The AI technology transforming imaging

Welcome everyone. Today's webinar Ai

technology that is transforming imaging.

I'm only gamble Becker's healthcare and

thank you very much for joining us today. Before

we begin I'm going to walk through a few

quick housekeeping instructions

first. We'll begin today's webinar with

a question and panel discussion and then we'll

have time at the end of the hour for question

and answer session. You can submit any

questions you have throughout the webinar

by typing them into the Q. And a box you see on

your screen.

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You can also submit any technical questions

into that same Q and A box. We are here

to help with that. I am

pleased to welcome our terrific speakers

today.

Dr Evan commoner is the chairman of radiology

at Montefiore Nyack Hospital

and Ceo of Hudson Valley Radiology

Associates. Dr Sonia

Gupta is the Chief medical officer of Change

healthcare and an abdominal radiologists,

radiologist. Association of florida.

Dr Commoner. Dr Gupta. Thank you so

much for joining us today. We're just thrilled to have

you here. Can I begin by asking

you each to tell us a bit more about yourself.

Dr Kamler, let's start with you.

Thank you and good afternoon everybody and

thank you molly for sharing this talk.

Uh So I'm at Montefiore

Nyack Hospital. It's a license

for 400 beds but we typically have a sense

of about 200 to 2 50

in house at any time. We

do about 60,000 visits

a year. We do about 90,000

radiology procedures a year were level two

trauma center.

We're about 30 minutes north of New york city

and we're part of the Montefiore Health system. Since

about 2015

we've been we implemented our first AI

algorithms back in 2019

and seeing a steady growth in our portfolio since

that time.

Great! Thank you so much. Dr commoner

and dr Gupta. Let me turn to you next.

Yeah, thank you so much for

hosting us. Really excited

to be here. My background

is I was an academic radiologist

previously and faculty

at Harvard Medical School and University

Hospital as

an academic radiologist. I had

great experience teaching Residents and fellows

and kind of seeing their excitement for

artificial intelligence and

during that time I also served

as an adviser and consultant for multiple

Fortune 500 companies trying

to figure out, you know, Ai strategies.

It's all kind of new right now and

I'm really excited to be here

attendees. We have a lot in front of us

today. Over the next hour, here's a few

highlights about our discussion. We'll dig

into. We're gonna cover a number

of important topics around AI and medical

imaging, specifically. Today's experts

will explore the current landscape of Ai

adoption and imaging, how AI

has helped across care teams

and what to look forward to when it comes

to partnerships and AI investments.

So that said, let's start by

doing some some level setting.

We hear quite a bit about

AI today. Dr Steptoe and commoner

folks are considering what's the best

approach to helping them determine

even if AI should be part of their practices

and if so what AI is most effective.

Can you begin by telling us where

we are today with the adoption

of AI and imaging at large.

What are you seeing Dr Gupta? I'm going

to turn to you first.

Sure, well we're at a really

exciting point I think in

A I. And radiology, so

about one third of radiologists

are currently using AI in

some capacity in their practices

and even more plan to

integrate AI into their daily practice

in the next several years.

And the way I like to think about

AI broadly speaking is that we

can divide it into two big buckets,

we can talk about image interpretation

AI and workflow related

ai.

So right now a lot of large

radiology practices and hospitals

are using aI for a variety

of image interpretation

use cases. So examples

include identifying stroke

intracranial hemorrhage and acute

pulmonary embolism

re fractures and

others are using ai more for workflow.

So workflows being,

you know, incidental finding follow up

some of the administrative tasks

that we need assistance with assistance

with radiology reporting or

even improvement in image quality and M. R.

I. And pet decreasing

that stand time and using ai to

improve the image quality.

So we have you know lots of

exciting developments in this space right

now with a lot of investment and

innovation in the Ai space.

I think it's really helpful understand one third

of radiologists using AI right

now in their practices even more like you said

playing to integrate it into their daily workflows

in the years ahead. Dr

Cameron let me turn to you and get your sense of

what you've had experience with AI.

Can you tell us about what's worked

and what hasn't for the radiologist

in your group?

So we you know my practices

we have an inpatient practice

and we also have a large outpatient practice and

our ai

algorithms that were implemented differ

between the two practices

that I have for the hospital

based practice. We focused more

on ai that will find

E. D. Critical findings that we

either might have missed or that

we want to catch earlier and move up

in our algorithm in our workflow. So we read them

faster.

