0:36

Welcome to Who

0:36

would have thought my name is

0:38

Sacha Heppell, Chief Marketing

0:38

Officer of SmartTab. And I'm

0:42

hosting this podcast with Robert

0:42

Niichel our Founder and CEO.

0:46

Robert's experience and

0:46

leadership and management of

0:49

pharmaceutical research and

0:49

development led to the founding

0:52

of SmartTab in 2016 to combine

0:52

wireless technology with

0:56

pharmaceutical drug delivery.

0:56

Today we explore artificial

1:00

intelligence, otherwise known as

1:00

AI, and how it can empower

1:04

doctors. We will speak with an

1:04

extraordinary machine learning

1:07

specialist and AI mastermind

1:07

about the current and future

1:11

applications of artificial

1:11

intelligence for COVID-19 and

1:15

beyond. I'll pass it over to

1:15

you, Robert to introduce our

1:19

guests today, behind the

1:19

artificial intelligence

1:22

revolution, Artur Adib and Dr.

1:22

George Hauser.

1:26

Thank you, Sacha, I'd like to introduce Artur Adib, PhD Founder and CEO

1:28

of Biocogniv Artur has built the

1:32

flight controller software for

1:32

the world's largest electric

1:35

flying car and was a senior

1:35

engineer at Twitter, a former

1:40

faculty member at the National

1:40

Institutes of Health and leads

1:43

the vision and AI for Biocogniv.

1:43

We'd also like to welcome Dr.

1:48

George Hauser, Chief Medical

1:48

Scientist at Biocogniv. Dr.

1:52

Hauser is the author of over 20

1:52

peer reviewed publications, and

1:56

specializes in research

1:56

involving artificial

1:59

intelligence and electronic

1:59

health records. At Biocogniv,

2:03

their model for COVID-19

2:03

screening is the largest

2:06

application of artificial

2:06

intelligence to COVID yet, and

2:09

they're leading the push to

2:09

empower doctors with AI. Okay,

2:14

good morning. Hello, Artur and

2:14

George, welcome to the show.

2:17

Thank you for joining us today.

2:17

Artur, can you tell us your

2:20

story, your background in AI?

2:20

And what inspired you to pursue

2:25

Biocogniv as the Founder/CEO?

2:28

Yeah, absolutely. It's a great pleasure to be here, Robert, and

2:29

Sacha, thank you for the

2:32

opportunity. My background is, I

2:32

came to this country for

2:36

graduate school in computational

2:36

and statistical physics about 20

2:41

years ago, that's back when AI

2:41

was sort of a fringe topic was

2:45

mostly talked about in academia,

2:45

to my knowledge, at the time,

2:49

there was very little being done

2:49

outside of academia. Biocogniv

2:53

started out of my catching up

2:53

with the literature of the last

2:56

few years of the medical

2:56

literature, applying AI into

3:00

diagnostics in particular. I

3:00

have even framed here in our

3:05

office, some of the early papers

3:05

that that I read that really

3:08

left an impression. Those were

3:08

papers that applied a state of

3:12

the art machine learning to

3:12

image classification in the

3:15

specific case of pathology. And

3:15

I found that it was one of the

3:20

most powerful things that one

3:20

could, simply by presenting

3:24

examples of disease state

3:24

images, and healthy in that

3:29

specific case for skin

3:29

conditions, skin lesions, that

3:34

you could train a model simply

3:34

by showing examples and it

3:37

outperformed, in some cases,

3:37

dermatologists. And I felt like

3:42

that was such a milestone for AI

3:42

20 years later, that I wanted to

3:48

be part of it. And as we now

3:48

know, there's quite the

3:51

conversation about AI in this

3:51

space. And so that's sort of the

3:55

the gist of how that all started.

3:59

And then moving

3:59

over to George, could you share

4:01

with us about your background

4:01

and your story in machine

4:04

learning and and how the

4:04

platform at about Biocogniv, how

4:08

that works?

4:09

Yeah, I echo

4:09

what Arthur said, I'm happy to

4:12

be here. Thanks for putting this

4:12

together. I think it's great to

4:14

have people start discussions

4:14

around this and see where it

4:17

leads us. So my background is in

4:17

biomedical engineering. And when

4:23

I started undergrad, wasn't that

4:23

long ago, and there was only a

4:27

few biomedical engineering

4:27

programs in the country. And I

4:30

went to one at the University of

4:30

Michigan. And when I graduated,

4:34

I went into medicine. And I was

4:34

using a lot of just like

4:39

computer technology in general.

