wtf

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Transcript

0:01

hello and welcome to this wonderful discussion on our subject I think which literally impacts Our Lives

0:07

transforming medicine and redefining Life Life as we know it and humans as we

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know them today I think it's not just that medicines are transforming us we

0:18

are also transforming medicines so the discussion today would be about what is happening how is it happening what are

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the guard rails what should be the guard rails how do we prepare for these changes what is good and what is perhaps

0:32

something that we have to be careful about I'll be joined by three experts leaders

0:39

in the subject let me quickly introduce all three of them Robinson or Everett professor of Law and

0:46

philosophy director Duke science Society Duke University USA Megan Palmer executive director by a policy and

0:52

Leadership initiatives Adjunct professor Department of bioengineering Stanford University us kuldeep Singh rajput Chief

1:01

Executive Officer of bioformance USA and I am prankul Sharma from India

1:07

we're going to begin I'm going to start with you Anita there is a lot of exciting change which is taking place

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but while we'll discuss the examples of that uh there is there is also some anxiety

1:20

there is excitement but there is anxiety when we look at issues of digital Trend

1:27

personalized medicine we look at issues of data of of our human bodies floating

1:35

somewhere in the meadowverse perhaps how do we prepare for these changes

1:41

where we should be looking for a better life but a lot of people want a longer life how

1:46

is that playing out it's a good question hi everybody that you're here with us this morning I

1:52

am a ethicist and a futurist and what I

1:58

really focus on is how we ought to think about emerging Technologies how they

2:03

impact Our Lives what some of the risks are and really how do we maximize the

2:08

benefits of Technologies for Humanity and one of them that I've been focusing on a lot recently because of I think

2:15

this extraordinary intersection between advances in artificial intelligence and machine learning advances and

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nanotechnology and Engineering has been the developments in neurotechnology and

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I think the promises in neurotechnology are extraordinary for everything from

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really addressing some of the root causes of human suffering from neurological disease and degeneration to

2:42

Mental Illness but I've also unlocking a lot of the secrets of the human brain

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part of what I think is really exciting in that space is the coming a age of

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wearable neurotechnology so rather than implanted neurotechnology neural interface as sensors become much smaller

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as they become much more integrated into multifunctional devices so if you can

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have an EEG sensor in each ear as part of your ear pods where you also take

3:12

conference calls and you also listen to music but you have brainwave activity that is being monitored all day every

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day do we suddenly have fitbits for the brain that enable us to be able to track

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brain health in ways that we haven't been able to over time people are very used to quantifying their own different

3:33

areas you go in you get a cholesterol test you have a blood test people use now watches that have ECG sensors that

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track their heart rate they're very familiar with their heart health but there's very little up until now

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understanding of the human brain or self-reflection on it and I think think that that will be extraordinary also a

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little bit terrifying because as we realize that we can track and decode a lot of what's happening in the human

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brain it opens up significant ethical risks of who is using the data how are

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they using the data it also challenges our own self-conception you think that you are a morning person you're here

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because you believe you're a morning person you believe you work most productively first thing in the morning you believe that you're more focused

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after you have a cup of coffee and the Brain metrics start to tell you that actually it's your worst time of day for

4:26

Focus that you are the least productive you have the most mind-wandering or you

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start to see things like cognitive decline that's happening over time and a

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slowing of your mental processes how does the quantification of brainwave activity outside of our bodies where we

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can reflect on it change our conceptions of self how does it change how we think about who we are how does it change how

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other people think Who We Are so I'll just start there as a teaser to open up the conversation thank you

4:57

Megan the phrase bioengineering is so amazing I'm an economist and I know that

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a lot of scientists are here in the in the room and and watching us online as well so let me ask as an economist what

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does bioengineering mean does it mean that we create new people out of spare parts like we do with the with vehicles

5:17

and others wonderful to be here especially with

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such a packed room of science enthusiasts

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um so I'm at I'm at Stanford University now in our department of bioengineering but I also work across the university

5:37

with some of our ethics society and Technology efforts as well as with our

5:42

Institute for international studies really looking at the ways that advances in science and engineering are shaping

