Artificial Intelligence in Gastroenterology (AI for GI)

Summary

Dr. Brennan Spiegel, a gastroenterologist at Cedars-Sinai, discusses the impact of artificial intelligence (AI) in gastroenterology. He explores how AI, particularly natural language processing (NLP) and image recognition, is transforming diagnosis and treatment in the field. Topics include automated polyp detection in colonoscopies, AI-assisted diagnosis of conditions like Barrett's esophagus and ulcerative colitis, and the potential for personalized medicine. Spiegel also touches on the limitations of AI, emphasizing the importance of human judgment and the collaboration between machines and doctors for better patient outcomes.

Takeaways

• 🤖 AI in gastroenterology is rapidly advancing, focusing on how human intelligence and AI can complement each other, termed as 'augmented intelligence'.
• 📚 Artificial Intelligence (AI) includes components like machine learning, natural language processing (NLP), and image recognition, which are relevant to medical applications like gastroenterology.
• 💡 AI systems are being developed to assist doctors by automating tasks such as polyp detection in colonoscopies, improving accuracy and efficiency in diagnosis.
• 🏥 AI can augment doctors' abilities rather than replace them, allowing more time for patient consultation, decision-making, and empathy, as discussed in the book 'Deep Medicine'.
• 🔬 AI has proven useful in areas like image analysis in gastroenterology, where it can accurately identify cancerous or precancerous conditions like Barrett’s esophagus and gastric cancer.
• 📈 In clinical trials, AI-assisted reviews increased the diagnosis rate of high-grade dysplasia and cancer by over 14%, showing the potential impact on patient outcomes.
• 👁️ AI systems in colonoscopy can increase the adenoma detection rate, improving overall outcomes in screening and diagnosis of colorectal conditions.
• 🔍 AI's application in capsule endoscopy helps detect ulcers and erosions with high accuracy, reducing the burden on clinicians sifting through large image sets.
• 🧪 AI can analyze blood work (CBC) to predict colon cancer risk, offering a non-invasive way to screen for potential issues before using more invasive procedures like colonoscopies.
• 🧠 The adoption of AI in healthcare is in its early stages, and its future success will depend on more prospective validation studies, cost-effectiveness evaluations, and integration into daily clinical workflows.

Q & A

What is the main focus of Dr. Brennan Spiegel's presentation?

The main focus of Dr. Brennan Spiegel's presentation is the exciting and rapidly evolving role of artificial intelligence (AI) in gastroenterology, with an emphasis on how AI can augment human abilities in clinical settings.

What is the difference between artificial intelligence (AI) and augmented intelligence as described by Dr. Spiegel?

According to Dr. Spiegel, artificial intelligence (AI) refers to computer systems simulating human behavior, while augmented intelligence emphasizes the collaboration between human intelligence and machines to enhance natural human capabilities.

What are some components of AI that Dr. Spiegel highlights in the presentation?

Dr. Spiegel highlights several components of AI, including machine learning, natural language processing (NLP), and vision systems, with a focus on how they apply to gastroenterology.

How has AI shown its capabilities in gastroenterology according to the presentation?

AI has demonstrated its capabilities in gastroenterology through natural language processing (NLP) for diagnostic support, machine learning for risk prediction, and vision systems for detecting abnormalities such as polyps during colonoscopy.

What is an example of AI being used in real-time during colonoscopy as mentioned by Dr. Spiegel?

An example is the use of AI to detect polyps in real-time during colonoscopy by placing boxes around potential polyps, improving adenoma detection rates and assisting gastroenterologists in identifying abnormalities that might otherwise be missed.

What concerns are raised regarding the use of AI in gastroenterology?

Dr. Spiegel mentions concerns such as selection bias in the images used for training AI models, the varying quality of real-world images, and the need for prospective validation studies to ensure the efficacy and reliability of AI tools in clinical practice.

How has AI improved the detection of gastric cancer and Barrett's esophagus?

AI has shown promise in detecting early gastric cancer and Barrett's esophagus through convolutional neural networks that analyze endoscopic images, achieving high accuracy in identifying dysplasia and cancerous changes with strong area under the curve (AUC) scores.

What potential role does AI have in diagnosing conditions like ulcerative colitis and IBS?