4:39

And it felt like there was a

4:43

real need to be able to

4:43

synthesize all this information

4:47

that was being and I still think

4:47

it is, poorly organized and just

4:51

just saw an opportunity there.

4:51

So graduated medical school, and

4:55

then I went to train as a

4:55

clinical pathologist, which is

4:59

subdomain in medicine that

4:59

focuses on laboratory testing,

5:03

and in that space, I've

5:03

continued my interest in AI. And

5:07

then so when when Artur came up

5:07

with his proposal, it just

5:10

seemed like a natural fit. So

5:10

you know, we're working together

5:13

in this space now.

5:17

And then two

5:17

questions. The first one is,

5:19

what exactly does your platform

5:19

around the AI? What exactly does

5:24

it provide for the physicians?

5:26

Yeah, so the

5:26

company started last year. And

5:30

one of the things I guess I

5:30

didn't mention is, after

5:34

graduate school, I spent several

5:34

years at the National Institutes

5:36

of Health as a faculty there,

5:36

and then spent some time in

5:39

Silicon Valley, I decided that

5:39

academia wasn't quite right for

5:43

me, and then spent some time

5:43

Silicon Valley at a company,

5:46

companies like Twitter, Magic

5:46

Leap, and more recently, an

5:50

aerospace company. But the way I

5:50

think about Biocogniv, is this

5:54

marriage of software

5:54

engineering, and academic

5:57

research, and when I started

5:57

working at Biocogniv last year,

6:01

obviously, there was no

6:01

Coronavirus, and the goal of the

6:05

company was to identify unmet

6:05

needs, in hospital systems in

6:09

particular, tertiary care, and

6:09

try to help address those unmet

6:14

needs with AI. So we had some

6:14

targets we started working on

6:19

before Coronavirus hit, but then

6:19

it happened, COVID happened. And

6:23

we saw how, early, how much of

6:23

an impact it was going to have

6:27

in hospital systems here and

6:27

whatnot. So I think this is a

6:31

testament to the power of AI,

6:31

combined with the availability

6:36

of electronic medical records.

6:36

So we pivoted our efforts

6:40

towards COVID. And we were able

6:40

to come up in probably less than

6:45

five months, we were able to go

6:45

from zero to a model, product,

6:52

that could rule out COVID with

6:52

extremely high sensitivity based

6:57

only on blood test data like

6:57

complete blood count and

7:00

comprehensive metabolic panel,

7:00

which are routine labs that are

7:03

performed at hospital systems.

7:03

Again, I think this is a

7:06

testament to the power of AI.

7:06

Typically when when one wants to

7:10

come up with a new biomarker,

7:10

traditionally, this is a multi

7:14

year process tens, maybe

7:14

hundreds of millions of dollars

7:17

to understand the

7:17

pathophysiology of the disease

7:21

and then identify the molecules

7:21

that take part in the process

7:25

and then isolate the molecule

7:25

and then come up with an assay

7:28

for it and so on. I think this

7:28

is a great example of how AI can

7:31

be really helpful, like George

7:31

was alluding to, there's

7:35

hospitals in the country, in

7:35

general, has a ton of data

7:38

already. And it's generally

7:38

poorly organized and poorly

7:42

leveraged. So we think that AI

7:42

can really unlock the power of

7:45

that data and in what we call

7:45

AI-COVID. That is our first

7:49

product and is a great example

7:49

of how that can happen. And

7:54

answering your question around

7:54

the utility and how it can be

7:59

used in clinical practice. The

7:59

main reason we did this was in

8:03

response to the ongoing shortage

8:03

of kits, of testing kits, across

8:08

the country. So FDA has approved

8:08

over 200 test kits. Despite that

8:14

there is an ongoing issue with

8:14

supply chains across the

8:18

country, it varies from week to

8:18

week, one week, a hospital

8:22

system will have plenty of

8:22

swabs, plenty of reagents, and

8:25

so on. But another week, they

8:25

will run out of one of those

8:27

components. And that will set

8:27

them back. Obviously, AI doesn't

8:32

have that issue in particular,

8:32

especially because AI in

8:37

particular lives on top of very,

8:37

very routine laboratory data

8:40

that you generally don't run out

8:40

of reagents for so that's

8:44

essentially it. That's the use

8:44

case is tertiary care hospital

8:47

systems, is to help them triage

8:47

really COVID patients when

8:51

they're struggling with their

8:51

PCR kit supplies.