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our our world including the ways that we relate to each other and also to ourselves and our shifting environment

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and so it's great to be here at Davos looking at many of these uh many of these questions so some of us may be new

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to bio engineering at Stanford this is

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um the the newest engineering uh it's the newest Department the last

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Department that we opened was computer science and so you can imagine the

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impacts of this new field that are being anticipated and invested in as we look

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to what is the next revolution in our ability to solve many problems not only

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in health but also in other areas right in manufacturing and climate change and

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and Beyond um but certainly I think the advances in medicine are some of the most uh

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exciting so my own PhD was in bioengineering and specifically looking at how we understand and engineer our

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immune system our immune system is fascinating right it has capacities to

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right detect and go after things that it has never seen before and now through Fields like synthetic biology which is

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the area I've primarily focused in in the last 20 years we're developing the

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foundational tools and knowledge in order to be able to engineer our

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biological systems right the most sophisticated technology on the planet

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to be able to do new and and useful things including potentially

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reconstituting life itself from those basic components and so we're looking at

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the types of advances like can we again re-engineer our immune system to now be

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able to precisely go after cancers right that's one of the most exciting areas

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where we've seen dramatic advances but also how can we open up new interfaces with our body not only in the brain but

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also in the gut can we have sort of real-time surveillance and markers of the types of things we'd like to see so

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it's it's really quite dramatic and very exciting and you know you introduce

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another phrase which some of us would find it new synthetic biology I'm going

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to come back to you on that okay you're using a lot of artificial

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intelligence and I think I don't know if it's happening but it basically means that artificial intelligence is is doing

8:27

the clinical trials it's figuring out what's right and wrong with us and it also means the scientists and

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researchers are very worried is that true yeah so I think there's always both

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sides to the coin um if you look at the advancements what what are happening in the industry today specifically

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um you know the pandemic really accelerated two big trends um which happened worldwide today one is

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how do we deliver care and these are complex care Acute Care in the home Post

8:58

Acute Care in the home and bring that hospital into patients home and that Trend

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um we have seen significantly Advanced over the past few years on the other

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hand you mentioned clinical trials how do we virtualize the trial it's not easy

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um you know to run a clinical trial maybe there are few percentage like 20

9:21

to 30 percent of Trials which could go virtual and we have seen 10 growth in clinical trial uh going virtual year on

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year over the past four years it's a 10 billion like an interruptive what is going virtual for clinical trial mean

9:34

yeah so you know let's take an example A cardiology trial if you want to run a

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Cardiology trial typically you know every three months or six months the patient comes back to the site or the

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hospital does regular lab tests you know maybe Imaging and keeps doing that or a two

9:56

years duration all the monitoring is very episodic you know you're getting four or five data points every year or

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during the visits and when I say trials are going virtual there are a few big

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benefits to it the first one is how are we able to continuously monitor patients

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using sensors like Nita mentioned to gather all the data use the data

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captured you know to be able to analyze any complications the patients might

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have or side effects the patients might have more rapidly versus every three or

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four months what might traditionally give us very limited information second When Trials are going virtual all the

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home you know visits what traditionally would happen in the hospital are moving home so labs in the home medication in the

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home infusion in the home Radiology in the home so patients don't need to

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technically go to a site and and you know take their measurements and what

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this really enables um you know to the Pharma companies and to all of us is how can we get drugs

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faster to the market you know decentralized clinical trial or virtual clinical trial significantly reduces

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trial duration thereby giving us more richer data to be able to get products

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out to the market um on the flip side the challenge with decentralized trial is also

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um you know industry is struggling with patient recruitment um you know how can we enroll patients

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um you know faster and do we just do it in a site do we just do it in a specific

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region but what needs to really happen and really needs to scale you know

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globally is we can enroll patients from anywhere and what that enables is diverse population in the clinical trial

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you know and different demographics which all of us require today I think

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that's a great point because digital clinical trials are also about focusing on specific communities yep which you

12:06

often don't get with the diversity and and this we saw and I can give you the example of India that when we developed