We first started with these

E. D. Interpretation. I'll call them

algorithms that look for pulmonary

embolism, intracranial hemorrhage,

free air in the belly things

that should we miss them could

have severe outcomes to the patient

and things that we might want to

alert the er faster

that there's this finding. So we focused on

that first.

Then in the outpatient we started

we just recently implemented a new algorithm

for reading mammography which

at first just prioritized studies

that were abnormal but now actually will

show us the cancer and can

find 10% better the

cancers earlier by as much as

two years. So we have

implemented that in outpatient. We're toying

with. Well actually we're close to implementing

prostate um our algorithm

in our outpatient because that those are very difficult

to read. And we needed some help in reading

those as well. Uh In our

in patient we have algorithms

for care coordination. We can talk about later

that later in our discussions.

And we're also right now

implementing a test in report

generation where the er an

ai algorithm will generate

the impression in our reports to try and

not only speed up the radiologist

but also you know uh

make a cohesive report.

That uh

I guess the best way to describe it is uh

abides by standards created by the american

College of Radiology. So it kind of puts it all together

in an impression for us.

Thank you so much. It's a great overview of the different

algorithms that you your work and your

groups are associated with. Like you mentioned the one

for E. V. Interpretation

looking for pes other serious

issues that if missed would have some severe

outcomes for patients like you said. And then the

other algorithm from mammography identifying

issues up to two years sooner than otherwise

would be detected.

Um So some some great improvements that

sounds like dr commoner in clinical practice

but if we had to take a step back

and think about Nyack and usually

when these solutions are rolled out there is

some problem or problems to

be solved. Right and I'm curious

in your case what were some of the

challenges Nyack was running into?

And how did ai

help address them or drive better

workflows to coordinating care

especially with teams. Can you speak to

that a bit more?

Yeah I'm gonna uh There's

a couple different topics and I'm going

to break him out a little bit. Let's talk first about how

we integrated ai into our workflow.

Prior to ai implementation

we had a workflow manager which we had

set up to

organized the radiologist workflow

that more optimally read the studies

in concert with what the hospital's expectations

were. So we went to the

ICU team and said what time do you around

and what time do you want to report to read body. We

went to the hospital's team and asked the same

question. We went to the E. R. And ask the same

question and then we created a workflow

that more optimally coordinates

the radiologist reading with those of the

care teams we support

Using that that workflow. We were

able to reduce our E. D. turnaround

time by 38%.

And not only that but we had an agreement

with the er that we would read the er

cases within 30 minutes

And that compliance went from 79%

prior to workflow organization

to 94% which was a great

help to the emergency room. We

also uh

uh improved our I. C. U. Report turnaround

time by 50%. This was

before we even had artificial

intelligence

and our mantra at that point was we

wanted to read the most important case first.

That point case might be the I. C. U. Or

the er depending upon you know

how critical the patient's those are our

most critical patients.

But with ai we're able to add a new

new layer into that which is if you want

to read the most important case first

what's more important than a case that you know is positive.

So the ai would

scan that would scan the study

determine if there's a positive finding

and then automatically raise that case

to the top of our work list so that it was read next.

Using this adding this layer

of um

Workflow into our workflow.

We were able to improve our er

turnaround time by at least

17% and that's an underestimation

because all those cases

were positive and I could not capture

when the er was actually called with the

result because every one of them required a phone call

so that 17% is an underestimation

of the actual improvement in turnaround

time. So that was

how we you know

layered this

ai into our workflow to

try and support the hospital.

We did have challenges when we went live.

Uh, some of the challenges

was radiologists, you know, just change

management, you know, getting the radiologist to accept

it. We had a problem with the AI, taking

too long for the results to come back.

The radiologist would read the study

before the AI results came back

and we had to create workflows to try

and capture those positive results and tell the

AI tell the radiologist. I'm sorry

that there's an AI positive finally, and make

sure it coordinated with your interpretation.

Um, and then we just had some radiologists

that just didn't want to wait for the AI. But

eventually, you know, we work with our vendor, we

worked through some of the issues and

now the AI report turnaround

is such that we have the result before

the reports. Even at the radiologists

for interpretation, that was one of our main

challenges was just the speed in which

the AI results come back to us.

It makes a lot of sense. Thank you for kind of

illustrating from beginning to to date

of how this has gone at Nyack.

And I think you mentioned a few really impressive

metrics there. Dr camera in terms of

compliance with turning reads around

in 30 minutes or less your turnaround

times improving that 17%

and underestimate like you said, Is

there anything else worth mentioning about feedback

from the many teams

that your department works with? Um

In terms of how the solution has been embraced?