8:56

Talking some

8:56

more about the COVID-19. Like

8:59

you said, you went from zero to

8:59

you have on your web page listed

9:03

over 60 US based hospitals, over

9:03

200,000 patients. That's a

9:07

significant, that's pretty heavy

9:07

lift. And so, maybe you could

9:11

talk about some, number one,

9:11

challenges through that. And

9:15

then, it looks like COVID-19 is

9:15

going to be around for a while

9:18

so how do you see your

9:18

technology being continued to be

9:21

scaled up? What are your plans

9:21

there to, you know, say move it

9:25

from 200,000 patients to a

9:25

million to two million etc?

9:28

Yeah, great

9:28

question. I mean, obviously,

9:31

there's a lot of papers, a lot

9:31

of AI work being done. To your

9:35

point, like we think that one of

9:35

the key things that sets us

9:38

apart from other studies is the

9:38

sheer size. The multicenter

9:43

nature of it, 66 hospitals. Some

9:43

of it was heavy lifting in terms

9:49

of getting IRB approvals with

9:49

hospital systems and whatnot,

9:53

but the vast majority was

9:53

through a vendor or a partner

9:56

and we believe that it's the

9:56

largest, perhaps not only for

10:01

COVID, but probably for

10:01

infectious diseases application

10:05

of AI to infectious diseases and

10:05

whatnot. In terms of

10:07

multicenter, we can spend some

10:07

time talking about the, you

10:12

know, as you probably know,

10:12

there's a lot of concern around

10:15

the blackbox nature of AI,

10:15

especially physicians and

10:19

clinicians having the ability to

10:19

understand what is going on,

10:23

before they accept a certain

10:23

result. A great, probably the

10:28

largest part of earning that

10:28

trust with the community, the

10:33

medical community is making sure

10:33

that the product generalizes

10:37

well to different populations in

10:37

different health systems, often,

10:40

you will see models and AI

10:40

models that have been trained on

10:43

one or two hospital systems that

10:43

are not for a variety of

10:47

reasons, not representative of

10:47

the wild, right. So the the

10:51

field in general, where it would

10:51

be applied. And so we believe

10:55

that we can mitigate some of

10:55

these concerns by making sure

10:58

that A we look at as many

10:58

hospital systems as we can but

11:03

also B do a deeper analysis of

11:03

how does the algorithm perform

11:09

in different population groups

11:09

by demographics, for example, is

11:13

one one way that we look at it?

11:17

And then let's

11:17

talk a little bit about, how you

11:20

expand. So you started in a

11:20

certain way, and then COVID-19

11:24

came along, and you're focusing

11:24

on that? And then do you see

11:27

this technology as you expand it

11:27

being more demographic focused

11:31

or on particular, right, so

11:31

COVID-19 is a particular disease

11:36

or virus, you focus on that? And

11:36

then is there like something

11:39

else, like and does this get

11:39

broken out per disease? Or how

11:44

do you see it expanding per

11:44

category?

11:48

Like you mentioned, we think that COVID is gonna be here for a while,

11:50

unfortunately. So that's keeping

11:53

us plenty busy. But that said,

11:53

we started the company with a

11:55

with a bigger mission. And we

11:55

continue to believe in that

11:58

mission. And that is that there

11:58

is a variety of conditions for

12:02

which AI can deliver value. I'll

12:02

give you one example that we're

12:07

particularly interested in, that

12:07

will be pulmonary embolism. If

12:10

you take something like

12:10

Troponin, that is the biomarker

12:15

of choice, obviously, for

12:15

myocardial infarction, virtually

12:18

every patient that presents with

12:18

chest pain in the hospital

12:20

system will get your Troponin

12:20

ordered. We try to be the

12:22

Troponin for pulmonary embolism.

12:22

Yes, there's a marker for ruling

12:27

out PE, D-dimer. But it doesn't

12:27

have the same characteristic

12:32

performance as Troponin. So it's

12:32

not as widely used, we believe

12:35

that we can move the needle on

12:35

that. And as a result of that,

12:39

we can avoid the over

12:39

utilization of CT scans, for

12:43

example, which is considered by

12:43

probably the majority of

12:47

emergency physicians, one of the

12:47

top issues that they're facing

12:51

is again, the over utilization

12:51

of imaging, which has been

12:55

actually reflected in how payers

12:55

see this. The largest payers in

12:59

the country are not paying for

12:59

outpatient imaging, CT imaging

13:03

and it's not getting reimbursed

13:03

because they feel like it's been

13:05

over utilized. So we think we

13:05

can help with that.