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many of these covered vaccines some that were developed in India were good for Indians and the and the ethnicity

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because there's diversity even within within India and some of the ones developed in U.S they had to be tested

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in India whether it would work on the people living in a different context so that's I think that's that's uh

12:31

advancing it a lot um Nita I want to come back to you you're a futurist and atheist one of the

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questions which I've always found fascinating is about not just Precision medicine but predictive medicine so

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where it's happening is that if you have information about a person perhaps a digital twin where you know you have all

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the information about how the person's heart lungs livers work you can know

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what's going to happen to them there are some ideas around that that has an impact in insurance that has an impact

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on social connects it has an impact on your employment somebody can look up

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your CV and find the data service I don't think you're going to be around for the next three years so I'm not giving you a job right so

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it sounds fastidious but it can have very serious implications it's exciting but again how do we prepare for this

13:20

what are the guardrails that we need for it yeah it's it's such a layered issue right so once we can predict the future

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and as a futurist I will say you cannot predict the future perfectly right but once you can probabilistically and

13:33

through modeling be able to much better see what's going to happen take for example the fact that we already can

13:40

start to see signs of Alzheimer's many many decades potentially before a person starts to manifest the condition do they

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want to know um and if they don't want to know should other people have the ability to know

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should an insurance company be able to make choices about whether to cover them should an employer have access to that

13:59

information to make decisions about whether or not they are somebody that they Auto auto employ a lot of people

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and a lot of different organizations that I work with struggle with questions around genetic predictions so

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particularly for highly penetrants meaning it's very very predictive that

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you'll likely develop the disease take a disease like ALS for example but you don't know when so you have incredibly

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High prediction but very little sense of when the onset would be how do you

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counsel somebody about how to integrate that information into their lives whether or not they should do genetic testing what the implications for their

14:39

family members maybe as well because if they have that particular Gene that

14:45

particular mutation it may very well be that their children have it or it may very well implicate whether or not they

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decide to have children to pass that along to their children whether or not it could be corrected through synthetic

14:56

biology whether or not it's something they would want to correct so as we have these developments thinking about how do

15:03

we make sure that people are prepared for the information that's being developed but also that Society is

15:10

prepared for the information that's being developed as an ethicist what I I try to do is to both educate people

15:17

about the broader set of implications to help them think about the broader set of implications you want to go under

15:22

undergo genetic testing here's why genetic counseling may be valuable and the broader set of Social and

15:29

psychological and other issues that you may encounter even Financial ones that may shape how you want to think about it

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I also try to work with corporations and governments and international organizations to help to define the

15:42

principles around how that information will be used and governed in society should we make it off limits for example

15:48

for an insurance company to have access to that information about individuals and to make choices about them whether

15:54

to cover them or to exclude them should employers have access to that information should the individual have

16:00

access to that information should it come direct to them does it have to go through a trusted intermediary we have

16:06

to be in an ongoing dialogue it's very difficult for laws and regulations to keep up with the pace of innovation but

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that doesn't mean that social organizations International organizations non-profit organizations

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non-governmental organizations and the corporations can't be continuously asking the questions and addressing it

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as the developments come along that's you know you mentioned corporations and I'm going to turn to uh

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you and that Megan you know the whole idea of Precision Health and also you mentioned synthetic biology also raises

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the question about who's going to invest in the research for this we know there

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is a history of Pharma companies only investing in those Healthcare issues which can give them

16:51

the maximum return When you know that well 100 million people are going to need this particular medicine so let's

16:57

invest in it we also saw and I think covert taught us that the Western World

17:03

had literally given up on vaccines it was mostly in the emerging markets

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and therefore uh they were not investing it and that's why India for example is the largest producer of of uh vaccines

17:15

in the world because we need it the African continent needs it many of the emerging developing economies needed my

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question then to you is when you have Precision medicine will it increase the health care Gap

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because only those who can afford it and only a few people who can say well just make that special cocktail for me and I

17:35

don't mean the cocktail you had last night but but uh it will create problems there's going to

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be a cost investment you know configuration which people will have to figure out