You mentioned there was a little bit of reluctance at first

people did get on board. But is there anything

else worth mentioning? Because as you said in your remarks

you really are trying to coordinate outcomes

for a number of different care teams

and departments and I'm just curious if there's

any feedback worth mentioning here.

So uh

we there's actually two phases of

that question when we first went live,

the information was just presented to

the radiologist uh and

the the er was thrilled

that we had it and it was something that

they spent a lot of time talking

up that we had a i to improve

not only our interpretations to find

those abnormalities

which we have had

which could be missed quite frankly

but also to speed up the result

turnaround time.

But eventually we also then moved

into a new ai

algorithm which which is in what's called a care

coordination space and what

that does is it allows you

to coordinate ai uses

the ai to coordinate various teams in

your hospital. So the most common

example is the stroke team but there's also

a product out

there that works with a

pulmonary embolism rapid response team.

But for the stroke team we were able

to coordinate care not only in my own hospital

but across the entire health system. So

what the system does is it

it the the pack system

sends the images from a brain C. T.

Or a C. T. A. Of the brain to

the ai the ai then interprets it if

it finds a positive result, it notifies

everybody

on the care team which would be the er

the neurologist and also the interventional

radiologists who do

the clot extraction and other

procedures which at another hospital.

So once they announced

the radiologist which of course then does the

final read just to make sure the Ai is correct.

Excuse me correct.

But by getting everybody on board

and moving that that result up sooner.

We can then improve outcomes because as we

know in stroke time

is brain tissue. So we

by notifying everybody on the care team.

Everyone can start organizing to

faster to take care of that patient.

And we also able to make that across

the entire hospital health system so

that all the smaller hospitals then can

send a more critical patients to the tertiary care

center for therapy as

needed. Dr

canter is a departure from the norm because

everyone on that care team, the stroke care team

is getting the same notification at the same

time. Whereas otherwise before

it might have been a

ladder of stars were a tree

that prior to this the

you know we would call the emergency room radio doctor

who then would activate the care team.

They would have to call the neurologist. They'd

have to call the pharmacy. They'd also

have to advise the Church

care center that there may be a patient coming

their way. Now everybody gets

notified at once.

They're also able to communicate by this

application. So they can not

only they can coordinate their care. Oh, I looked

at the ct of the brain. This

patient is not a candidate for intervention.

So that changes our practice here

at our local hospital. Or the

interventional radiologist could say, you know what this

patient is a candidate for for

cloud extraction or

whatever they're gonna do. And they can then

coordinate with our local team to get the patient

transferred more quickly. And as

we all know, transfers take a long time

to happen. So it really does speed

up the care of the patient by getting everyone

on the team notified at the exact

same time.

You know, it makes a little more complicated to implement such

a product because

there are more stakeholders in it

and you still have the problem that the,

you know, the Ai is not perfect, none of them are perfect.

So there's always gonna be false positives and quite frankly

they're gonna be false negatives. So the radiologist

is still an integral part of this whole process

to make sure that the AI is accurate.

But in general, you know, sometimes we

get we get activated and we have to stand down

and sometimes we have to use an old method of

activation. But its general,

it makes a significant improvement in patient care

across the entire organization, both

my own hospital and across the entire

health system.

That's such a great point that these solutions are by no

means replacements for radiologists that can help

augment them in so many ways. As your remarks

just beautifully illustrated, doctor, let

me turn to you here because I think DR commander's

remarks, they really help us see

all the various types of burdens that AI

can help reduce, whether that be cognitive,

administrative. Um can you

talk a bit about how you see

AI helping to alleviate the

burdens that physicians might otherwise run

into in their day to day practice?

Yeah, absolutely. We have to really pay

attention to AI in the workflow.

You know, we don't want to adopt

an AI solution that kind of makes

the workflow tougher for the radiologist

and puts more of a burden on them because

ultimately that will kind of hinder the adoption

and then, you know, choosing

AI partners and vendors

that are able to work

with your system and your, you

know, group to make sure that the workflow

stays efficient and

you know, there's not a lot of extra application

or clicks involved so that,

you know, the interaction is more smooth

and kind of less distracting

so that you're able to get all the benefits,

you know, without some of the negatives

that there could be with adopting a new

technology

DR Cannon or anything you would add in terms of

burdens and what you've seen really be

alleviated with the roll out of this this

tool

yeah. When we when we first implemented

look the Ai

you know is a double reed for the radiologist.

You know we we hope that

you know we know that the radiologist

has a certain error rate and we know

that the ai has a certain error rate.

