:01:05
    we're in that critical moment of
  • 00:01:07
    application and we heard from Nigam that
  • 00:01:09
    there's a Chasm and I think collectively
  • 00:01:11
    this group together can help bridge that
  • 00:01:13
    Chasm and I think take all of the
  • 00:01:16
    opportunity but also all the reality of
  • 00:01:18
    what it takes to truly create a business
  • 00:01:20
    in this world
  • 00:01:22
    as I am going to turn to our group we're
  • 00:01:24
    going to have each individual introduce
  • 00:01:26
    themselves the company that they're
  • 00:01:27
    working with and any high level thoughts
  • 00:01:29
    and then what we're going to do is I
  • 00:01:31
    have a few questions prepared but I know
  • 00:01:33
    that your questions are on your mind
  • 00:01:35
    because people have already grabbed me
  • 00:01:37
    in the hallway and started questions so
  • 00:01:38
    uh start to keep those in your mind as
  • 00:01:41
    we get the introductions because I
  • 00:01:42
    promise they'll be time for uh for all
  • 00:01:44
    of your questions all right if uh if you
  • 00:01:46
    don't mind started
  • 00:01:48
    hi everyone my name is Rohit takar I
  • 00:01:51
    head the technology at carpool so
  • 00:01:53
    carpool is a platform that allows you to
  • 00:01:55
    explore validate and deploy Medical
  • 00:01:57
    Imaging AI Solutions in your clinical
  • 00:01:59
    workflow I think we all believe that AI
  • 00:02:04
    how can improve the patient outcomes
  • 00:02:06
    stream streamline the processes and can
  • 00:02:09
    help reduce the cost but what are the
  • 00:02:11
    challenges that we are facing right now
  • 00:02:12
    so I believe that there are two
  • 00:02:14
    challenges uh that I feel ah that are
  • 00:02:17
    actually handling the deployment of AI
  • 00:02:20
    uh in clinic clinical workflow one is
  • 00:02:23
    the implementation so how do you present
  • 00:02:26
    these solutions to the end users which
  • 00:02:28
    in our case are Radiologists so ah there
  • 00:02:31
    could be multiple solution because one
  • 00:02:33
    fits for all ah it doesn't happen the
  • 00:02:36
    other is once you have deployed a
  • 00:02:38
    solution so how do we monitor it over
  • 00:02:40
    time so there could be model drift data
  • 00:02:42
    drift and there would be some issue with
  • 00:02:45
    the pipeline so these are the two
  • 00:02:47
    challenges that I feel uh which ah we
  • 00:02:50
    need to figure out together as a
  • 00:02:52
    collaborative effort and see how can we
  • 00:02:55
    build Solutions create proof points and
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    then deploy them in uh on a wider ah at
  • 00:03:02
    a clinical level so that's what I feel
  • 00:03:06
    could be something that we can do in
  • 00:03:08
    2023
  • 00:03:10
    thank you
  • 00:03:12
    hi I'm ten viewers either Mahmoud I'm an
  • 00:03:14
    IBM fellow at IBM research IBM research
  • 00:03:17
    as you know is the academic wing of IBM
  • 00:03:20
    where we our Charter is to explore the
  • 00:03:22
    Next Generation Technologies in various
  • 00:03:24
    fields in healthcare in particular as
  • 00:03:27
    you know we were
  • 00:03:28
    you know ahead of the curve in trying to
  • 00:03:31
    realize the importance of AI and
  • 00:03:32
    radiology and some of you have been to
  • 00:03:35
    RSA 2016 or 2015 onwards you've seen the
  • 00:03:39
    introduction of AI in Radiology it
  • 00:03:42
    transitioned into business so we've gone
  • 00:03:43
    through the cycle of you know the hype
  • 00:03:47
    and practicing putting it in
  • 00:03:50
    products and then coming back to the
  • 00:03:52
    realization of what is the next things
  • 00:03:54
    to do and so I will have some
  • 00:03:56
    perspectives on now what works what
  • 00:03:58
    doesn't work and where are the
  • 00:04:00
    challenges particularly when it comes to
  • 00:04:02
    looking at capturing fine-grained
  • 00:04:05
    nuances which is where the AI needs to
  • 00:04:08
    get to in Next Generation models
  • 00:04:12
    okay my name is cu I'm a healthcare
  • 00:04:15
    investor at F Prime which is a venture
  • 00:04:18
    capital group mostly based in Boston SF
  • 00:04:20
    and London but before that I'm a
  • 00:04:23
    Stanford Medical School alumni and also
  • 00:04:25
    did research at the Stanford machine
  • 00:04:27
    learning grouping AI for healthcare as
  • 00:04:28
    well and I think right now we're looking
  • 00:04:30
    at a lot of early stage opportunities
  • 00:04:32
    with people with ideas on twofolds one
  • 00:04:36
    is actually similar to the previous
  • 00:04:38
    panel talk about speeding up a lot of
  • 00:04:41
    the processes that we encounter in the
  • 00:04:43
    clinic you know managing the back office
  • 00:04:45
    for clinicians and whatnot and the other
  • 00:04:47
    side is actually a step forward to to do
  • 00:04:51
    things that clinicians cannot do right
  • 00:04:53
    now and that's an area that we also look
  • 00:04:56
    at currently too
  • 00:04:59
    so good morning
  • 00:05:01
    good morning
  • 00:05:03
    good morning
  • 00:05:04
    you know what I said that to weigh me up
  • 00:05:06
    because I just fly from the other side
  • 00:05:08
    of the Earth from Vietnam to here so so
  • 00:05:12
    you guys just remember a Vietnamese guy
  • 00:05:14
    on the stage so that's me so
  • 00:05:16
    so I'm Stephen Drew I'm the guy for big
  • 00:05:21
    brain
  • 00:05:22
