by now we've seen how tech has really helped scientists and healthcare workers
fight the coronavirus pandemic from telehealth to iot
to all the technologies that we're using every day while working from home
tech has played an integral role in our lives i want to focus today on how
ai is helping to fight the coronavirus pandemic
stick around
hey everyone welcome back to today in tech i'm julia beauchamp i'm here with
info world senior writer serdar yugilalp and serdar you are sort
of our expert on all things ai and i'm
really glad that you could be here to chat with me today
thank you so much to be here glad to be here so i think
we can focus about how ai is helping healthcare workers and scientists fight
the pandemic a little bit better now than we could before
just because now we've seen some really practical use cases
on how everything from you know hospitals to
pharmaceutical companies have been deploying ai so i'm curious
why is ai so well suited to respond to these challenges well one of
the main things that ai does for us is that it provides automation of
processes um that would normally be very difficult
for people to do at scale um when you're looking for a
solution to a problem one of the things ai can do is help you
narrow down the possible field of solutions as well as give you a
potentially predictive solution or predictive answer
but it's the first of those two that i actually think is most immediately
useful when you have say you know 200 000
reports that you have to sift through and there may be some useful piece of
information in only five of them and you can develop an ai that would
allow you to winnow down the field to just those five
that's a big time saver and that too really points to how not every single
problem is immediately solved by ai i think
perhaps on first glance you can look at it and say
well it's artificial intelligence it should be able to do everything for me
but that's not necessarily the case not at all i mean the whole point of ai
is that it's it's a tool to augment um existing human function it's a way to
it's a way to give us um what the computer can do best so that
we can combine it with what a human being can do best
it's not necessarily designed to replace human protection it can be used to
augment human predictions and in fact if you try to use it to replace human
prediction wholesale you often get um you often get results that are highly
inaccurate and i'm curious too i mean it makes
sense as you think more about it that obviously ai is helpful in this instance
when scientists and healthcare workers are going through
tons and tons and tons of data and it obviously makes a lot of sense
that ai can really speed up the analysis of that data because
you don't want one human to get bogged down in this one task when they could be
doing other things that perhaps ai is not best suited for but i'm
curious especially as we think about and in any circumstances
you know delays in data reporting or inaccurate data how can that affect
statistical modeling and predictions well it's going to have a huge impact
especially if you don't know what the extent of the missing data is one of the
biggest problems that we've been experiencing with the pandemic right now
is a lot of incomplete data or a lot of data that is being deliberately
suppressed for instance the number of available
hospital beds the number of hospitalizations
um those kinds of of suppressions or omissions
make any model that depends on them that much less accurate
and it also becomes that much more difficult to figure out just what the
degree of error is so you could well be using a model
that no longer has any predictive value at all
so is that a reason why so many of why predictions have evolved
over time over the course of the pandemic i mean i know that that
back in march when the cdc and the sort of coronavirus task force
task force was assembled here in the united states
there were statistical models saying that there could be upwards you know
millions and millions of deaths but obviously as we've progressed in the
past few months those models have changed is that a
reason why that could have happened that's certainly one of the reasons and
it's also because there may well be multiple models
that can be used depending on the way that things played out
a model is only as good as what it is designed to predict and it may be
designed to predict something which has certain um unspoken biases in
it it may for instance one of the models may have been designed such a way to
assume a degree of a high degree of of
explosive contamination you know universally instead of it just
being um spread by super spatters in a few
specific locations under a few specific conditions
so the type of model is going to make a huge difference as well as the data
provided to it but now that we're further along
we have some better idea although not necessarily a great idea
of how these things are being spread we can also pick the models that seem to
best complement the way things have already been going
and disease modeling is really just one way that ai
can play a role in helping understand and also
in some ways combat the coronavirus pandemic so can you tell me some of the
ways that scientists and healthcare workers
have been using ai in anything from you know diagnosis
to research well research is certainly one
of the big ones um for instance i in the i think in the last talk that i
did i talked about the kaggle challenge which was um basically a
uh a challenge that was provided by the folks at kaggle
to determine the best way to get useful information
about covet 19 research from the stacks and stacks of research that are out
there so instead of having to look for look by
hand through all of the papers that are available
for something that may be relevant to your specific topic of study
you can type in a couple of keywords and you could get um
the the research that is most relevant this is in a much more sophisticated way
than you could if you was just using a search engine
and a number of other projects in this vein have come up since like aws
has a research paper search system um and there was another one developed
by a team at the kellogg school of management at northwestern university
so that they could make faster decisions about
figuring out uh which of the treatments that they have information about are
more likely to succeed so that's one way that it's used in
research i know the ways that it's used this one is probably
the one that everyone thinks of as being the most obvious one is using it to find
new treatments the antibody treatments the vaccine treatments
drug treatments one of the ways that it works there is by
modeling how the different treatments can
can bind to and act on the virus itself now with that kind of thing it's
interesting because artificial intelligence machine learning
is not necessarily the only way that you can do this computationally sometimes
you can do this computationally by simply throwing tons and tons of
computational power at the problem in parallel and sometimes you can get
results that are as good or better than an ai type model but the ai models
what they tend to do again is they tend to make it easier to narrow the field
so that you could do more targeted research and then where you're not
wasting tremendous amounts of computational