13:09

And then as you

13:09

expand the pulmonary market,

13:12

that's a huge market right

13:12

there, just that one. And then

13:15

do you go back to these same

13:15

60-66 hospitals you're in? Or do

13:19

you focus more on ones that are

13:19

more advanced with cardio or

13:22

emergency rooms? Or how did how

13:22

does that roll out?

13:25

That's a great

13:25

question, I think you're getting

13:27

to sort of the core of a

13:27

successful AI product. And that

13:32

is, it has to be representative

13:32

of the data that it will

13:36

actually see in clinical

13:36

practice. And one way to do

13:40

that, obviously, is to just try

13:40

to get as many hospital systems

13:44

as you can, not only in

13:44

different geographic regions,

13:48

but also in different resource

13:48

sort of circumstances, like

13:52

small community, rural

13:52

hospitals, versus large academic

13:56

centers, and so on, because

13:56

they, they'll have different

13:58

instruments, they will have

13:58

different practices and whatnot.

14:00

So you want the model to be

14:00

robust against that. We are

14:03

working with some collaborators

14:03

at different institutions, this

14:06

will probably provide them the

14:06

most depth in terms of the

14:10

quality of the data. But it's

14:10

impractical for a startup like

14:14

ourselves to go after, let's

14:14

say, 60, hospital systems one at

14:18

a time, getting one IRB at a

14:18

time, and so on. And so we are

14:22

lineal partners, and so on for

14:22

this.

14:26

The larger

14:26

strategic partners are always

14:29

helpful, especially if we get

14:29

the correct one. So I have one

14:32

last question here, and then I'll turn it over to Sacha. So really, where do you see your

14:34

technology as it rolls out, how

14:37

it gets built out? So number

14:37

one, your technology,

14:40

specifically in the next five

14:40

years, and then in general, more

14:43

just the entire AI market? What

14:43

does that look like in five

14:47

years from now?

14:48

Yeah, that's

14:48

that's a timely question. How do

14:50

you roll this out? So I was just

14:50

reading the news yesterday and

14:54

today. The country has been hit

14:54

with a massive cyber attack in

14:58

hospital systems in the country.

14:58

Over 400 are being hit with

15:01

cyberattack, including one here

15:01

in our backyard and the

15:04

University of Vermont. And we

15:04

are very, very aware of this, we

15:09

are going to be a software based

15:09

deployment in hospital systems.

15:13

And we have hired two of some of

15:13

the the top engineering talent

15:18

out of Silicon Valley, including

15:18

one who's an expert in security.

15:21

So I would say that one of the

15:21

key issues, it's not talked

15:25

about too much, in addition to

15:25

all the blackbox nature of AI

15:29

and all of this. One of the key

15:29

issues will be if you're

15:31

connecting to a hospital system,

15:31

how will you make sure that this

15:34

is a secure connection that not

15:34

only will protect patient data,

15:38

but it'll prevent hackers from

15:38

entering the hospital system

15:41

through your system. And so

15:41

we're very much on top of this,

15:44

it's one of the top concerns

15:44

that FDA has. So we've been

15:47

having almost bi weekly meetings

15:47

with FDA to get our product

15:51

through the finish line. And

15:51

this is top of mind for them.

15:53

And right now, like literally in

15:53

the last 24 hours this this

15:57

massive breach has happened, I

15:57

think it's gonna only going to

16:00

become more important that we

16:00

address this. So I would say

16:03

that answers a part of your

16:03

question like our deployment

16:06

strategy is safety and security

16:06

first, the second part is

16:10

projecting out into the future.