AI can aid in diagnosing ulcerative colitis by distinguishing active from healing inflammation using endocytoscope images, and it may also be able to identify imperceptible abnormalities in IBS, potentially leading to faster and more accurate diagnosis.

What is Dr. Spiegel's perspective on using AI in routine gastroenterology practice?

Dr. Spiegel believes AI has the potential to significantly enhance gastroenterology by assisting with polyp detection, reducing radiation exposure, and identifying microscopic abnormalities. However, he stresses that more research and real-world validation are necessary before widespread adoption.

What are some future challenges for AI in gastroenterology, as discussed by Dr. Spiegel?

Future challenges include conducting more prospective validation studies, proving the impact of AI on patient outcomes, assessing cost-effectiveness, and figuring out how to integrate AI tools into routine workflow without disrupting the clinical process.

Outlines

00:00

well hi I'm Brennan Spiegel I'm a,gastroenterologist...

This paragraph discusses well hi I'm Brennan Spiegel I'm a, gastroenterologist here at Cedars-Sinai, and director of Health Services Research, for our health system and today I'm, going to give you a brief overview on, some of the exciting and fascinating, updates in the use of artificial, intelligence in gastroenterology this is,a big topic why don't we get started, here I'm going to pull up my slides...

05:01

good example of the difference between,humans and computers...

This paragraph discusses good example of the difference between,humans and computers here's this unusual,situation all the humans know, instinctively what to do the computer,thinks it's just going to cut everybody,off and that's just not going to fly now, this is an even more concerning,situation that occurred a few years ago,with this woman who just struck, um this passenger I'm sorry this,this woman who is in the passenger seat,I should say was not...

Keywords

💡Artificial Intelligence (AI)

Artificial Intelligence refers to the simulation of human intelligence in machines that are programmed to think, learn, and perform tasks typically requiring human intelligence. In the context of the video, AI's role in gastroenterology is highlighted, with examples like AI-driven polyp detection in colonoscopies and image analysis. The video discusses the growing use of AI in both clinical settings and its broader applications in medicine.

💡Natural Language Processing (NLP)

NLP is a branch of AI that focuses on the interaction between computers and human language. It involves the ability of a machine to process and analyze large amounts of natural language data. In the video, NLP is mentioned as an area where AI has demonstrated significant progress, notably with IBM Watson and ChatGPT, capable of understanding medical terminology and aiding in diagnosis and treatment plans.

💡Machine Learning

Machine Learning is a subset of AI that allows systems to learn from data, identify patterns, and make decisions with minimal human intervention. In gastroenterology, machine learning models are used to predict conditions like colon cancer and Barrett's esophagus, as mentioned in the video. The speaker refers to automated systems for diagnosing conditions and identifying patterns in medical images.

💡Image Recognition

Image Recognition is the ability of AI to process and interpret visual data from images or videos. In the video, image recognition is especially relevant in gastroenterology, where AI is trained to detect abnormalities like polyps or ulcers in colonoscopy images. This capability has been shown to improve diagnostic accuracy, such as distinguishing between cancerous and non-cancerous lesions.

💡Endoscopy

Endoscopy is a medical procedure in which a camera is used to examine the interior of a hollow organ or cavity of the body. The video focuses on how AI can assist in endoscopy by identifying polyps, ulcers, or cancers more accurately than humans alone, particularly during procedures like colonoscopies. The speaker mentions AI’s potential in detecting neoplasms and other conditions in real-time during endoscopic procedures.

💡Convolutional Neural Networks (CNN)

CNNs are a type of deep learning algorithm specifically designed for image analysis. In the video, CNNs are mentioned in the context of diagnosing gastric cancer and ulcerative colitis by analyzing images from endoscopies. The CNN model's high accuracy in classifying medical images is emphasized as a key tool for improving diagnosis in gastroenterology.

💡Barrett's Esophagus

Barrett's Esophagus is a condition where the lining of the esophagus is damaged by stomach acid, which can lead to cancer. In the video, AI’s role in detecting Barrett’s esophagus and associated neoplasms through histopathology is discussed. AI helps improve early detection of this condition by analyzing biopsy samples and endoscopic images more efficiently than traditional methods.