power and just brute force search another one that i came across recently
is analyzing the x-rays or chest scans of patient
data to see if they match the symptoms of
covet 19. this was interesting because part of the
problem was that in in order to make this work you have to
have data that is correctly labeled you basically have to have a radiologist
a certified radiologist a trained radiologist looking at this scan and
saying yes this is from somebody who's had cover 19
this is somebody who's in pneumonia and then being able to add that to the
model without good labeled data you can't make
a model like that work so the radiological society of north
america got together something like 600 volunteers
to go through and label 10 000 ct scans and 25 000 x-rays
to build a database out of that that could be used for further analyses
and that way you could look at you could look at someone who's had you know
just a chess scan and you could get an idea of whether or not they had covered
19 without even any further information and it's all
about teaching the machine to look for these anomalies
and that's why i what i think as i learn more about ai
and i understand more of its practical use cases
it's just really a powerful tool in identifying
the anomalies amongst you know swaths of data
that and it's just much more efficient than a human can do so you know the
radiologists can do something else exactly they could
they can leave their time for more creative work
but there's still this initial outlay of effort
that is human effort and that's no different for covet 19 research than it
than it's going to be for any other kind of research it's just that because the
time scale on this has been so compressed
and because you know that the the urgency of the situation is so grand
people have had to drop everything and start to think very quickly about how
they can put this stuff together normally something like this would have
been done over the course of several years you know if you have a disease
that is that is chronic but manageable in the
population that doesn't have explosive growth
um then you can take your time and you can create a more
uh you can create a label data data set uh
you know and take that much more time to make sure that the data is clean and now
you're trying to do this in a matter of months
that's a huge challenge i'm wondering too
are these systems that were pretty commonplace as it is in healthcare
organizations or pharmaceutical companies you know
science companies whatever it is or is this is are these use cases
specific or unique to just kova 19 research
i think again the case is that they are techniques that have been in existence
for some time that have been refined but are now being
refined at a in a much more compressed time frame
again because of the urgency of the moment
and that the way that we do that in itself is going to be useful because if
we can figure out how to drop everything you know and pivot very quickly to this
kind of of uh you know model labeling and so on so
forth in a matter of months you know when the next outbreak comes
along that we're not prepared for it'll be really useful
but even outside of that um it'll still be useful in the future you know to have
these things and to be able to use them in a slightly
less urgent time frame and again to quality to guarantee the
quality of the data and and to match up the quality of the predictions with the
with with the actual results that we get i'm curious too before i let you go i
think i'm curious a little bit about the
concept of deep learning and where that plays in
to i mean i guess sort of any sort of pandemic research
can you tell me a little bit about what it is and how it's being utilized
here generally when we talk about deep learning we're talking about a very
specific corner of ai and machine learning you
know ai being the biggest biggest overall overarching term
machine learning being a subset of that and deep learning being even smaller
subset of that the idea is that you have a set of data
which you use to create a model and the model is then used to generate
predictions about future behavior again based on past behavior and the
issue that i have generally had with using
model predictions is that again they're only going to be as good
as the data that you put into them they're not magic they may feel like it
because they can they can give you predictions that are
often quite accurate but they are more accurate um the
narrower the the scope of the prediction set is
because then you can you can make sure that when you have a narrower
data set and a cleaner data set because of the narrowness
you can guarantee much better results that way
so a lot of this i've i've always been a little skeptical about what the
significance of the predictions can mean it's
it's very it's very useful to be able to say that
this unit has a 96 chance of failure within the next 24 hours or
or this virus will has like an 85 chance of infecting people who have had
these pre-existing conditions those are useful things to have but the
downside is that if we end up using those as gospel as dogma
for how things are going to go if we don't match those aggressively to
the actual outcomes and try to figure out you know
where the model can be improved and also to what degree is it really useful to
take this information on face value that's really what's most
important it's not necessarily about generating more accurate predictions
it's about understanding how those predictions are really
supposed to be used so this is a situation in which ai
in this use case is going to hopefully at least be
constantly evolving as more information and more data becomes available
very much so and it's good that we're targeting some
fairly specific use cases you know looking looking for for instance for a
protein receptor that will bind with this particular you know
thing on the virus that kind of situation that's very narrow and it's
very focused and you can get very good results that
way what you do that outside of that is another story and that's really where
the where the big challenges are it's not necessarily being able to come up
with a better predictive model for these really specific research tasks
that's actually not that hard as long as you have good data and you
have a good labeling system for it what's harder is to know what to do with
it in the real world great well thank you so much sirdar i
really appreciate you calling in and chatting with me about
this i think it's so interesting to get a more in-depth understanding of
just how different technologies are really
touching on obviously the touch on every aspect of our life i think we've all
known that for decades at this point but understanding
really how these practical use cases are helping
our everyday lives and scientists healthcare workers are
like you know we're all in this together so i really appreciate you calling in
and chatting with me about it glad to be here and thank you all so
much for watching this episode of today in tech
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if you have any questions or comments about ai
especially in regards to the specific use case fighting the kovid 19 pandemic
please leave them in the comments below i will get back to you or i'll get back
to you on certain behalf thank you all so much again for watching
and i'll see you next time