We're just hoping that the two

what if you look at the swiss cheese model

of you know, errors, those

two holes don't align so that

there won't be an error that gets missed by both

of them or find this missed by both of them.

So the radiologist really ended

up liking this even though it

there's not a not a large

number of cases that that the a

I found that the radiologist did not find

but it like having that second read

that we know that second reads

improved quality. We knew that from mammography space

many years ago but we just could never afford

to have two radiologists read every

single study which is to cost too

costly. But now when

one of them is a computer

now we get the benefit of that second read

and a much more affordable cost

which should improve quality reduce

errors. Now when

we first rolled it out

we asked the radiologist to

tell us how many of these cases

the a I actually made a difference on uh

and it turned out to be not a large percentage.

But then when they asked the radiologist

saying, okay, granted it didn't improve

your

the number of cases you had

you had a change in interpretation.

You know, should we pay for this or

should we pass on this technology? And

every single one of my radiologists said

we want to keep it.

And the reason why is the

benefit. We're trying to reduce

stress for all doctors. It's it's

a burnout and stress is a

is a,

you know, entire system, you know, health

system wide problem. Every

every doctor is seeing

more patients has to work faster

and we're dealing with stress and burnout.

It's not the positive findings that

causes the stress and the radiologist,

it's the negative finds the study that has nothing

on it. Because those are the cases you're

signing off when you're saying, I don't see a lung

nodule, I don't see a pulmonary embolism

and you're hoping that you're

not hoping that you're right. But you know, you're using

your strength of your practice and experience to make sure you don't

make an error. But by saying,

I've looked at the study, I don't see anything

and now I look at the ai and the ai

doesn't see anything either.

That extra reassurance

is a big stress reliever for my radiologist,

especially when I have radiologists that are

occasionally reading outside groups, especially

when they're on call for them

for a body radiologist

to know that the A I didn't see intracranial hemorrhage

and the and the neuro radiologist to say

that the A I did not see a pulmonary embolism

was also a big

stress reliever burden, relieved to know

that there was a second algorithm

that was looking over your shoulder to make sure that

that you didn't make an error. And

that was a big stress lever and let

all my radiologists to say we want to keep

this technology.

Right. So like you said,

you you can never afford to have a second read

for every image because you said in your opening remarks

90,000 diagnostic imaging

procedures a year at Nyack. Right,

okay. So I have a question question

for you dr canter, how how do you describe

that? What you just described

for us that the physicians are feeling in terms of

that added support, do you call it to your

leadership team? Is that added protection?

Is that a safety net? How

do you describe what you just discussed?

I actually call it a safety net. I mean if you kind

of think about, you know, the trapeze artist,

you know, they're doing their job there,

you know, swinging up there. They're grabbing each other's

arms and occasionally they miss

and there's a safety net at the bottom to catch them if

they fall. That's the way I look at

it. This the ai that we have

is a safety net to make sure

that we

don't miss critical findings we have

prior to the AI implementation. We

had a patient that had an intracranial

hemorrhage that was not picked up by the radiologist

and went on to get anti

coagulation and had a bad outcome because of

that. Uh we,

the ai in retrospect,

would have picked up that case and had a different outcome

for the patient. So this this

uh technology

does significantly improve

the quality of the radiologist reads.

And it also has an unquiet

unquantifiable at this point, improvement

in our efficiency because, you

know, we don't have to look at it quite as

long because we have that safety net there as

well. So it

has to to improvements right there

for at least the radiologist workflow.

Mhm. I see attendees.

I see many questions pouring in. Thank you so much.

We have a couple more questions we're going to get through in

our prepared discussion today and then

we'll dive into your questions with

Dr Gupta and commoner. So thank you and keep

them coming in if you will. Um dr

Gupta, I'm gonna turn to you, we've we've seen

a I embedded into we're close

a great deal of the past couple of years

specifically and one of the hot

topics, if you will, that seems to be

popping up time and time again is incidental

findings from screening programs

from other exams. Can you help

us understand the impact that

AI has on the management of those.

Incidental findings.

Yeah, absolutely. You're right. It is a

hot topic and one that we're hearing

a lot more about. So

you know, I think this kind of falls into that

bucket of AI for workflow,

you know as we are starting to kind

of move past covid, we've

seen a lot of our screening programs

kind of get all of the volume of patients

that were not doing their annual

screenings, you know during 2020

and 2021. All of them

are coming back now and

incidental findings are just

popping up on a lot of these exams

and incidental findings

are findings that need further

care and follow up and

sometimes that communication gets lost

because the patient is coming in

for one issue

and then you we see an incidental

finding which actually has nothing to do with the

reason the patient came to us

or why they have their appointment but

it is equally important and it

means you know, further care either that

is repeat imaging, you know

in a certain time interval, six months or

a year or it could mean that they need

to have an appointment with a specialist.