💡Automated Polyp Detection

Automated Polyp Detection refers to the use of AI to identify polyps during colonoscopies. The video discusses how AI systems can assist doctors by highlighting areas of concern, such as small polyps that may be missed by the human eye. This is an example of how AI enhances gastroenterological practices by increasing the adenoma detection rate, which is a critical factor in preventing colorectal cancer.

💡Area Under the Curve (AUC)

AUC is a performance measurement for classification models, where an AUC of 1.0 indicates a perfect model. In the video, AUC is frequently mentioned when discussing the accuracy of AI systems in gastroenterology. For instance, AI models for detecting gastric cancer or identifying inflammation in ulcerative colitis have high AUC values, demonstrating their effectiveness in clinical applications.

💡Deep Medicine

Deep Medicine is a concept popularized by Dr. Eric Topol, referring to the potential of AI to enhance the humanity of healthcare by allowing doctors to focus more on patient care rather than administrative tasks. The video cites this idea, explaining how AI can streamline tasks like diagnosis and data analysis, giving physicians more time for empathy, shared decision-making, and building stronger patient relationships.

Highlights

Introduction to AI in gastroenterology: AI is not replacing human intelligence but augmenting it with technologies like natural language processing (NLP) and image recognition.

Definition of AI: AI is a branch of computer science simulating intelligent behavior in machines, like passing the Turing test to imitate human intelligence.

AI's potential in gastroenterology: AI can help evaluate images in real time during procedures, like colonoscopy, and aid in diagnostic processes.

Natural language processing (NLP): AI has advanced in understanding and processing human language, highlighted by systems like IBM Watson, which outperformed Jeopardy champions.

AI in vision systems: AI in dermatology and gastroenterology helps classify conditions, like skin cancer and Barrett's esophagus, through image recognition.

The importance of image quality: Research shows that AI's accuracy may decrease with low-quality or real-world images, pointing out the need for more real-world validation.

AI for capsule endoscopy: Deep learning systems can effectively analyze thousands of images in capsule endoscopy to detect erosions, ulcers, and other abnormalities.

AI-assisted polyp detection: AI has shown 96% accuracy in identifying polyps during colonoscopies, leading to higher adenoma detection rates in randomized trials.

Machine learning for CBC-based predictions: AI systems analyzing complete blood counts (CBC) can estimate colon cancer risk without relying solely on colonoscopy.

AI for inflammation in ulcerative colitis: AI can distinguish active from healing ulcerative colitis using an endocytoscope, showing high sensitivity and specificity.

AI reducing radiation exposure: AI in interventional endoscopy can reduce radiation exposure for patients and providers by deciding when radiation should be applied.

AI and IBS diagnosis: AI has shown promise in identifying subtle abnormalities in IBS patients, sparking debate about its potential to revolutionize IBS diagnostics.

Potential overuse concerns: Some experts argue against using colonoscopy for IBS diagnosis, but AI's ability to detect imperceptible differences offers new biological insights.

Adoption challenges: AI still faces validation, cost-effectiveness studies, and workflow integration issues before being widely adopted in clinical practice.

AI and medical board exams: The speaker discusses using AI (ChatGPT) for medical board exam preparation and compares its performance with humans, showing its current limitations.