    so Wing brain is a company around four
  • 00:05:25
    years and you know one day I told my
  • 00:05:29
    boss a secretive in Microsoft say I'm
  • 00:05:32
    not going to do any more Microsoft stuff
  • 00:05:35
    I want to do something
  • 00:05:37
    a little bit more Humanity so I decided
  • 00:05:41
    to get into this field to build AI
  • 00:05:44
    to assist grade watches
  • 00:05:47
    on the workflow
  • 00:05:49
    and improving the efficiency like most
  • 00:05:52
    people said and also the by through
  • 00:05:55
    spending and also through the
  • 00:05:58
    misdiagnosis in cancer area
  • 00:06:02
    and then also we do the social impact we
  • 00:06:06
    just like dealing with tuberculosis so I
  • 00:06:09
    went to MetLife who you know a few
  • 00:06:12
    months ago in Geneva tried to tackle
  • 00:06:15
    that problem in other side of the world
  • 00:06:18
    so basically Wing brain is focusing on
  • 00:06:22
    religious workflow we have 52 you know
  • 00:06:26
    comprehensive detection
  • 00:06:29
    and at the same time we realized that
  • 00:06:32
    not just AI we need to focusing on the
  • 00:06:36
    Enterprise side which is where we
  • 00:06:39
    connect all of the data together because
  • 00:06:41
    you know that hospital have risk lists
  • 00:06:43
    his love of law so they vary this very
  • 00:06:48
    so we bring the connector to bring the
  • 00:06:50
    data together and we can train
  • 00:06:53
    Ai and we also one of the guys that
  • 00:06:57
    interray the activity and all of that
  • 00:07:00
    into the workflow so we can improve the
  • 00:07:02
    reporting as well and the data
  • 00:07:05
    extraction from
  • 00:07:07
    La language model for the medical field
  • 00:07:10
    so with this I'm really looking for a
  • 00:07:14
    lot of questions about like you know on
  • 00:07:17
    how we really deployed to a hundreds
  • 00:07:19
    Hospital you know the way we built is
  • 00:07:22
    two year we never do anything else
  • 00:07:24
    except just helped out to be a decent
  • 00:07:27
    product and the third year we go from 10
  • 00:07:29
    to 20 to 100 hospital and today we
  • 00:07:33
    affect around 2 million people live
  • 00:07:35
    through our Pipeline and we have a FDA
  • 00:07:40
    you know one of the only one in the
  • 00:07:42
    region of Southeast got that and as well
  • 00:07:45
    we assign agreement with Microsoft
  • 00:07:48
    Nvidia and also stand for so great to
  • 00:07:53
    work with curl every two weeks we see
  • 00:07:55
    his face on life so thank you very much
  • 00:07:59
    and great to see all of you again
  • 00:08:02
    hi everyone my name is Nish I'm the CEO
  • 00:08:05
    of Bunker Hill previously the starting
  • 00:08:07
    Bunker Hill I was a student at Stanford
  • 00:08:09
    I was a graduate researcher at the
  • 00:08:11
    Stanford Amy group actually and Dr
  • 00:08:14
    nigamsha mentioned that there were many
  • 00:08:15
    algorithms that are being built I'm
  • 00:08:16
    pretty sure my algorithm was one of
  • 00:08:18
    those in the tally there and you know
  • 00:08:21
    after we built those algorithms we
  • 00:08:23
    really stumbled stumbled hard on the
  • 00:08:25
    question what now
  • 00:08:27
    okay you know what the steps are you
  • 00:08:29
    know you need to externally validate
  • 00:08:31
    your algorithm you know you need to show
  • 00:08:32
    that it has some kind of clinical Roi
  • 00:08:34
    you know you need FDA clearance you know
  • 00:08:36
    you need to deploy but for all of those
  • 00:08:38
    phases for all of those questions you
  • 00:08:41
    had two pressing problems one I don't
  • 00:08:43
    even know what it takes to get there too
  • 00:08:46
    I don't even know where to start and we
  • 00:08:48
    see this as a pressing problem you know
  • 00:08:50
    the only way algorithms are hopefully
  • 00:08:53
    going to get out into the clinical
  • 00:08:54
    practice if you're able to take
  • 00:08:56
    researchers algorithms and actually have
  • 00:08:58
    a way to get them into practice the
  • 00:09:01
    current solution of starting a company
  • 00:09:03
    around a single algorithm and hoping to
  • 00:09:05
    get Venture funding is just not working
  • 00:09:07
    and so what we do at Bunker Hill is that
  • 00:09:11
    we are creating a way to get algorithms
  • 00:09:14
    built by the broader research Community
  • 00:09:16
    out into the clinical world we have
  • 00:09:18
    built a Consortium of academic medical
  • 00:09:20
    centers which is effectively a legal
  • 00:09:22
    bubble there are 17 academic medical
  • 00:09:24
    centers a part of this today and what
  • 00:09:26
    this allows them to do is researchers at
  • 00:09:28
    each other researchers can collaborate
  • 00:09:30
    across institutions share data share
  • 00:09:32
    algorithms validate each other's
  • 00:09:34
    algorithms and when they are validated
  • 00:09:36
    Bunker Hill files for FDA clearance
  • 00:09:38
    commercializes it on a platform and then
  • 00:09:40
    shares the revenue back with the
  • 00:09:41
    researcher hopefully that creates a way
  • 00:09:43
    to actually get all of the one wonderful
  • 00:09:46
    research work that is being done by such
  • 00:09:48
    daring innovators and actually getting
  • 00:09:50
    them into clinical practice and making
  • 00:09:52
    AI actually a reality
  • 00:09:56
    hi my name is tanishq Abraham and I'm a
  • 00:09:59
    researcher at stability Ai and
  • 00:10:02
    previously I was also a PhD student at