So this presents a

big workflow problem in a lot of ways because

the communication back to the referring

physician and the patient,

you know about this finding and the fact

that they need, you know a little bit extra

care for it can sometimes get

lost and our goal is to

make sure that that finding doesn't get

left unnoticed or untreated

or just kind of lost and all the other things

that are going on in the patient's

care journey.

So ai can be used to address

this by communicating

to a patient navigator. It can be automated

to get an incidental finding

back to the referring physician and to

track that communication, you

know, whether the communication is by phone

or facts or certified mail, you

know, it can automate that process and track

it And you know, when

the finding occurs, the study can be sent

to a special work list

and it can go through a process to make sure

that the diagnosis gets back to

not only the physician but also the patient

and this kind of workflow improvement.

You know, it alleviates a lot of the manual

work that's involved. Uh you

know, again we talked earlier that Dr

has 90,000 exams, you know,

passing through the system. So if you

can imagine in a typical radiologist taking

care of probably over 100 patients

a day, you know, there's that's a lot of

incidental findings and a lot of

manual work that adds up. You know, whether

it's keeping a

list, a separate list of patients that need

to have findings that are faxed or

called to another office. You know, keeping track

of all of that can really

be automated by ai and

that's what I'm really excited about.

I think the context that you place your remarks

in about the pandemic and people who put

off care had to delay their care

and how costly that has been

for for people and for the health system and then it

comes down like you said Doctor Gupta

to even work flow and just making sure that those incidental

findings are communicated with urgency

um and if they ever slip through the cracks that is only

adding to the problem. So it's great to understand

how AI can really solve that challenge

or at least make improvements for it. Um

I wanted to talk about another hot topic

and that is the debate of implementing

ai on prem or

in the cloud dr commoner.

What has been your experience with this? Do you

see one choice as stronger than

the other?

Yeah. I actually have given a lot of thought

about this particular topic. Um

The on the on premises solution

is attractive to many hospitals because they

like to maintain control of their servers

infrastructure and also the patient

health information which is a big concern for

institutions to make sure that patient

data as it goes over the internet

is secured.

But the problem with that solution lisa my institution

is the burden of supporting that equipment, uh

the service need maintenance, they need operating

system upgrades, they need security updates

to make sure that we don't get a data breach

which would of course put the patient information

at risk.

Uh The equipment occasionally gets

end of life, there's nothing worse for me

than to have the budget. You know several million

dollars for a disc farm for

my pack system because while

it works perfectly fine it's end of life

and the vendors no longer supporting it,

that takes a million dollars out of my budget

that I could be using for something else to

basically just buy something that sits in the closet

and doesn't have any

you know our ally on it

beyond what

already exists already haven't just replacing

what it had. So the

on premises solution has a

huge cost associated with which I would

rather shift to my vendor.

Let them worry about operating system

updates. Let them worry about security patches.

Uh The cloud

services like google cloud

and amazon web services,

they're gonna be much better at security than we

are at our hospital. So

I'm basically shifting the burden to them.

I have a very small footprint at

my institution which is just a

virtual server which is very inexpensive

for me to create

and maintain and let them worry

about about everything else. If they have to

upgrade their disks then let them worry about

it. I have a fixed monthly fee

that I can budget for easily. I don't

have to worry about these. You know every

you know couple of year major updates

that I need to do to replace servers and disk

farms and the like

and I also have a much smaller I. T. Staff to manage

this. I don't have to so I have savings

there. So in general I have been

looking for for solutions that shift

the burden of infrastructure,

computer infrastructure support to my

vendors

and less on my own internal staff

and then have a kind of have

a fixed budgetary fee that I can then

maintain rather than have like

I said these you know every couple of years

major infrastructure improvements that I

got to pay for.

That makes a lot of sense. I think thanks for

outlining your your thinking and

the pros and cons there for us so cleanly.

Um let's talk about the Ai partnership

if you both bring a different point of view

to this question. But what are some

key takeaways for any attendees

who are with us today who have been debating

partnership are looking to pursue one

in the near future. What are some

things that should keep front and center

and they're thinking dr Gupta. Let me turn to you

first.