17:48

well this is again a very multi-layered topic and it's where we have to realize

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that we have a choice right we have a choice in the types of Ambitions and

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targets that we set for ourselves as we look at the frontiers of Science and

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Innovation and medicine and their impacts in society we need to have

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ambitious targets about the types of Technologies and opportunities that we open up but we also need to couple those

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with ambitious Targets in terms of equity right and the types of experiments that we need

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organizationally in order to see what works um both in terms of outcomes in terms of

18:31

the health effects but also in terms of the trust right the trust between communities that helps us to ensure that

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the types of Innovations actually inevitably have the the intended impacts both in sort of times of stability and

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in times of of crises where new types of Health burdens emerge of which pandemics

18:53

are certainly a large one so a lot of my work over the last 20 years and

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certainly over the last dozen or so have been involved working between public

19:05

research institutions as well as a number of different private entities and working with the government both in the

19:12

U.S and the governments around the world around how we think about funding and

19:17

organizing science and medicine in order to do exactly these things right be able

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to deliver the types of Innovations but figure out the financing around it how

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to incorporate these ethical issues into that appear at all stages of of

19:36

Discovery and Innovation and we actually just had a whole nother panel just

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before this on fostering scientific collaboration across borders and there

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what I've learned is that really we do need to try many different models and the particular model might be different

19:53

in different contexts but what's key is committing ourselves to that goal where

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they're you know they're not might not be the market today but if a few public leaders stand up and say we are

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committing in order to have impacts not just on sort of personal and individual health but public and societal Health

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then I am very optimistic that we're going to get there but it won't be it won't be easy thank you I'd like you to

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keep your questions ready please uh after a quick intervention from kuldeep I'd like to come to you and get some of

20:26

your ideas and thoughts as well I have another simple question for you will

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artificial intelligence improve Equity or reduce it because we are we are we

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are looking at many such situations emerging

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the the challenge is as as both of them magnanite Jose that these are serious

20:51

complex issues with technology going to resolve them or make it worse

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I think you know my perspective is it's going to resolve it

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um and and I'll give you a few examples to illustrate that today for um you know when you do big data you

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have a lot of data and you try to build population level models you always have

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biases and you have you know when you use AI but from my perspective what AI

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can really do um you know is be able to provide personalized care to patients I'll give

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you an example on on one of the major problem in the US let's take heart failure um you know one in four patients come

21:38

back and get readmitted um you know within 30 days after discharge less than one percent of patients with

21:45

heart failure in the in the US as well as worldwide are on optimal dosage and that accounts to sixty percent of

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the reason why patients come out get you know hospitalized 160 billions dollars

21:58

wasted every year um so the question was

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um you know when when we started building and and tackling heart failure five years ago we said okay there are

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two two all these problems there are two ways we are going to solve it first

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can we predict heart failure exacerbation ahead of time so that clinicians can intervene early so we

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reduce these re-hospitalization and second is is there a way once we detect

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because we don't want to just stop there we wanted to go a step further and say can AI accurately identify the right

22:36

precise dosing for the patient so that we can get the right dose to the right patients at the right time eventually

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improving cost or reducing cost so we proved that in number of clinical trials

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today we manage almost half of the entire heart failure population and you

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know what AI could really do is capture continuous you know biomarkers from

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patients passively in the comfort of their home use all of that to detect or

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predict clinical exacerbation and we you know we're able to precisely dose you

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know the biggest challenge for us was when we went to the FDA and Regulators

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there was a big question around okay this has never been done before

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how can you know AI automatically dose a patient every patient with heart failure

23:31

90 of them in fact have multiple coma bit conditions so imagine the complexity

23:37

in terms of accurately dosing of course it was uh you know it took a lot of convincing a lot of clinical trials and

23:44

this was the first time FDA after we ran the trial granted forced ever

23:50

breakthrough designation for a software and an AI which can accurately

23:56

um you know solve the problem what level of accuracy are we talking about yeah so

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um so specifically if you look at early detection of heart failure around 90 to

24:08

93 percent um you know accuracy but for the dosage I'm worried about the dosage dosage you