  • 00:10:06
    UC Davis and just recently completed and
  • 00:10:10
    yeah at stability AI I work on medical
  • 00:10:13
    AI research in fact we founded an
  • 00:10:15
    organization known as medarc to help
  • 00:10:17
    accelerate some of this medical AI
  • 00:10:20
    research especially looking at the
  • 00:10:22
    intersection of generative Ai and
  • 00:10:24
    medicine so of course you know we've
  • 00:10:26
    heard a lot about that already today but
  • 00:10:28
    yeah we're really excited about about
  • 00:10:30
    what we can how we can apply a
  • 00:10:32
    generative AI to Medicine especially at
  • 00:10:34
    a company like stability which is known
  • 00:10:35
    for their efforts in that space with you
  • 00:10:38
    know the famous stable diffusion image
  • 00:10:40
    generator and you know there are very
  • 00:10:42
    surprising and interesting applications
  • 00:10:44
    of of generative Ai and you know you
  • 00:10:46
    never think like something like an image
  • 00:10:48
    generator you know what kind of
  • 00:10:49
    applications it could have in medicine
  • 00:10:51
    but it turns out you know there's some
  • 00:10:53
    really interesting applications and I
  • 00:10:56
    think also one of the really exciting
  • 00:10:58
    things about working in medical AI
  • 00:10:59
    research is also the exchange just yeah
  • 00:11:01
    generally the exchange of ideas uh in
  • 00:11:04
    just the general AI field but also and
  • 00:11:07
    with you know medical AI I I think um
  • 00:11:09
    you know of course there's lots of
  • 00:11:10
    applications of various AI Technologies
  • 00:11:14
    in medicine but also lots of interesting
  • 00:11:16
    applications of methods that were
  • 00:11:19
    originally developed in medical Ai and
  • 00:11:21
    bringing them to kind of the general AI
  • 00:11:23
    uh uh Fields so I mean we've seen some
  • 00:11:26
    of these examples in the past for
  • 00:11:27
    example the unit was originally
  • 00:11:29
    developed for you know biomedical
  • 00:11:31
    segmentation even clip which was you
  • 00:11:34
    know which was developed by open AI it
  • 00:11:36
    was kind of a scaled up version of a
  • 00:11:38
    paper that was originally published by
  • 00:11:40
    Stanford Amy actually so there's lots of
  • 00:11:42
    interesting applications in general AI
  • 00:11:44
    that kind of start out in some of the
  • 00:11:48
    medical AI research so I think that sort
  • 00:11:50
    of collaboration that sort of exchange
  • 00:11:52
    of ideas is really exciting and I think
  • 00:11:55
    yeah the sort of development in parallel
  • 00:11:57
    of ideas in general AI generative AI
  • 00:12:00
    medical AI That's what I'm most excited
  • 00:12:02
    about
  • 00:12:03
    excellent and congratulations
  • 00:12:05
    so we were talking about the business
  • 00:12:07
    models how do we actually create
  • 00:12:10
    sustainable businesses and if you think
  • 00:12:12
    about outside of the healthcare space a
  • 00:12:15
    lot of the AI is embedded in tools where
  • 00:12:17
    there's already an ad model or there's a
  • 00:12:20
    freemium model where you start but you
  • 00:12:22
    pay for additional services or to direct
  • 00:12:25
    to Consumer so you if we can start with
  • 00:12:27
    you what are some of the business models
  • 00:12:29
    that you're seeing that are effective in
  • 00:12:31
    healthcare because in many cases it's a
  • 00:12:33
    B2B right so you're an industry selling
  • 00:12:35
    to someone else who is paying for health
  • 00:12:37
    care who are ultimately serving the
  • 00:12:40
    customer our constituents patients and
  • 00:12:42
    clinicians so it'd be good to just hear
  • 00:12:44
    a little bit about how you evaluated
  • 00:12:46
    businesses and then we'll hear from a
  • 00:12:48
    few of the companies what business
  • 00:12:49
    models are working and potentially if
  • 00:12:51
    any of them haven't worked yeah
  • 00:12:53
    definitely I think that's an era that
  • 00:12:55
    I'm also learning as well so I would
  • 00:12:57
    love to hear other people's ideas too so
  • 00:12:59
    I think we see a couple of different
  • 00:13:01
    ways people try to get paid and I was
  • 00:13:03
    previously mentioned by other people as
  • 00:13:05
    well on the provider side either as
  • 00:13:07
    hospitals or clinicians the margin is
  • 00:13:10
    quite thin right like there's not as
  • 00:13:12
    much of Liberty to just pay for hundreds
  • 00:13:15
    of thousands of dollars of software as
  • 00:13:17
    what other companies can pay for even
  • 00:13:20
    just one single user so one way we've
  • 00:13:23
    seen people trying to get around that is
  • 00:13:25
    to not just create clinical value from
  • 00:13:27
    their product but also direct monetary
  • 00:13:31
    value from their AI product to for
  • 00:13:34
    example to get it reimbursed from the
  • 00:13:37
    payer side for providers so that
  • 00:13:39
    provider is not just using it to get out
  • 00:13:42
    of their own pocket but also actually
  • 00:13:44
    create hopefully Financial value
  • 00:13:46
    directly for them as well another way
  • 00:13:49
    that we see this being monetized is
  • 00:13:51
    actually to go beyond the direction to
  • 00:13:54
    patient with back to clinicia angle but
  • 00:13:57
    also to tap into the broader Healthcare
  • 00:13:59
    ecosystem people who tap into the
  • 00:14:02
    healthcare data market which is huge and
  • 00:14:04