16:10

I'm extremely optimistic about

16:14

what's what's happening with AI

16:14

across the board. I know that

16:18

there's there's some well

16:18

received skepticism. And I think

16:21

that's the that's necessary,

16:21

it's healthy. It forces us to

16:25

think hard, long and hard about

16:25

how we can prove safety and

16:29

effectiveness, which is sort of

16:29

FDA per view for medical

16:32

devices. But 5 to 10 years from

16:32

now, I think it's easy to

16:36

imagine a world where radiology

16:36

for example, right now, you

16:40

don't even have to project that

16:40

out that far. Like we're already

16:42

starting to see unprecedented

16:42

reimbursement pathways, a

16:46

company like Viz AI, for

16:46

example, in the last month or

16:49

two has received reimbursement

16:49

as sort of a reimbursement

16:54

pathway from CMS that for their

16:54

pure AI software, which I think

16:59

is unprecedented. I think we're

16:59

going to start seeing a lot of

17:02

folks are saying that this has

17:02

opened the floodgates for AI

17:05

reimbursement, I think we're going to start seeing more and more of this, that's certainly a

17:07

path that we want to pursue, we

17:10

want to make sure, ultimately

17:10

what we feel is that AI has the

17:15

potential to become as safe as

17:15

effective as any other medical

17:21

device, we just have to show

17:21

that that's the case. And I

17:24

think that there's mounting

17:24

evidence now that that's

17:27

becoming the case. And so in 5

17:27

years from now, 10 years from

17:30

now, I think pathology is going

17:30

to be, and I'll let George speak

17:35

somewhat to this, as a

17:35

pathologist, he knows that

17:38

there's a lot of activity in

17:38

this space, radiology is another

17:41

one. And we think Laboratory

17:41

Medicine, this is sort of our

17:45

niche, folks talk about 70% of

17:45

medical decisions being made

17:50

through laboratory results. And

17:50

imagine how much you can, how

17:54

much farther you can go by

17:54

combining multiple laboratory

17:59

results, and analyzing them and

17:59

identifying patterns that

18:03

humanly were nearly impossible

18:03

to identify these patterns. And

18:07

so that's essentially what our

18:07

product for COVID has been able

18:10

to do, is to identify patterns

18:10

in routine laboratory tests that

18:14

doctors are used to seeing every

18:14

day. But by presenting large

18:18

numbers of these results, and

18:18

ground truths, as we call them

18:23

for who's sick, who's not, then

18:23

the models have been able to

18:27

pick up these patterns. So I think we're going to start seeing more and more of this,

18:29

and how that helps hospital

18:32

systems in a variety of ways. We

18:32

already talked about avoidance

18:36

of CT scans and whatnot, we

18:36

think there's also an

18:39

opportunity to to expedite

18:39

results. Cultures, for example,

18:43

are notoriously slow, they take

18:43

on the order of days, bacterial

18:48

cultures and whatnot to come

18:48

back. Potentially, we could

18:51

shorten that time to minutes or

18:51

hours and really change patient

18:56

outcomes there. Because with

18:56

that information, doctors can

19:00

intervene accordingly.

19:03

Well, that's,

19:03

that's great. Now, that's it for

19:05

my questions. And then Sacha, go

19:05

ahead.

19:09

Thanks, Bob. So

19:09

George, I'd like to ask you

19:12

about what do we have yet to

19:12

discover? You know, as you're a

19:16

machine learning specialist, I

19:16

just like to know, kind of like

19:20

what our Artur was just saying

19:20

about identifying those patterns

19:24

and all of that data coming

19:24

together. What do we have yet to

19:28

discover about the benefits of

19:28

that, of this technology?

19:32

Yeah, I think

19:32

you can take the technology in a

19:35

lot of different directions. I

19:35

think one of the avenues that

19:38

we're taking it in is sort of

19:38

this ability to find patterns

19:44

and which otherwise may be

19:44

interpreted as noise, in the

19:47

diagnostic realm. But I think in

19:47

general, you know, depending on

19:51

how you want to box in AI, it's

19:51

going to be used in a lot of

19:54

different ways, throughout

19:54

healthcare, in the diagnostic

19:58

realm, which we've been talking

19:58

about now, but in a lot of other

20:02

ways, including more mundane

20:02

things like scheduling,

20:07

workflow, and I think you're

20:07

going to start seeing it more

20:10

integrated outside of this sort

20:10

of Crystal Palace and more

20:15

democratized to things like

20:15

visits online, no sort of

20:20

virtual doctors. And then that's

20:20

a huge shift for medicine, which

20:24

has traditionally been a very

20:24

sort of brick and mortar type of

20:30

experience that that people have

20:30

had. And I think there's a lot

20:35

of change that's going to

20:35

happen, you know, it's sort of

20:37

been rippling through a lot of

20:37

different fields, you know, from

20:40

finance for quite a while and

20:40

moving into medicine more

20:45

recently. But I think I think

20:45

we're just standing on the tip

20:48

of the iceberg here, it's going

20:48

to affect essentially every

20:51

process within a healthcare

20:51

system, at some point.

