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
0:13
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
0:25
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
1:13
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
2:21
nanotechnology and Engineering has been the developments in neurotechnology and
2:28
I think the promises in neurotechnology are extraordinary for everything from
2:34
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
2:48
part of what I think is really exciting in that space is the coming a age of
2:53
wearable neurotechnology so rather than implanted neurotechnology neural interface as sensors become much smaller
3:02
as they become much more integrated into multifunctional devices so if you can
3:07
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
3:20
day do we suddenly have fitbits for the brain that enable us to be able to track
3:26
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
3:41
track their heart rate they're very familiar with their heart health but there's very little up until now
3:48
understanding of the human brain or self-reflection on it and I think think that that will be extraordinary also a
3:55
little bit terrifying because as we realize that we can track and decode a lot of what's happening in the human
4:01
brain it opens up significant ethical risks of who is using the data how are
4:07
they using the data it also challenges our own self-conception you think that you are a morning person you're here
4:13
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
4:20
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
4:32
start to see things like cognitive decline that's happening over time and a
4:37
slowing of your mental processes how does the quantification of brainwave activity outside of our bodies where we
4:44
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
4:51
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
5:04
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
5:11
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
5:23
such a packed room of science enthusiasts
5:29
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
5:50
our our world including the ways that we relate to each other and also to ourselves and our shifting environment
5:58
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
6:05
to bio engineering at Stanford this is
6:10
um the the newest engineering uh it's the newest Department the last
6:16
Department that we opened was computer science and so you can imagine the
6:21
impacts of this new field that are being anticipated and invested in as we look
6:28
to what is the next revolution in our ability to solve many problems not only
6:35
in health but also in other areas right in manufacturing and climate change and
6:40
and Beyond um but certainly I think the advances in medicine are some of the most uh
6:47
exciting so my own PhD was in bioengineering and specifically looking at how we understand and engineer our
6:56
immune system our immune system is fascinating right it has capacities to
7:02
right detect and go after things that it has never seen before and now through Fields like synthetic biology which is
7:09
the area I've primarily focused in in the last 20 years we're developing the
7:15
foundational tools and knowledge in order to be able to engineer our
7:21
biological systems right the most sophisticated technology on the planet
7:26
to be able to do new and and useful things including potentially
7:33
reconstituting life itself from those basic components and so we're looking at
7:39
the types of advances like can we again re-engineer our immune system to now be
7:45
able to precisely go after cancers right that's one of the most exciting areas
7:51
where we've seen dramatic advances but also how can we open up new interfaces with our body not only in the brain but
7:58
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
8:04
it's it's really quite dramatic and very exciting and you know you introduce
8:10
another phrase which some of us would find it new synthetic biology I'm going
8:15
to come back to you on that okay you're using a lot of artificial
8:21
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
8:33
researchers are very worried is that true yeah so I think there's always both
8:39
sides to the coin um if you look at the advancements what what are happening in the industry today specifically
8:46
um you know the pandemic really accelerated two big trends um which happened worldwide today one is
8:53
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
9:04
um we have seen significantly Advanced over the past few years on the other
9:10
hand you mentioned clinical trials how do we virtualize the trial it's not easy
9:16
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
9:28
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
9:42
Cardiology trial typically you know every three months or six months the patient comes back to the site or the
9:49
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
10:04
during the visits and when I say trials are going virtual there are a few big
10:11
benefits to it the first one is how are we able to continuously monitor patients
10:16
using sensors like Nita mentioned to gather all the data use the data
10:22
captured you know to be able to analyze any complications the patients might
10:28
have or side effects the patients might have more rapidly versus every three or
10:33
four months what might traditionally give us very limited information second When Trials are going virtual all the
10:41
home you know visits what traditionally would happen in the hospital are moving home so labs in the home medication in the
10:48
home infusion in the home Radiology in the home so patients don't need to
10:54
technically go to a site and and you know take their measurements and what
11:00
this really enables um you know to the Pharma companies and to all of us is how can we get drugs
11:07
faster to the market you know decentralized clinical trial or virtual clinical trial significantly reduces
11:14
trial duration thereby giving us more richer data to be able to get products
11:20
out to the market um on the flip side the challenge with decentralized trial is also
11:27
um you know industry is struggling with patient recruitment um you know how can we enroll patients
11:35
um you know faster and do we just do it in a site do we just do it in a specific
11:41
region but what needs to really happen and really needs to scale you know
11:46
globally is we can enroll patients from anywhere and what that enables is diverse population in the clinical trial
11:53
you know and different demographics which all of us require today I think