    you know companies pay hundreds of
  • 00:14:06
    Millions for for that in some ways and
  • 00:14:08
    also other sides of the healthcare
  • 00:14:11
    ecosystem as well which sometimes can
  • 00:14:13
    have more Liberty in trying out and
  • 00:14:16
    paying for these type of AI Solutions
  • 00:14:18
    and from some of the company's
  • 00:14:20
    perspectives
  • 00:14:21
    what are the models that you're using I
  • 00:14:23
    can take a stab at it
  • 00:14:25
    um I think their business model it
  • 00:14:28
    implies money and I think that's where
  • 00:14:30
    AI just reduces down to any other
  • 00:14:32
    medical intervention you just have to
  • 00:14:34
    follow the money so if you look at who
  • 00:14:36
    the healthcare stakeholders are okay so
  • 00:14:38
    you have providers the health systems
  • 00:14:40
    you have Pharma and then you have payers
  • 00:14:42
    on the provider side what can you do to
  • 00:14:44
    make a financial case for them okay
  • 00:14:46
    could you find them more patients could
  • 00:14:47
    you reduce or automate away mundane or
  • 00:14:50
    boring or expensive tasks okay so if you
  • 00:14:52
    can do that there's a business incentive
  • 00:14:55
    there's a financial incentive for the
  • 00:14:57
    providers to adopt it and actually pay
  • 00:14:58
    for it on the on the Pharma side can you
  • 00:15:01
    accelerate their clinical trials by
  • 00:15:03
    finding patients faster if you can do
  • 00:15:05
    that there's a business case for them
  • 00:15:06
    there's money to be saved by them can
  • 00:15:08
    you find more patients who would be
  • 00:15:10
    eligible for drugs that they just came
  • 00:15:12
    out with okay now that's again you're
  • 00:15:13
    increasing the market that's a business
  • 00:15:15
    model on the insurance side on the peer
  • 00:15:18
    side can you reduce their cost of care
  • 00:15:20
    within two years okay that's another
  • 00:15:21
    incentive there's a there's a financial
  • 00:15:23
    uh play for them so again in this case
  • 00:15:26
    AI just reduces down to any other
  • 00:15:28
    medical intervention it does not really
  • 00:15:29
    matter what technology is supporting
  • 00:15:32
    that intervention ultimately it's who is
  • 00:15:34
    being who financially benefits from the
  • 00:15:37
    widespread deployment of that tool so
  • 00:15:39
    Stephen yes so I think uh I go back to
  • 00:15:43
    the paying the pain point of hospital
  • 00:15:45
    and the operation of the hospital so as
  • 00:15:50
    you know that in my point of view the
  • 00:15:52
    hospital uh you know I.T is uh probably
  • 00:15:57
    not as strong at the order vertical
  • 00:16:00
    market like you know e-commerce supply
  • 00:16:03
    chain manufacturing so we come down to
  • 00:16:06
    not only using the AI to increasing the
  • 00:16:10
    efficiency
  • 00:16:12
    misdiagnosis you know showing the TB
  • 00:16:15
    problem of the world we also look
  • 00:16:17
    looking at how to really solving the
  • 00:16:19
    pain of data display and data platform
  • 00:16:23
    and combined with the AI we can connect
  • 00:16:26
    on the data and generate the analytic
  • 00:16:29
    and forecasting for the operational as
  • 00:16:32
    well as for the doctor in efficiency so
  • 00:16:35
    you can think of the the model business
  • 00:16:37
    we do a software as a service because we
  • 00:16:40
    like to be more like TurnKey solution
  • 00:16:43
    comprehensive solution rather than
  • 00:16:45
    single POI solution and we tackle from
  • 00:16:48
    two Dimension One is the Enterprise that
  • 00:16:52
    we can connect all of the display data
  • 00:16:54
    into the center line with the identifier
  • 00:16:57
    model the second is the AI to bring the
  • 00:17:00
    value of efficiency and the third
  • 00:17:03
    dimension we do is the monetization of
  • 00:17:05
    AI so we view a platform to do the
  • 00:17:08
    labeling annotation for all of the
  • 00:17:11
    company Wai because we know Wing brain
  • 00:17:14
    cannot do everything so that's why we
  • 00:17:16
    bring you know the monetization AI not
  • 00:17:20
    only from labeling and speak up with
  • 00:17:22
    hundreds of pre-latches it's very hard
  • 00:17:24
    in the U.S to get 100 languages to do
  • 00:17:27
    the labeling you know of course we have
  • 00:17:29
    to define the standard of how to do the
  • 00:17:32
    label correctly and then we're doing
  • 00:17:34
    that so we build those platforms not
  • 00:17:37
    only for solving the doctor and the
  • 00:17:40
    workflow also we're doing with the
  • 00:17:42
    labeling and then the third dimension we
  • 00:17:45
    also want to make sure that like you
  • 00:17:47
    know sharing the data to the communities
  • 00:17:50
    in the public matter so that we can
  • 00:17:53
    solve the problem together and
  • 00:17:55
    Marketplace our platform any other
  • 00:17:58
    thoughts maybe I can give the other end
  • 00:18:00
    of the spectrum perspective from a large
  • 00:18:02
    company and why we got out of the
  • 00:18:04
    healthcare business
  • 00:18:05
    so um
  • 00:18:07
    you know the the kind of Revenue that
  • 00:18:09
    makes sense uh for IBM a billion dollar
  • 00:18:12
    revenue is still not good enough so so
  • 00:18:14
    this field and and just by AI alone uh
  • 00:18:19
    surviving is more for the smaller
  • 00:18:22
    businesses that's why you saw so many
  • 00:18:24
    startups in this space but as the
  • 00:18:25