I think the first step is just to

identify the issues that you want to solve

in your practice you know and not just

get a i for the sake of getting a i

it really helps to have a clear

list of problems that you think

that ai can help with. You know space

specific areas where you'd

like some help in your practice and I

think once you've identified those problems

that you want to solve and, you know, those problems

they vary, they vary

based on practice size, you

know, geographic geographic region.

So it could be workflow related,

it could be image quality related,

you know, maybe you need help with scheduling

or image interpretation,

you know, once you identify the specific

problem that you want to solve, then

the next step is to find resources to kind

of support that journey for your practice

or your group. And our

professional societies and radiology have

really done a great job with this,

you know, radiology, especially

as you know, within medicine

is using more AI than a lot

of other specialties right now. And

so are professional societies have really been on the

cutting edge of that and they have excellent

forms to guide practices through these steps

and, you know, interacting with your

colleagues is another way contacting

friends from residents, your fellowship or

maybe colleagues working in other practices

to compare notes and ask them what they're

doing with AI or what ai they might

be planning to adopt.

That's a great way to get practical recommendations.

You know, we've heard a lot from dr

who has great hands on experience,

you know, evaluating multiple AI partnerships

and I think that's a really great

opportunity for us to learn from.

So come with

some clear goals in mind, don't just do

it to hop on the AI bandwagon, That being

a number one pointer from dr Gupta

dr Kamler, you look at things from the

other side of the aisle, What would you say

if colleagues at health systems practices

are looking for an Ai partner?

What are your recommendations? Yeah,

I think, you know, we started this with

a simple trip to the R S N. A, which

is the big radiology show every

year, the week after thanksgiving,

they have a whole floor devoted

to aI with just about

every vendor you can imagine there, it's

a good way to get a sense

of the landscape, who the players are, what

the types of solutions there are. There

are different ways you can do this, you could make,

you can go to each one individually

and set up your own solution

with them, but they're also what

are called Ai marketplace. It's kind of like

app stores for AI and there are

several vendors, you know, some of them might be

vendors you already work with like nuance for

your, which uses

power scribe for radiology reporting. Um

A lot of the pacs vendors have their own Ai

marketplaces and they kind of act as a middleman

between you and the AI vendors

and give you a common interface for all of them, it

kind of reduces your infrastructure, so

you have one server serving

all the Ai structures rather than the building

a separate server for each one.

The only problem is you got to make sure that that

workflow works for you. Uh

we I've seen a bunch of the workflows

and I think a lot of them make it harder for

the radiologist to get their work done. Radiologists

are very thoughtful

about efficiency. So you really want to

make sure that whatever solution you choose

has the minimum has a minimal impact

on the radiologist efficiency. Otherwise you're not

gonna get a big buy in from the radiologist.

The other thing I'd like to point the listeners

to is a is a new article that was just

published in the journal radiology by

big players in the radiology of traumatic

space, keith Dreyer and kurt Langlands.

They just published an article basically

going over a I governance,

how to choose a. I algorithms. It's

an excellent article about how to

to dive in ai and make

investments which

are useful for your institution, including

uh an algorithm that they developed

for deciding which ai ai

algorithms are worthwhile for your institution.

So I think if you're looking at a

I it's worth spending looking at that article,

it's very informative and helpful

suggestion. Thank you so much. We are

going to now turn to our questions from our attendees.

There are many that have come in at this

time. If any of you still have

questions that you have not yet entered into that Q and a box

on your screen by all means. Please do so

for devoting the rest of our time together to answering

your questions. Um So I'm gonna

go right to one that just spoke to what

you were sharing. Dr commoner, but an attendee

would like to know what ai vendor does dr commoner

use. Can you tell us more?

Yeah, so we use uh

we try to be vendor neutral in these talks, but

we use ai doc for our

hospital based solutions

and we also use radnet

slash deep health for our private

practice solutions for

our mammography and prostate M. R. I.

Thank you.

Another

question for you dr camera or the AI results

imported into the EMR

uh So good question.

We have the choice about whether

to do that or not. We

elected by group not to include

them in the Ai into the EMR

uh We don't store the Ai

results in our pack system, but

you could if you wanted to

we just decided that it's it's

it's you know, since we're the ultimate

arbiter were the one who makes the final

determination that we didn't think

it was necessary to put the AI result

in GmR

Great

and dr kim I'm gonna stay with you for this next question.

Dr Gupta, I would welcome your perspective

here in this one as well. I think it's an interesting question

from your perspective, have you found that

patients are more or less comfortable

with the radiology report knowing

ai was leveraged or in general,

does the patient not know what tools were

leveraged for the interpretation?

Yeah, We

you know, we did a little advertising

around ai when we first got it.