20:55

Yeah. And

20:55

focusing on one of those areas

20:59

that actually we work on here at

20:59

SmartTab, we're developing drug

21:02

delivery systems that would work

21:02

inside of some of these AI

21:06

systems. How do you see that

21:06

kind of playing out? How would

21:09

this potentially integrate with

21:09

digital medicine?

21:12

I think the

21:12

system you're describing has a

21:15

lot of different sources of

21:15

information from like image

21:18

analysis to patient records,

21:18

diagnoses. And anytime you start

21:23

to have those sort of inputs,

21:23

you get a certain amount of

21:26

complexity. And that's what AI

21:26

is good for, is sorting through

21:31

that level of complexity. So you

21:31

know, one of the additional

21:35

streams that you could add to

21:35

that would be some of the

21:38

diagnostic products that we're

21:38

developing and integrating that

21:41

with, with what you're doing to

21:41

optimize the patient outcome.

21:46

That's, I think that's what

21:46

we're trying to do here is about

21:48

that, that data integration step

21:48

and how to do that efficiently.

21:53

When it becomes overwhelming. I

21:53

mean, you guys are probably

21:57

taking quite a few images, as

21:57

your device travels through the

22:02

body and being able to process

22:02

those, analyze them and optimize

22:06

it is a difficult task for a

22:06

person to do by themselves.

22:11

And so then looking at, you know, virtual care as it's become a true

22:13

delivery model. Now, how do you

22:17

see AI really supporting that in

22:17

the short term, but also down

22:22

the road? And like, how this

22:22

will evolve?

22:25

Yeah, sure, I

22:25

think it's probably going to

22:27

speed up the whole transition to

22:27

AI, you know, if you want to

22:31

think of AI is like a train, you

22:31

know, it has a certain amount of

22:34

momentum. And I think early on

22:34

the gatekeepers of that

22:40

momentum, we're not familiar

22:40

with the technology, it was sort

22:44

of foreign to them. And so it

22:44

kept the pace of the train

22:48

moving pretty slow. And as the

22:48

the new leadership comes up, and

22:53

they've grown up with these

22:53

devices and and use them, you

22:57

know, basically their whole

22:57

lives, I think the speed of the

23:01

train is starting to pick up.

23:01

And as we transition more to

23:05

virtual care, what that's doing

23:05

is taking away the elements that

23:10

may have been traditionally

23:10

difficult to capture in sort of

23:13

structured data, things like the

23:13

physical exam, for instance. And

23:18

then that dialogue that you have

23:18

with the patient. So what the

23:22

virtual care is likely doing, if

23:22

you're looking at the big

23:26

picture of AI, it's creating

23:26

more data. And as you create

23:31

more data, and especially in a

23:31

in a way that can be analyzed,

23:35

then that's going to be able to

23:35

feed back and train the

23:40

algorithms and allow them to be

23:40

improved. And just keep the

23:45

train moving. And I think it's

23:45

starting to pick up the momentum

23:48

from that. And it'll likely keep

23:48

doing so for for a number of

23:52

factors, from just keeping costs

23:52

in line to outperforming current

23:58

standards of care. You know,

23:58

healthcare is a very human

24:02

intensive field. We have a lot

24:02

of support staff, nurses,

24:07

technologists, technicians who

24:07

have specialty training and in

24:11

each of those fields, and you

24:11

know, AI certainly has a lot of

24:15

different niches that it can

24:15

fill in any number of these

24:19

applications.

24:21

What is one that

24:21

you see is like the most

24:23

important that would really

24:23

that's really going to empower

24:27

the doctors and in the work that

24:27

they're doing. Which area would

24:31

you say is like the most

24:31

important?