    starters have also discovered just
  • 00:18:28
    having an AI model alone is not enough
  • 00:18:30
    it is only a small piece of the big
  • 00:18:32
    puzzle that you have to get into so if
  • 00:18:35
    you think of what are some good models
  • 00:18:36
    for Success that where AI will still
  • 00:18:38
    succeed and where we see a future for it
  • 00:18:41
    will be in a few different deployments
  • 00:18:43
    situations one and I remember there's
  • 00:18:45
    Intel Inside there's like I think terms
  • 00:18:48
    that you see on your laptops uh it
  • 00:18:51
    should be something like that where it's
  • 00:18:53
    the other thing that in which AI is
  • 00:18:56
    built into that is giving the value
  • 00:18:58
    proposition whether that is Hardware in
  • 00:19:01
    which you have ai doing this naturally
  • 00:19:02
    or is in the packs where it's already
  • 00:19:04
    built in and it's not being charged
  • 00:19:06
    separately so the way you win in this
  • 00:19:09
    might be through those vehicles and then
  • 00:19:12
    having some attribution back to aisl but
  • 00:19:15
    AI by itself
  • 00:19:16
    is not something that people will be
  • 00:19:19
    willing to pay big bucks for and that
  • 00:19:21
    was one of the learnings as such the
  • 00:19:24
    other thing I could say is and and I
  • 00:19:26
    mean if you've heard of the think and
  • 00:19:28
    what's the next announcement recently
  • 00:19:29
    and all we are big into AI models still
  • 00:19:31
    but the way we're looking off is the
  • 00:19:34
    value proposition is using those for
  • 00:19:36
    some use cases and so the AI is again
  • 00:19:39
    built inside and it's not the focus but
  • 00:19:42
    it's the end user use case that is the
  • 00:19:44
    focus and there there is opportunities
  • 00:19:46
    in Consulting in you know in those sort
  • 00:19:49
    of methods of deployment so bundle
  • 00:19:52
    inside is one way and I think we can
  • 00:19:55
    still make a business case for AI
  • 00:19:59
    so I think ah
  • 00:20:02
    the biggest question that we get is who
  • 00:20:04
    will pay for the implementation of AI
  • 00:20:07
    right so whether the radiology
  • 00:20:09
    department the hospital or will you cut
  • 00:20:11
    it from the radiologist pocket so that
  • 00:20:13
    these are some of the questions that we
  • 00:20:14
    get
  • 00:20:15
    uh I think if you can do an Implement a
  • 00:20:18
    implementation where we can really
  • 00:20:20
    augment the Radiologists so if let's say
  • 00:20:22
    they are doing 100 scans and they are
  • 00:20:24
    paid X dollars for that
  • 00:20:26
    at some point they can do more than 100
  • 00:20:28
    scans and by implementation of AI let us
  • 00:20:33
    say so AI Solutions you can get for 30
  • 00:20:35
    cents 20 cents for 40 cents per study so
  • 00:20:39
    if you can uh give them a value that is
  • 00:20:41
    X Plus Delta so I think that would be
  • 00:20:44
    something that can be that can come from
  • 00:20:46
    the radiologist pocket as well so those
  • 00:20:48
    are the business cases that we are
  • 00:20:49
    actually trying right now the other is
  • 00:20:51
    how can you get more patients so ah from
  • 00:20:54
    your x-rays to CD scans can you convert
  • 00:20:57
    more patients which need
  • 00:20:59
    CT scans to be done so I mean that could
  • 00:21:03
    be the other way
  • 00:21:05
    businesses wants to ah see the value
  • 00:21:09
    coming in yeah so it sounds like there
  • 00:21:11
    are two major themes that are coming
  • 00:21:13
    together one is this idea if you think
  • 00:21:15
    about life science and Healthcare there
  • 00:21:16
    are already models out there so if you
  • 00:21:18
    can accelerate drug Discovery if you can
  • 00:21:20
    increase efficiency and value leverage
  • 00:21:24
    those models and the second one that can
  • 00:21:26
    be really emphasized is that the AI is
  • 00:21:29
    the one piece surrounded by all of the
  • 00:21:31
    services and just think holistically I
  • 00:21:33
    think about the offering so I think
  • 00:21:35
    those are just good reminders I mean I
  • 00:21:37
    did want to add that it is possible if
  • 00:21:40
    AI could simply be that a reason for a
  • 00:21:43
    business if AI had that capability right
  • 00:21:45
    now the current models can only do touch
  • 00:21:49
    some portion of this like think about
  • 00:21:50
    chest x-ray work there's still like 4 14
  • 00:21:52
    14 findings I mean what about the rest
  • 00:21:55
    and you know so if I have to remove so
  • 00:21:58
    so you have to raise the capability of
  • 00:22:00
    AI and make it comprehensive to replace
  • 00:22:03
    or to augment in a way that is uh
  • 00:22:06
    TurnKey so I think until that time
  • 00:22:09
    you'll still have that and people don't
  • 00:22:11
    have the patience to do that you know
  • 00:22:13
    just as we ran the medical Civ Grand
  • 00:22:15
    Challenge for six years we took us a lot
  • 00:22:18
    of effort to cover all the findings in
  • 00:22:20
    chest x-rays lots of clinician
  • 00:22:22
    involvement lots of data sets so such
  • 00:22:25
    resources should be there you know order
  • 00:22:27
    to get to those levels and and that's
  • 00:22:29
    just for one modality and you know so
  • 00:22:32
    you think about that so that's the level
  • 00:22:34
    at which AI should be working if we want
  • 00:22:37
    to have that kind of impact but if
  • 00:22:39
    you're only doing a little bit of this