11:59
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
12:12
many of these covered vaccines some that were developed in India were good for Indians and the and the ethnicity
12:18
because there's diversity even within within India and some of the ones developed in U.S they had to be tested
12:25
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
12:38
questions which I've always found fascinating is about not just Precision medicine but predictive medicine so
12:44
where it's happening is that if you have information about a person perhaps a digital twin where you know you have all
12:50
the information about how the person's heart lungs livers work you can know
12:55
what's going to happen to them there are some ideas around that that has an impact in insurance that has an impact
13:01
on social connects it has an impact on your employment somebody can look up
13:07
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
13:14
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
13:27
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
13:46
want to know um and if they don't want to know should other people have the ability to know
13:53
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
14:06
and a lot of different organizations that I work with struggle with questions around genetic predictions so
14:12
particularly for highly penetrants meaning it's very very predictive that
14:18
you'll likely develop the disease take a disease like ALS for example but you don't know when so you have incredibly
14:25
High prediction but very little sense of when the onset would be how do you
14:32
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
14:50
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
15:35
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
16:14
that doesn't mean that social organizations International organizations non-profit organizations
16:20
non-governmental organizations and the corporations can't be continuously asking the questions and addressing it
16:27
as the developments come along that's you know you mentioned corporations and I'm going to turn to uh
16:32
you and that Megan you know the whole idea of Precision Health and also you mentioned synthetic biology also raises
16:40
the question about who's going to invest in the research for this we know there
16:45
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
17:08
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
17:22
question then to you is when you have Precision medicine will it increase the health care Gap
17:29
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
17:42
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
17:55
that we have a choice right we have a choice in the types of Ambitions and
18:01
targets that we set for ourselves as we look at the frontiers of Science and
18:06
Innovation and medicine and their impacts in society we need to have
18:12
ambitious targets about the types of Technologies and opportunities that we open up but we also need to couple those
18:19
with ambitious Targets in terms of equity right and the types of experiments that we need
18:24
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
18:38
the types of Innovations actually inevitably have the the intended impacts both in sort of times of stability and
18:46
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
18:59
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
19:24
to deliver the types of Innovations but figure out the financing around it how
19:30
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
19:42
before this on fostering scientific collaboration across borders and there
19:47
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
20:00
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
20:06
committing in order to have impacts not just on sort of personal and individual health but public and societal Health
20:14
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
20:21
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
20:31
artificial intelligence improve Equity or reduce it because we are we are we
20:38
are looking at many such situations emerging
20:44
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
20:58
I think you know my perspective is it's going to resolve it
21:03
um and and I'll give you a few examples to illustrate that today for um you know when you do big data you
21:11
have a lot of data and you try to build population level models you always have
21:16
biases and you have you know when you use AI but from my perspective what AI
21:23
can really do um you know is be able to provide personalized care to patients I'll give
21:30
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
21:52
the reason why patients come out get you know hospitalized 160 billions dollars
21:58
wasted every year um so the question was
22:03
um you know when when we started building and and tackling heart failure five years ago we said okay there are
22:09
two two all these problems there are two ways we are going to solve it first
22:15
can we predict heart failure exacerbation ahead of time so that clinicians can intervene early so we
22:21
reduce these re-hospitalization and second is is there a way once we detect
22:27
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
22:43
improving cost or reducing cost so we proved that in number of clinical trials
22:48
today we manage almost half of the entire heart failure population and you
22:54
know what AI could really do is capture continuous you know biomarkers from
22:59
patients passively in the comfort of their home use all of that to detect or
23:05
predict clinical exacerbation and we you know we're able to precisely dose you
23:12
know the biggest challenge for us was when we went to the FDA and Regulators
23:18
there was a big question around okay this has never been done before
23:23
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
24:02
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
24:21
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
24:54
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
25:06
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
25:18
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
26:10
the insurance companies all the legal approval companies have to work with it and finally called it with your area AI
26:15
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
28:12
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
My diary of what I am thinking, since writing helps me clarify thought.
Mostly rants or thoughts about medicine, politics, and the whole damn thing of life.
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