24:34

I've always

24:34

tried to try to stress the

24:37

integration of computers with

24:37

with healthcare providers rather

24:41

than use words that suggest a

24:41

replacement. Because I feel like

24:48

AI does dumb things well, and

24:48

people do some things well, so a

24:52

computer might not be able to

24:52

solicit information from a

24:55

person who is a little reticent

24:55

to give that information. But

25:01

like I said, computers much

25:01

better integrating the large

25:04

amount of information. So I

25:04

think they have to work

25:07

together. And I think you've

25:07

seen some instances where, like

25:12

with the Watson, I think one of

25:12

the ways that they sort of

25:15

approached it the wrong way was

25:15

they try to pitch it as sort of

25:19

like a replacement for the for

25:19

the doctor. And I think it

25:22

didn't, it didn't work out so

25:22

well in that sort of pitch. And

25:26

remember watching the Watson

25:26

performing the Jeopardy against

25:30

Ken Jennings, I think you can

25:30

still find that on YouTube. And

25:34

the clip that stands out to me

25:34

was this point in time where,

25:38

the question was asked, and then

25:38

somebody rang in and they gave

25:41

an answer. And then it was

25:41

wrong. And Watson rang in right

25:47

after, and gave the wrong

25:47

answer, the same wrong answer

25:50

that the person before it had

25:50

done. And so it seemed like they

25:53

hadn't trained Watson to listen

25:53

to what the other person was

25:57

saying it was more sort of like

25:57

a no give its answer

26:00

independently. So you know, the

26:00

AI can fail spectacularly. And I

26:04

think it's important to keep in

26:04

mind that we're all working

26:08

together, and everyone has sort

26:08

of their advantages, and just

26:13

sort of go about it with that

26:13

general mindset.

26:16

Yeah. And as you

26:16

develop your system, are you how

26:21

do you keep that mindset alive?

26:21

While you're innovating your

26:25

system? Like, what what are some

26:25

ways that you make sure that

26:28

you're really having it be

26:28

physician-centered, or

26:32

patient-centered, that it's

26:32

going to actually really make a

26:35

difference, and not just be a

26:35

great piece of technology?

26:38

yeah, that's the goal, that's the holy grail there is to integrate it in with

26:40

what you with, with the

26:44

healthcare environment. You

26:44

don't want it to be this sort of

26:47

like warp that people don't want

26:47

to touch. It needs to be

26:51

integrated well, and you do that

26:51

with getting feedback from the

26:54

people who are going to use it,

26:54

and making sure that they

26:58

believe in it. And when they

26:58

give you honest feedback, that

27:01

it doesn't work, you try to

27:01

improve it. I think one of the

27:05

things that we really strive to

27:05

do is to actually change

27:09

outcomes. And it's very hard to

27:09

change clinical outcomes,

27:15

whether it be extending life, or

27:15

whether it be improving quality

27:19

of life, or whether it be, you

27:19

know, showing a clear monetary

27:24

savings. Those are all very hard

27:24

outcomes to achieve. And I think

27:30

working towards, you know,

27:30

proving that an AI system can do

27:34

that, in a real world setting.

27:34

That's what needs to be

27:39

demonstrated and demonstrated

27:39

repeatedly and in many different

27:43

areas, to keep that train moving

27:43

as fast as it can.

27:47

Yeah, I would

27:47

echo that, I think that there's

27:49

a lot of, there has been an

27:49

attempt to bring AI and I think

27:54

the Watson might be an example

27:54

of that, where it's, and maybe

27:58

Google, you know, it's a catch

27:58

all type of thing, like you're

28:01

bringing AI, and that's going to

28:01

be this massively disruptive

28:05

thing that is gonna, you know,

28:05

do XYZ all at once. And so one,

28:10

we feel like, it's, we're gonna

28:10

have to win this one battle at a

28:13

time, like, like George was

28:13

saying, like, boy, it's so hard

28:16

to prove that he actually moved

28:16

the needle on outcomes. So I

28:21

like to think of us as more of a

28:21

diagnostic company first, that

28:25

happens to be using AI. And so

28:25

that keeps us more grounded in

28:29

terms of the deliverable to our

28:29

customer. It's not just a fancy

28:35

gadget, it's something that will

28:35

actually improve the quality of

28:39

your diagnosis. But you can't do

28:39

that. It's incredibly hard to do

28:43

this. If you try to do

28:43

everything at once you got to

28:47

you got to carve out every

28:47

little unmet need at a time. And

28:53

boy, does it take effort to

28:53

prove that you can actually do

28:56

something in that little space.

28:56

Right. But I think that

28:59

ultimately, that's that's going

28:59

to be how the the puzzle is

29:03

going to be solved. It's like

29:03

one piece at a time and not like

29:07

everything at once, if that makes sense.

29:09

Yeah, totally,

29:09

totally makes sense. If you put

29:12

yourself in the shoes of a

29:12

doctor, maybe there's an example

29:14

that you could paint a picture?

29:14

What would that look like, as

29:18

they're doing their work?