Um But not a lot.

And there's no well

That's actually not 100.

Our mammography reports do say that

they were scanned by an Ai. Just like

they used to say they are scanned by a cat program.

So we do put in our mammography reports

are radiology reports. We

do not state that it was scanned by AI.

And I do not know

uh the answer to that question of

what the patients from the patient's

perspective which they would prefer.

I know my preference would be that

I definitely would want a eye scanning because there's

no question that it improves

outcomes. Uh

It finds mistakes that,

from my personal experience there's plenty

of cases where AI is made a change

in my interpretation. There are also plenty

of times where I found something that they I did not

find as well. So it's a it's a it's

a cooperative

relationship between AI and the radiologist.

AI is not gonna be replacing radiologist

anytime soon. We're gonna

see a

An efficiency improvement from a I just

like we saw an efficiency improvement from pacs systems

back in the in the mid-2000s.

So eventually we'll see that

it will speed up the radiologists and make

us more efficient. But

I don't think it's replacing a radiologist anytime

soon. Anything.

You know that you can speak to on this when it comes to patient

attitudes, any literature research

or even anecdotal evidence and that you would

bring to the table.

I think it's so early stage right now

that it's hard to make that decision

or that question for patients because

you know like dr Kamenar said are typically

the reports don't have a little notation

that says you know AI was used.

I think it would be up to the individual practice or

hospital if they wanted to include that information.

But it is used sometimes for marketing

you know and that kind of to

let patients know that your

hospital system is in my

opinion at the cutting edge because you're evaluating

new technologies and using them

for improving patient care and

kind of taking that leap. But

I think you know I know again my patient

preference would be that my doctor

used ai uh

thank you both for sharing your thoughts

on that one. It sounds like there's still a

lot of T. V. D. In terms of

understanding patient attitudes at a large scale

about about that tool and how it's communicated to them.

Another question for

either of you or both of you but

this attending would like to know what the potential

of result bias based on the ai interpretation.

Have you implemented any different peer review

processes for for studies

using ai

doctor. Can I turn to you first for your

thoughts here and then dr Gupta if you have anything to

add I welcome your input too.

Yeah. When we first started the

Ai uh we did

have each radiologist uh

fill out a form. It was

a computer form so it wasn't it was

you know it was true positive, false positive,

true negative, false negative. And from

there we got our you know, our accuracies

and all the statistics that come with those values

and our our

measurements were similar to what was published by

the vendor.

We did ask whether a I made a difference

in the interpretation. We found that it was a very small

percentage but like I said earlier

that it wasn't so much

the number of cases that have changed report on it

was the fact that it reduced stress and

improved accuracy which overall

lead us to keep the product.

Um There's definitely a bias

in those values. There's no question

because we don't screen, we did

not screen the ai results from

the radiologist. So there's

no question, there's a bias in there. And

no we did not implement any

new algorithms around

our our radiologist um

peer review process around

this. Now we have not done that.

Have you got to anything that you would add?

Yeah it does require, you know,

like dr said it requires a manual review

which can be kind of labor intensive,

you know, to have radiologist review

the cases where either there's a disagreement

with the Ai or they're

an agreement but they just need to make sure that the

report lines up with what the AI found.

And sometimes you can do the review,

you know, after the fact, you could ask your

Ai vendor to just give you a list of the positive

cases and then you can have a

radiologist or a group of radiologists

go through the cases manually and look

at them and see if they all agree. So

I mean there's a lot of different ways to do it

but you know it can be labor

intensive on the front end and I usually

encourage practices to kind of talk to the partner

that they are considering to

talk and discuss how they want to evaluate

the metrics together, you know, so they can

kind of lean on the vendors expertise,

especially if they're deployed at multiple sites

because all the practices are kind of doing the same

thing to kind of have a list like

a checklist of how to evaluate it,

you know, can I just add one more now that I think

about it when we you know, one of the things

that dr lang locke suggested

is that you have a a

when you first implement it that you don't roll it out

to everybody you first implemented silently

in the background so that you can make sure it

works and make sure you don't get this bias. When

we rolled out our mammography algorithms.

We first did not show

the individual radiologist the results, we

would the results would go to a third radiologist.

Second radiologist

who would then get a I results and then

compare decide whether they agreed with

the Ai or the original radiologist

and then have a conversation with the radiologist.

They thought there was a mess.

Uh

that gave us a way of

kind of blinding the the radiologist

to the ai results as we tried to work

through the issues of the AI

and make sure we didn't have a bias

as as far as at the outset

of it. But eventually we un blinded

the radiologist and now the radiologist

sees the results right away. But they recommend

this first phase where you have a blinded

in

blinded go live at first

is what their recommendation is.