24:15

know the baby reduced that issue or we we sought out that you know was we

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always had um you know when there was a level of accuracy issue and we felt that a

24:28

clinical oversight is needed we had a clinical oversight okay so you still need the humans right we needed the

24:34

humans five percent of the times oh gosh okay we needed the human we will not talk about the 95 but that's the offered

24:42

questions from no but but I'll um you know finish with one point in

24:47

in medicine I don't think AI is going to replace Physicians and nurses completely

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you know the qualification yeah nurses and Physicians have to use technology to

25:00

make them more operationally efficient right and that's how we are going to see

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industry evolving over the next five ten years fair point because actually even in a hospital room you know just

25:11

checking whether the liquids are Etc going or if they're ending their sensors which can tell and alert the nurse's

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desk that you know something is not happening please rush into it even before they physically come and check so

25:24

I appreciate your point so let's take some questions we'll have a mic here please right here the gentleman yes if

25:29

you can raise your hand so that the team can see you kindly introduce yourself thanks Jeff Richard Sayo Foundation I'd

25:35

like to connect all your parts into because they're all interesting to this so Nita you mentioned census census are

25:41

the future digitalizations of the future so we have a new sensor to replace x-rays in bone fracture healing so you

25:47

can see what's going on exactly all the time it's autonomous you can see you can tell the patient directly to their

25:52

smartphone how they should learn how much they should load so on economical benefit of that is that people don't

25:58

have to do in the hospital all the time for x-rays they don't have the checkups is telling the doctor when there's a

26:04

problem at the same time so this is also very good it's collaborative it has to be throughout everyone in the world all

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the insurance companies all the legal approval companies have to work with it and finally called it with your area AI

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this is the nice new bit thinking that we'll be able if we can push FDA to do better legal approval on this all this

26:22

information can go in for an actual clinical trial AI without having to have huge costs for this state no studies and

26:29

so on and it would say it really would save a lot of money and census that's just an example you could do this

26:34

everywhere so digitization is really the massive and it's coming as a future let

26:40

me add one thing just in response to this which is agree I think that was a really nice connection between the three

26:45

one thing that um was a little chilling to me to hear last night at dinner a

26:50

really terrific futurist Amy Webb was speaking um and she mentioned something that had

26:56

not really been on my radar but I think should be on all of our radar which is the coming possibility of deep fakes

27:02

within medicine and so as you think about the cyber security issues between

27:07

people who have sensors at home X-rays at home information traveling between the hospital and individuals you know

27:15

mobile devices and the possibility of deep fakes and generative deep fakes you

27:20

know uh you know you have problems with the fracture healing you don't have problems with the fracture healing there's a lot of potential risk that we

27:28

have to address and that's just one of the kind of areas where if we're thinking carefully about this is

27:33

exciting potential we're all incredibly excited about the possibilities of transforming Health we need to make sure

27:39

that we're attending to the risks of it so that as as we start to adopt it we're integrating additional security measures

27:46

additional ethical guard rails on it to have it really help us in the best possible way that's a great point you

27:52

know a few weeks ago India's Premier Medical Institute was hacked into we

27:59

still don't know who or why I mean we have some ideas I can't mention them now but um the lot of data of patients could

28:06

have been not just stolen but altered yes yeah it's um so I part of my work I spent

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seven years at the center for International Security and and cooperation in which I was actually one

28:17

of the few scientists working with a lot of political scientists and and Security Experts really working across domains

28:24

and just wanted to emphasize exactly this along you know every stage of innovation we also need to look at what

28:29

could go wrong in a sort of constructive way in order to build new tools but also

28:35

to do these types of scenario developments that really stretch our our thinking about uh what's possible and

28:42

underlying a lot of this is you know how do we engineer a trust and integrity into these systems at all levels and

28:50

just one last note on it I think this is an area where we can put our to work our

28:56

science and Innovation System not not you know treating these things as things to be sort of circumvented but actually

29:02

treating them as science and and design questions unto themselves and I've seen

29:07

so many different communities really being motivated around exactly asking asking those questions quick