29:19

I can speak to for example, the the COVID product that we have today. It's

29:19

going to be seamlessly integrated with the Electronic

29:21

Health Record system and we

29:23

think that all of our products are going to be like that. So it's not going to be the case

29:24

where a physician is going to have to interrupt their workflow

29:25

and to go learn like an app or

29:37

whatnot. And they're going to be

29:37

able to place an order just like

29:39

they place an order for

29:39

laboratory tests today. And

29:43

rather than that taking however

29:43

long that would normally take,

29:47

you know, we're seeing some some

29:47

hospital systems, taking days to

29:51

get a result back because they

29:51

have to do a send out and so on.

29:54

They're going to be able to just

29:54

do a routine blood test and get

29:56

the result in under one hour. So

29:56

from their from their

29:59

perspective. If it will work

29:59

just like any other lab, except

30:03

that it will be a faster

30:03

turnaround, and potentially even

30:07

more accurate than, than the

30:07

existing available tests.

30:11

And so what's

30:11

next for Biocogniv? What do you

30:14

sees the next steps for you and

30:14

what your focus is, like you

30:20

say, focusing one step at a

30:20

time, what's the next steps for

30:23

you?

30:24

Yeah, we're

30:24

laser focused on getting this

30:28

product through the FDA. We

30:28

want to really help with the

30:34

current pandemic, like we talked

30:34

about, there's, this is not

30:37

going to go away anytime soon.

30:37

And so we want to make sure that

30:40

we can help and move the needle

30:40

on testing capabilities across

30:44

the country. And the first step

30:44

towards that is really to just

30:48

prove the safety and

30:48

effectiveness of the product

30:51

with FDA. Then obviously,

30:51

pursuing reimbursement

30:54

strategies and so on. But then

30:54

we do have a pipeline, and we

30:59

like to think of us as more of

30:59

like a machinery that produces

31:02

these models, reproducible

31:02

machinery. And so we're building

31:07

a system internally that will

31:07

enable us to more efficiently

31:12

produce these models, whether

31:12

they're, you know, for

31:15

infectious diseases, or for

31:15

cardiovascular conditions, like

31:19

we talked about shouldn't

31:19

matter, but we want to build a

31:21

platform that can actually

31:21

produce them. And so we have

31:26

sort of the alpha version of

31:26

that, that we've used to, to

31:30

build AI-COVID. But ultimately,

31:30

we're building this bigger

31:34

platform for for other

31:34

conditions. But like I said, I

31:37

think it's really important to

31:37

stay focused, one piece of the

31:40

puzzle, at a time. I think COVID

31:40

is gonna keep like 90% of the

31:45

company busy for the next year

31:45

or so. And then the remaining

31:48

10% is going to be like one step

31:48

forward thinking about this

31:52

platform and in the new

31:52

indications.

31:56

Thank you for

31:56

taking that on and your

31:59

commitment to really focusing on

31:59

COVID-19. And, and making a

32:04

difference for transforming

32:04

healthcare and your commitment

32:08

to advancing this technology for

32:08

that. And we really, we admire

32:11

that and and your team for the

32:11

work you do in innovation and

32:16

sticking to it and figuring it

32:16

out. You know, this is so we

32:20

really appreciate that. Is there

32:20

anything else that you'd like to

32:26

share with us?

32:27

No, I would

32:27

just echo what you're saying, we

32:30

just went through Y Combinator

32:30

over the summer, which is

32:33

probably the number one startup

32:33

incubator in the country, and I

32:36

got to meet phenomenal

32:36

entrepreneurs there. I would

32:39

just say that, it goes right

32:39

back to you guys. Innovation is

32:43

so incredibly hard. And I have

32:43

great admiration for people who

32:48

go out of their way to pursue

32:48

innovation under very uncertain

32:53

circumstances and so on. And so

32:53

it's a rough road, but we think

32:58

it's a it's a worthy road. And

32:58

kudos to you guys. I mean, the

33:02

the product you guys are

33:02

envisioning sounds out of like a

33:05

science fiction movie. So very

33:05

much appreciate your work as

33:09

well. So thanks for having us.

33:11

Yeah, thank you for coming on today.

33:12

And how can

33:12

potential partners contact you?

33:15

Yeah,

33:15

absolutely. So probably the best

33:17

way is to just email me directly

33:17

for now, at my email address:

33:22

[email protected]

33:32

Okay, awesome.

33:32

Thanks so much for spending the

33:34

time with us. We look forward to

33:34

staying up to date on your

33:36

progress. I appreciate your

33:36

time.