  • 00:22:40
    or a little bit of that then you'll just
  • 00:22:43
    be a tool that people may want to turn
  • 00:22:45
    you off so then you don't have a way to
  • 00:22:48
    get Revenue yeah it's interesting right
  • 00:22:49
    because the Health Care system is
  • 00:22:51
    already so fragmented that it's a
  • 00:22:53
    reminder to us that if we come in with
  • 00:22:55
    one point solution when I put my
  • 00:22:57
    clinical hat on I'm thinking how does
  • 00:22:59
    that integrate into the Haiku app that
  • 00:23:01
    sits in my pocket so you're right it's
  • 00:23:04
    very hard to think in fact the best
  • 00:23:06
    thing we could do for patients and
  • 00:23:08
    clinicians is to bring down the
  • 00:23:09
    fragmentation there's another debate as
  • 00:23:12
    we were just talking about business
  • 00:23:13
    models and how open or closed do we stay
  • 00:23:16
    with these businesses open source
  • 00:23:19
    becomes there's just a lot you can't
  • 00:23:21
    open up the New York Times The Wall
  • 00:23:23
    Street Journal without seeing major
  • 00:23:24
    companies taking different stances and
  • 00:23:26
    and certainly a lot of the the up and
  • 00:23:28
    coming companies
  • 00:23:30
    um do you mind giving us a little bit of
  • 00:23:31
    your perspective on the role of Open
  • 00:23:33
    Source in the public-private
  • 00:23:35
    Partnerships and actual deployment yeah
  • 00:23:38
    I think open source is extremely
  • 00:23:39
    powerful for you know accelerating
  • 00:23:41
    research and
  • 00:23:43
    um yeah there's a lot of opportunities
  • 00:23:45
    in that in that sector but also
  • 00:23:48
    um the kind of the approach that a lot
  • 00:23:50
    of these open source companies including
  • 00:23:52
    stability uh tend to take is developing
  • 00:23:55
    a kind of General open source models
  • 00:23:58
    that can be publicly released but then
  • 00:24:00
    utilizing their expertise to fine-tune
  • 00:24:02
    these models for specific use cases so I
  • 00:24:05
    think this may also have a potential
  • 00:24:07
    um this is also potentially something
  • 00:24:09
    that can be done in the healthcare
  • 00:24:10
    sector where you can have research
  • 00:24:12
    models that are developed but also
  • 00:24:14
    fine-tuned models for specific clinical
  • 00:24:16
    data sets from hospitals you know
  • 00:24:18
    specific hospitals situations like that
  • 00:24:20
    and I think that will be a really
  • 00:24:21
    interesting model as well
  • 00:24:23
    um for applying AI to healthcare yeah
  • 00:24:26
    excellent any other comments on open
  • 00:24:29
    source so I think that besides what the
  • 00:24:33
    gentleman here said I think the open saw
  • 00:24:35
    is the way to promote
  • 00:24:37
    the demoretization of AI and technology
  • 00:24:40
    so I think as a company like you know
  • 00:24:44
    doing Health Tech we believe in that it
  • 00:24:47
    beside that we also have to look at
  • 00:24:50
    responsible AI responsible software when
  • 00:24:53
    you utilize open source so so like
  • 00:24:57
    example we we have to have a board
  • 00:24:59
    director with the policy make you know
  • 00:25:02
    to make sure that we follow it and we
  • 00:25:04
    make sure they identify data and all the
  • 00:25:07
    validation of the data point that have
  • 00:25:09
    no data leak for you know like personal
  • 00:25:12
    information so that is the fundamental
  • 00:25:15
    when we view our pipeline for for data
  • 00:25:18
    thank you so I'm going to ask one last
  • 00:25:21
    question then we're opening it up and
  • 00:25:23
    then you are the team will keep us on
  • 00:25:25
    time so we've heard a lot this morning
  • 00:25:28
    about what it takes to have a algorithm
  • 00:25:31
    or product but what it really means to
  • 00:25:33
    engage with the regulatory bodies
  • 00:25:34
    whether it's the FDA emea
  • 00:25:36
    it would be helpful to hear some
  • 00:25:38
    practical examples how often are you
  • 00:25:40
    refreshing your algorithms for example
  • 00:25:42
    how have you navigated the quality
  • 00:25:45
    management systems and Regulatory
  • 00:25:47
    environment if we could just have maybe
  • 00:25:48
    one or two examples with that I think it
  • 00:25:50
    brings it home and in fact I think Nishi
  • 00:25:54
    said that that was one of your passions
  • 00:25:55
    the reason why uh you you moved over to
  • 00:25:58
    create the company do you mind just
  • 00:26:00
    getting us started on some of that yeah
  • 00:26:02
    I think when we started the company we
  • 00:26:04
    wanted you know coming from the valley
  • 00:26:06
    the underlying notion was the FDA is the
  • 00:26:08
    reason why AI was not getting into
  • 00:26:10
    clinical practice it was back in 2017
  • 00:26:12
    2018 so my co-founder and Ivy flew to DC
  • 00:26:15
    we met with the FDA and they're just
  • 00:26:18
    they're the nicest people they're really
  • 00:26:20
    good at their job it's like and it's
  • 00:26:24
    their job is difficult and they've done
  • 00:26:26
    a tremendous amount of work in making it
  • 00:26:28
    very clear what the expectations are so
  • 00:26:30
    we actually just got FDA clearance for
  • 00:26:32
    the one of Stanford's algorithms this
  • 00:26:35
    was about a month ago and the process
  • 00:26:38
    was was 83 days end to end from
  • 00:26:41
    submission to clearance and what's
  • 00:26:43