29:13

intervention from you and then yeah I'll probably add one point to what you said you know X-ray and other things in

29:20

the home one of the things which we all need to be aware of and we have been seeing that Healthcare is too fragmented

29:28

with a lot of point-of-care solutions you know health systems for example you

29:34

know are using 10 different vendors 10 different solutions to solve a single problem but eventually what matters is

29:41

how do you deliver a holistic care to patients in the home and now the

29:46

question really is who is that going to be because pairs you know pay for outcomes for example Radiology in the

29:54

home not you know can you have a single kid at home platform which enables

30:00

management of patients throughout the care Continuum including Acute Care in the home Post Acute Care in the home

30:06

transition the patients to Chronic care and you know tailor the levels of

30:12

services you require in the comfort of their home so you know yes you know it

30:19

all depends on patient outcomes you know clinical you know benefits operational

30:24

benefits and how do we improve you know economic benefits so that's what payers

30:31

care about that's how you know Regulators look at it and you know all of us and the industry

30:38

we see and we will continue to see a lot of consolidation happening a lot of

30:44

point of Care Solutions will be integrated we'll see a lot of consolidators already emerging and that

30:49

Trend will continue in the next 12 to 24 months we could we could effectively effectively have a Erp for our bodies

30:56

all right so anyway any other questions and thoughts from from the audience

31:02

I think everybody is turned into silence yeah it's a lady right here

31:07

thank you very much for exciting discussion Milana sakolovska from University of Zurich I have a question

31:13

about AI because AI in medicine so far as I am concerned cannot compete with AI

31:19

in other parts of the of of the industry for example you will never get so much

31:25

data sets as Facebook has and so on and we know the algorithm are better when

31:31

you have bigger database so and the same algorithm the same AI is used in the

31:37

food industry to make people heart failure because they used they eat too much and the food industry want them to

31:44

eat too much so how you compete with that yeah so I think um you know there is

31:51

there there are two different things which we need to or um you know the industry need to be aware of in

31:57

healthcare as you said data is limited uh but the quality data annotated data

32:03

is very limited there's a lot of data but how do you the the kind of data you need to do the application what you are

32:10

looking for is limited um however you know in consumer world or

32:15

other industry um they are solving you know multiple problems using AI in healthcare I think

32:23

it's important that we all you know focus on one single thing what we are doing and the problem we are solving for

32:30

example Radiology or Imaging um you know an AI to be able to

32:35

accurately detect certain things works and we have seen that over and over again multiple regulatory clearances by

32:42

the FDA for that piece of technology so I think it will evolve what is extremely

32:49

critical um you know and and something for all of us to think about is Health Systems especially in the U.S

32:55

think about data as their core asset without having an ability to share it

33:01

with people share it with companies you know who could then utilize it to build you know Solutions so how are we going

33:09

to access data how are we going to get access to long-term longitudinal data

33:14

with highly accurate annotations is extremely important for us you know our

33:21

our biggest Focus has all been about how can we early detect certain clinical

33:27

complications in patients we are not using it or we are not using AI to

33:34

diagnose something and it's not going to get there immediately anytime soon as I

33:39

said if we can you know reduce the false alarm Burden rate you know of any alerts

33:46

we would be able to improve operational benefits and increase the nurse to Patient ratio significantly and that's

33:54

one of the biggest benefit of you know aibc today but being able to accurate

33:59

directly diagnose you know we are not there yet

34:06

sessions here at Davos on a

34:11

um they remark that this area in health and in Precision medicine is some of the

34:17

most exciting and certainly we've learned that quantity of data but also quality of data and being able to append

34:24

different data sets together and annotate them as a significant challenge

34:29

but we've already seen dramatic advances again in in genomics and the ability to

34:35

then look at you know the vast array of knowledge and how to how to predict targets and how to predict more than one

34:42

target you know in a Cell at a time so that we can recombine them in in new and

34:47

dramatic ways and this ends up being also at the social scale very personal right I have family member who has a

34:54

rare genetic disease and the the data set to be able to actually detect that right as a as a particular change in the