Great. Thank you so much for your thoughts

on that question. We have looks like we're

winding down here. Um there was

one other that dr Cameron I wanted to

share with you and get your response to

but this attendee said dr commoner you

surprised me a bit on the cloud approach.

How about the privacy of patient data?

Would you prefer to have a I run behind your firewall

and transfer data directly into your EMR

without going into a virtual server somewhere.

Let's assume the vendor still care

of all the updates and maintenance.

So that's a good question. So the way

at least our vendor does it. There's no

person patient health information that goes across

the internet. Only thing that goes across

the internet is

the images which have been de

identified at a token. So

the

the the actually the vendor itself

doesn't know who the patient is. It all stays

local on our site. So only

the images go across and then we

then say a query the server,

their server in the cloud, say I have

this token. Do you ever result back on this

token, is it yes or no, that's

it. So there's never any patient information that goes

across at all, You know, whenever

there's a cloud solution, there's got to be

uh security behind this.

Look, you know, we all do

you use the internet for our banking, at

least most of us do uh and

that seems to work pretty safely. So

I think these security issues have

been worked out around it, um at

least I have faith in those security issues.

And quite frankly, you know, there have been data

breaches and hospitals to, so

uh there's been hospitals that have

had to pay ransoms for their to

get their data back. So, you know,

I think there's on both sides,

there's security issues, but

I think the cost savings outweigh

the security issues in this issue. I do really think

the security has been worked out.

Yeah. And you know, to add to that the

information is generally more secure in the

cloud, you know, and

I think that's a common misconception that

the patient data is more secure on prem

uh you know, there's a lot of security that goes

behind a cloud solution that

is sometimes even more advanced than what an individual

hospital or health system

or practice might be, you know, keeping

I agree 100% what you just said, I agree

with that. A 100%.

Thank you to the attending for sharing their

the reaction and their thought process and raising

that question. So our panelists could expand more

on their thinking. Their let's say you've

got a few other questions in the

event that a positive finding is missed

by both the Ai and radiologist.

How will that open? Not only the radiologist

but the ai vendor to liability

any thoughts here. Dr Gupta.

Yeah, I mean, this is definitely a new

area that's kind of developing. We don't

really have a lot of legal precedent for

it right now. But I think ultimately

the radiologist is responsible for

their read and their interpretation.

Can you see it similarly to how

does ultimately it's the radiologist.

I mean, there will come a time when aI

might scan the normal studies and

only show the abnormals

to a radiologist for interpretation were

not there at the moment,

but ultimately, the radiologist,

we, as far as, I don't

know, I don't know how

the ai results get discovered by

the attorneys and that kind of process.

We're still very early in the process

of ai adoption. But

uh ultimately it's the

it's the radiologist that's responsible.

Okay,

there was a question

about reimbursing that came in from an attending doctor

Gupta. I'm gonna turn this one to you. Are

you seeing that aI is reimbursable

in some manner?

Yes, there are, you know, some

cpt codes associated with AI reimbursement

and I think that

that helps with that bottom line on

our ally that needs to be

examined when you're considering investing in

an AI solution. But you know, doctor

came and I brought up a really excellent point earlier

about the radiologist stress level

and burn out and you know, that is

kind of a soft roo you could say

because we really need radiologists

and you know, we kind of have a shortage

right Now actually. And so

really investing in the long term,

if we think about 10 years from now

we really need to keep our radiologists in the

workforce and we see rising rates of

burnout and stress. And

so, you know, if all of them are saying

they want to keep the Ai because it's helping with that,

that's a really, really important metric as well.

Mhm.

And attendees, if you are curious about

the article that dr commoner had mentioned

some questions have come in for you to re share

the publication and the author sector commoner.

So anyone who has a pen

and paper nearby can drop this down

DR camera will turn over to you to re share

that article. Sure. Um

it was just published actually I think

it's only online at the moment in the radiology

journal.

The title of the article is implementation

of clinical artificial intelligence and

radiology who decides

and how the first author

is. His last name is D A Y

D N E A D A and I A

D A Y E. But

the two principal authors are

keith Drayer, D R E

Y E R and Curtis Langlands,

L A N G L O T

Z A very

good article, very timely, especially

for this particular discussion uh

and you know I advise everyone

to review it

attending. Thank you so much for your questions.

This is all the time we have for today.

I want to thank our speakers, here's an excellent

discussion and also thank Change

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