    exciting is that we worked with them to
  • 00:26:45
    even come up with okay we are we know
  • 00:26:47
    that we're going to be deploying this
  • 00:26:48
    algorithm it's now live at 200 hospitals
  • 00:26:50
    already and as we deploy this we're
  • 00:26:52
    bound to find cases where like oh yeah I
  • 00:26:55
    think we could come up with improvements
  • 00:26:56
    for the algorithm how do we relay that
  • 00:26:58
    back to the researcher how does the
  • 00:27:00
    researcher work towards you know
  • 00:27:02
    defining the model with that additional
  • 00:27:04
    information and what does that look like
  • 00:27:06
    currently the pros they've actually come
  • 00:27:08
    up with something called pccp which is a
  • 00:27:10
    predetermined Change Control product
  • 00:27:11
    plan and they're really thinking about
  • 00:27:13
    this in the sense of we know that these
  • 00:27:15
    algorithms will need to be trained and
  • 00:27:16
    fine-tuned in the future and how do we
  • 00:27:18
    make it such that it's not too laborious
  • 00:27:20
    to to to to get them cleared again
  • 00:27:26
    right now there seems to be also debate
  • 00:27:28
    on how that standard may change and even
  • 00:27:31
    the boundary of what will be regulated I
  • 00:27:33
    think that's an interesting topic that
  • 00:27:35
    we keep following as well for example
  • 00:27:37
    clinical decision support like the
  • 00:27:39
    sepsis algorithm that was mentioned
  • 00:27:41
    although that's killed right now but can
  • 00:27:43
    that be regulated should that be even
  • 00:27:45
    regulated and I think that's also an
  • 00:27:47
    interesting direction to follow as well
  • 00:27:50
    so one point I want to share is we
  • 00:27:52
    cannot just rely on FDA to really doing
  • 00:27:55
    ethical and responsible because it takes
  • 00:27:59
    a lot of time and a lot of resource so
  • 00:28:02
    the way we view is we you have to
  • 00:28:04
    basically build into the culture and the
  • 00:28:07
    principle of the company where you want
  • 00:28:10
    to ensure that all the data is protected
  • 00:28:13
    by privacy and all of that because
  • 00:28:16
    that's the way to build sustainable
  • 00:28:18
    model for a company you know to go for
  • 00:28:21
    the long term and I think that that's a
  • 00:28:24
    fundamental I mean like FDA you probably
  • 00:28:27
    like I mean for us we have like 52
  • 00:28:29
    comprehensive on just x-ray that people
  • 00:28:31
    say but we cannot like apply like 52
  • 00:28:35
    probably take 10 years for us to or
  • 00:28:37
    maybe more to get on of that so I think
  • 00:28:40
    that we cannot like just rely only on
  • 00:28:43
    that but we and then beside you also
  • 00:28:45
    need to get like ISO and all of those
  • 00:28:48
    equivalent and CE things like that to
  • 00:28:51
    get further but the fundamental is a
  • 00:28:54
    principle and culture it's it's
  • 00:28:56
    interesting a lot of times quality
  • 00:28:58
    Engineers really need to be partnering
  • 00:29:01
    it has to be we had many signs about the
  • 00:29:04
    quality is everyone's uh problem or
  • 00:29:07
    everyone's purpose and we turned it into
  • 00:29:09
    this is everyone's purpose and if you
  • 00:29:11
    create really efficient Quality
  • 00:29:12
    Management Systems regulatory schema it
  • 00:29:14
    actually can be a business accelerant
  • 00:29:16
    and so one of my personal when I hear oh
  • 00:29:18
    they're all these barriers I say as you
  • 00:29:20
    said there's a reason that there are
  • 00:29:22
    systems in place to protect patients we
  • 00:29:24
    just need to help evolve them and I
  • 00:29:26
    think if anyone's looking for
  • 00:29:27
    interesting jobs I think at the
  • 00:29:29
    interface of the private public and
  • 00:29:32
    Regulatory boundary whether it's with AI
  • 00:29:35
    whether it's with now cell and
  • 00:29:37
    gene-based therapies just a little plug
  • 00:29:39
    for I think that's an interesting career
  • 00:29:41
    to choose all right so we're opening it
  • 00:29:44
    up on the regulatory aspects I think you
  • 00:29:47
    hinted on this summer as well but one of
  • 00:29:50
    the biggest barriers to scaling AI for
  • 00:29:52
    broader list of findings is you know
  • 00:29:54
    having to do this verification for one
  • 00:29:57
    finding at a time and the conducting the
  • 00:29:59
    clinical studies for those so that's why
  • 00:30:01
    most people go for a few findings and so
  • 00:30:04
    on but if you want to make the change
  • 00:30:06
    and get greater adoption and and scale
  • 00:30:09
    out we need to change the process of
  • 00:30:12
    evaluations the for such large when you
  • 00:30:15
    have large number of findings to be
  • 00:30:17
    evaluated that was one of the barriers
  • 00:30:19
    we found when we had the technology but
  • 00:30:21
    the company would not go through the
  • 00:30:24
    regulatory approvals for all of these at
  • 00:30:27
    a time because they're just too much
  • 00:30:31
    paperwork and stuff to go through even
  • 00:30:34
    if you had the capability right so I
  • 00:30:36
    think that there needs to be some
  • 00:30:38
    improvements I know FDA is working on it
  • 00:30:39
    obviously the other aspect that I think
  • 00:30:42
    people have emphasized more and where we
  • 00:30:43
    have made a bit more headways in the
  • 00:30:45
    generalization capabilities when you go
  • 00:30:47
    to new deployments and how to update