35:02

genetic code that that that delivered this was not available right 10 years

35:08

ago and so even though there are these challenges of how to um how you know how to develop these

35:15

data sets they offer really uh really dramatic discoveries and we're seeing investments in some of these other types

35:21

of everything from large language models and other types of AI tools being Unleashed within biological data sets in

35:28

ways that can now do things like predict the folding of proteins with amazing

35:35

accuracy that in many ways we we couldn't have dreamed of so the the closer you get to people right and

35:42

social systems and economic systems and otherwise the data gets even more difficult to to manage but if we started

35:49

looking even just at the the molecular scale there's dramatic things that that are possible I want to come at your

35:55

question a slightly different way I want to ask all of you a question which is I've talked about how I think that

36:01

wearable brain sensors are coming we don't have very good brain data sets

36:06

right now particularly of healthy individuals with continuous mind monitoring over time how many of you

36:11

will willingly share your data your brain data continuous monitoring of

36:18

your brain okay you didn't ask me with whom right but okay so this is a group of

36:25

scientists about a third of you raised your hand how many of you would be nervous about sharing your neural data

36:34

yeah about half of you okay I think part of the problem is a people and a social problem which is we

36:42

haven't created a system of trust for people to confidently share their data

36:48

and not fear that the data will be misused against them and also to believe that they're part of

36:55

the return on investments of sharing their data we have commodification of data that's happening by big tech

37:01

companies as you mentioned we have the modification of data that's happening in a lot of different sectors we all in

37:07

this room understand that the only way we're going to get to the tremendous insights that we need in health the only

37:12

way we'll get to the tremendous insights that we need to be able to address neurological disease and suffering is if

37:18

we can actually build large Rich data sets associated with a lot of our other behaviors and information but that means

37:25

that it's a social system problem of Designing the world in a way that

37:31

enables us to confidently share our data where it's not about access restrictions to data it's about minimizing the harms

37:39

of doing so and maximizing the benefits to all of us of sharing data but that's

37:44

why I think this is a social problem in health is that a lot of people treat it and believe that it's really incredibly

37:50

sensitive which is a proxy before I fear some form of dignitary harm or other

37:58

kind of misuse of my data against me and if we can design our world differently which we can so that that's not the fear

38:04

that people have but there's actually a benefit that they see societally for Humanity to share their data then we'll

38:12

get these insights AI will transform and revolutionize Healthcare and what it means to be human how we treat Health in

38:19

our bodies and our longevity but only if we can confidently share our data

38:25

yes I think there are two questions the three actually you know things are picking up so let's collect the

38:31

questions we have six minutes left so I'll request you all to be brief let's collect all the thoughts and then come

38:36

to the panel so I want to make a comment on the future but looking at the past

38:42

look the ancient human being invented glass

38:48

if he had a glass in his hand and it was a bottle of glass he could drink it was

38:54

clean it was great then one crazy guy took the glass broke it

38:59

and kill somebody wow that great glass now she's sweating

39:04

on human being what the society did we are always a human being found a way to discipline to

39:13

control to regulate and we used a good good a good use of to the glass the same

39:20

challenges came during the Industrial Revolution and again human being so what

39:25

I'm trying to say we as a society must regulate do more advanced to

39:31

improve our life and confidence do another some bad guy always will come the crazy guy that killson always will

39:39

come then we need to find another way how to improve it and how as a human being we leverage the value and not be

39:47

afraid of those changes thank you so sorry can I just select the questions because we have just five minutes I'd

39:53

like everybody to get four thousand yeah thank you hi it was also uh can you introduce yourself sorry my name is

39:59

Martin staller also AO Foundation it was also a similar thing I think there's a theme coming about

40:05

trust and the Anglers were interested in is how can we as the the problems and

40:11

the solutions become more complex and more difficult to explain so how can we build Society societal trust when at the

40:18

same time we have misinformation where basically someone can quite simply say oh a vaccine will make you sterile and

40:24

that's a simple solution which lots of people then become fearful how can we as a scientific Community tackle this this