  • 00:30:49
    your models and so on there you have I
  • 00:30:51
    think a bunch of Radiologists helped
  • 00:30:53
    shape that thinking there but when it
  • 00:30:56
    comes to large scale findings and
  • 00:30:58
    coverage of an entire Anatomy or
  • 00:31:02
    modality and so on we don't have yet you
  • 00:31:06
    know policies that would
  • 00:31:08
    you know ratify such software
  • 00:31:11
    this one all right any live questions
  • 00:31:17
    the panels have been exceedingly clear
  • 00:31:19
    and we do realize that we're standing
  • 00:31:20
    between us and lunch but uh any
  • 00:31:23
    um any questions here
  • 00:31:31
    oh yeah
  • 00:31:32
    um so I would love to hear your take on
  • 00:31:35
    when you might want to categorize
  • 00:31:39
    clinical decision support versus uh
  • 00:31:41
    clinical operations support and
  • 00:31:44
    what might be the toilet challenges like
  • 00:31:47
    the difference between those
  • 00:31:51
    so I'll take this answer so I think that
  • 00:31:53
    we one we target for
  • 00:31:56
    diagnosis assistant is clinical have
  • 00:32:00
    decision support so we even care about
  • 00:32:03
    like naming the product like we call Dr
  • 00:32:05
    Ray the aid so we intend to say we
  • 00:32:09
    assist we not to replace right and we
  • 00:32:12
    believe in land losses the experiment
  • 00:32:14
    religious use AI will replace there is
  • 00:32:18
    just hold on so I guess that's uh one
  • 00:32:21
    and then the second dimension we're
  • 00:32:23
    talking about like operational where we
  • 00:32:25
    build the Enterprise dimension of how to
  • 00:32:29
    Gathering all the data together on the
  • 00:32:32
    on-prem on private cloud or public Cloud
  • 00:32:35
    depending on the environment and where
  • 00:32:37
    we build analytic tool on top of that so
  • 00:32:41
    that we can prove the operational
  • 00:32:42
    efficiency so that's the way we
  • 00:32:45
    differentiate ourselves from the rest we
  • 00:32:48
    have two dimensions of capability to
  • 00:32:51
    solve the problem and comprehensive is
  • 00:32:53
    the fundamental like I mean if you do
  • 00:32:55
    like say the liver cancer we cannot just
  • 00:32:58
    look at only liver we have to look at
  • 00:33:01
    abdomen the whole area of a city and
  • 00:33:04
    find out what's wrong with that same for
  • 00:33:07
    just actually we cannot look at
  • 00:33:08
    pneumatic we have to look at 52 or
  • 00:33:11
    something like that that's you can do
  • 00:33:13
    with big data
  • 00:33:16
    all right so I'm going to close out with
  • 00:33:18
    a question for Dinesh um
  • 00:33:20
    technically what are you most excited
  • 00:33:22
    about as you look in the next five to
  • 00:33:24
    ten years it's obviously been a huge
  • 00:33:26
    amount of change you've focused your
  • 00:33:28
    career really being on the Forefront of
  • 00:33:31
    the latest Technologies what excites you
  • 00:33:33
    the most
  • 00:33:35
    um yeah I think uh the I'm really
  • 00:33:38
    excited by I think yeah what's most
  • 00:33:41
    exciting is the development of
  • 00:33:42
    multimodal systems now and applying
  • 00:33:45
    multimodal systems to Medical AI I think
  • 00:33:47
    what we've seen so far are very unimodal
  • 00:33:50
    applications where you know you're just
  • 00:33:51
    looking at images you're just looking at
  • 00:33:54
    um you know clinical text or time series
  • 00:33:56
    or whatever so being able I mean of
  • 00:33:58
    course in the actual clinical workflow
  • 00:34:00
    you're trying to integrate all of that
  • 00:34:01
    information together in order to make
  • 00:34:03
    clinical decisions so I'm really
  • 00:34:05
    interested in seeing the development of
  • 00:34:07
    multimodal systems that are able to now
  • 00:34:10
    take in you know various different
  • 00:34:11
    sources of data and you know be able to
  • 00:34:14
    provide
  • 00:34:15
    um support to clinicians by
  • 00:34:16
    incorporating all that information so
  • 00:34:18
    I'm really excited by uh developments in
  • 00:34:21
    that field and also yeah I'm just also
  • 00:34:23
    excited by uh not only I think there's a
  • 00:34:25
    lot of opportunities for medical AI in
  • 00:34:29
    various other settings like developing
  • 00:34:31
    countries and third world countries
  • 00:34:32
    where you know the opportunity duties
  • 00:34:35
    and kind of I think yeah there's a lot
  • 00:34:37
    of opportunities in that that in those
  • 00:34:38
    sectors that are not being explored as
  • 00:34:40
    much and I think that's also a really
  • 00:34:41
    exciting uh area for applying AI to uh
  • 00:34:45
    you know help support the the doctors
  • 00:34:48
    that and you know kind of ease their
  • 00:34:49
    burden in in those areas so yeah
  • 00:34:52
    excellent well thank you to the speakers
  • 00:34:53
    for giving us technical Vision but also
  • 00:34:56
    walking us through the realities on the
  • 00:34:57
    ground whether it's with regulatory or
  • 00:34:59
    the business models and people will be
  • 00:35:01
    around if you want to ask anyone any
  • 00:35:03
    further questions so a big round of
  • 00:35:04
    applause to our speakers here
タグ
  • AI in healthcare
  • medical imaging
  • patient outcomes
  • regulatory compliance
  • business models
  • open source
  • multimodal systems
  • clinical decision support
  • technology and life science
  • healthcare innovation