40:30

societal trust that's a great point I saw on the other hand

40:35

and then the lady in the front hi Monica Monica Weinberg I'm an internist and I'm just um thank you um for a great uh

40:42

discussion um I'm really excited to hear about the monitoring with heart failure and I guess in hearing some of that I'm just

40:48

wondering I'd love to hear if there's any other like in the pipeline practical applications like things in terms of

40:54

monitoring that like might be on the Forefront for example like diabetes development of atherosclerotic disease

41:01

things like that thank you one last point from here in the front I'm sorry we're running out of time this

41:07

is this is going to be a great discussion but they will be available here for interaction later I

41:13

virtue in India I wanted to come back with tea when will these advances be

41:19

available at affordable and at scale to developing countries so URL request uh three of you is to you

41:27

know give a one minute answer to to all the points you you can freely choose whichever you want sir

41:33

okay I'll start you know with your question um you know Acute Care in the home or as

41:39

you know internist um you know Edie in the home started emerging during the pandemic or just

41:45

before the pandemic as you know emergency rooms were you know flooded every health system in the country

41:52

um you know was looking at freeing up beds so we had to you know build a whole

41:58

care model where we are able to bring emergency room and deliver that same

42:03

quality of care in the comfort of patients home with the same level of safety and and we were able to show that

42:10

and that involves over 60 different kinds of diseases uh heart failure COPD asthma pneumonia cellulitis UTI like

42:17

diabetes multiple comorbid conditions and that's where things like the point

42:23

of care solution ready geology in the home Imaging in the home IVs you know

42:28

all need to be delivered and what I'll also add is you know because it was a

42:34

such a big need CMS and the reimbursement agencies started

42:39

reimbursing you know for acute care in the home and recently there was a two-year extension so this will continue

42:47

to evolve you know and there will be multiple diseases multiple comorbit conditions and how can we bring that

42:54

holistic care to the patient in the comfort of their home seamlessly is extremely important thank you Megan I

43:00

would love to address the comment about you know technologies that can help and harm and how we navigate those that's a

43:07

lot of what my research group spends their time on and what's interesting is

43:13

we have the capacity to couple right science technology innovations that can

43:18

help us do things more safely or at least monitor the systems and you know knowing their stay 8 but also we need to

43:25

couple it with social and behavioral approaches to engineer at this at this

43:30

social scale as we discussed before so my research group at Stanford has uh folks coming from bioengineering but

43:37

also coming from social psychology and anthropology and economics in order to

43:43

really look at how do we again engineer on both of these both of these levels because it's not going to be one or the

43:51

other and also how do we design those systems to anticipate the things that that will go wrong and try to prepare in

43:58

advance so there's there's a lot of Science and Innovation to couple here thank you Peter it's hard to close out a session with

44:05

these big questions but I'm going to address the issue of trust because the way we get to confidence in the systems

44:12

the way we get to really harnessing the power for good for Humanity is by building a system of trust and I think

44:19

there's just two small pieces of this that I will say we need to be addressing now one is transparency there are errors

44:26

there are limitations we can't over hype science we can't over hype the benefits we have to be transparent about the

44:32

limitations whether that's the data sets the bias the errors anything that we see

44:38

we have to be transparent about and the second is we have to realize that it isn't just about a communication or a

44:44

one-way dialogue from scientists to the rest of the public it's a bilateral conversation the patient the

44:51

individual's lived experience is an essential part of the conversation to figure out what is the beneficial

44:57

application what are the things that people want to use great we have sensors do people want to wear them is it

45:03

something that is comfortable for them do they want to share their data if not why not how does it feel at night when

45:09

they sleep with it and I say that because as long as we're having a bilateral conversation continuously over

45:16

time as a society through this process of democratic deliberation we will both

45:21

build trust but we'll also have the use cases that are beneficial for society and the ones that Society wants not ones

45:28

that are imposed upon them thank you I'll close with just three words I think

45:33

what we all could agree on is that as science advances technology accelerates

45:40

it we have to remember security trust and Equity should be the key pillars

45:47

that would define the new building blocks of advances thank you so much for joining us and please join me in

45:53

thanking the speakers

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