Transcripts

well hi I'm Brennan Spiegel I'm a

gastroenterologist here at Cedars-Sinai

and director of Health Services Research

for our health system and today I'm

going to give you a brief overview on

some of the exciting and fascinating

updates in the use of artificial

intelligence in gastroenterology this is

a big topic why don't we get started

here I'm going to pull up my slides

right now and start off with my cover

slide

I have no specific conflicts of interest

as it pertains to artificial

intelligence

now we now know that

social intelligence is crashing Upon Our

Shores you've probably seen chat GPT

I'll come back to that and

we're figuring out how do we combine the

human brain and all of its innate

capabilities with computers

and in some cases computers can easily

out thank us

in other instances computers are

virtually useless right now and so a

term that's starting to evolve rather

than thinking of all of this as

artificial intelligence is augmented

intelligence that's the other AI can we

augment our natural abilities as humans

and the Consciousness that we bring to

the world with machines and where is

that interface in medicine and in

Gastroenterology in particular and

that's what I'm going to talk about

today

so let's start with some basic

definitions you know what is artificial

intelligence well here's sort of the

Webster textbook definition it's simply

a branch of computer science dealing

with the simulation of intelligent

behavior in computers

it's also the capability of the image of

a machine to imitate intelligent human

behavior you may have heard of a touring

test which was put forward by Alan

Turing which was a test to see if a

human can tell the difference between

another human and a computer when faced

with the same sort of set of

conversations or interactions and we're

starting to see computers that can

pretty easily pass the touring test and

imitate intelligent human behavior

now artificial intelligence has many

different components we're not going to

go into a lot of detail today I'm going

to spend most of our time talking about

natural language processing and and

image recognition which is particularly

important in gastroenterology but I just

want to emphasize that there are many

different branches machine learning NLP

vision and speech systems and within

those there are even finer distinctions

that we're not going to go into today

which is more of a largely clinical talk

rather than an overview of AI but I just

want to be complete in showing you the

extent of what AI is now considered to

be the different methods involved

so let's talk a little bit about natural

language processing which is one area

where AI has really shown incredible uh

capabilities and you know we've heard

about this years ago when

um the Jeopardy champion here went up

against

at the time the IBM Watson computer

which has now really been eclipsed and

this was an amazing moment for

artificial total legal ease for 1200.

Watson what is Executor right

same category 1600 answer

there are the developers very excited to

see Watson the Double Jeopardy question

and it was at that moment that

um he knew he was going to lose to this

computer so that was a big moment sort

of in the public

um perception of AI uh other examples

are you know chess Grand Masters like

Gary Kasparov you know being defeated by

deep blue and computers being able to do

amazing work on a chessboard for example

now machine learning is another really

important area and we've been hearing a

lot about that with regards to

um driverless cars and automated driving

and of course Tesla's made a lot of

inroads although continues to struggle

with obtaining you know human-like

driving capabilities this is um waymo

that's been working on a form of

automated driving for many years I've

routinely as recently as yesterday see

this vehicle driving around Los Angeles

they are constantly testing out their

systems extensively but this is a video

years ago that's smooth though this car

is approaching an accident look at all

the human drivers pulling to the left

using their turn signals they can see

the lights from a long ways away the

emergency lights in that intersection

they know they can't go straight through

the intersection they're all merging

left cutting into the line probably

looking each other in the eye the waymo

decides now it's going to cut in the

line way ahead of about 50 people it

appears that the driver took over and

moved off to the right so just a really

robotic looking decision so a really

good example of the difference between

humans and computers here's this unusual

situation all the humans know

instinctively what to do the computer

thinks it's just going to cut everybody

off and that's just not going to fly now

this is an even more concerning

situation that occurred a few years ago

with this woman who just struck

um this passenger I'm sorry this

this woman who is in the passenger seat

I should say was not paying attention in

the driverless vehicle then all of a

sudden strikes a uh pedestrian and you

can see the moment that she looks up and

sees that she's actually striking his

pedestrian who who tragically died so

you know AI is here but it's not

completely here yet now Vision systems

have been particularly Advanced

particularly in medicine and this really

got its start with Dermatology which

makes sense looking at the surface of

skin can send photographs to your

dermatologist they can make a diagnosis

often just by looking at the image but

what if we can use a computer to

classify skin cancer using in this case

deep neural networks what you're looking

at here is a series of receiver operator

characteristic curves or Roc curves

you'll recall that the area under the

curve of 1.0 is a perfect perfectly

accurate test and here what they did is

they classified images by expert

clinicians and then train the computer

and they found that the AAC for cancer

melanoma and so on was extremely high

with an AUC and the 0.94 plus range

extremely accurate

now of course computers can get fooled

this is a classic image of puppy dogs

and fried chicken they look suspiciously

similar and computer can sometimes be

challenged in distinguishing these kinds

of things or here are dogs and blueberry

muffins which also look shockingly

similar but you know humans can

immediately tell them apart computers

can struggle so we still have a lot of

work to do there this book by Eric topel

called Deep medicine how artificial

intelligence can make Healthcare human

again really talks about the limitations

of computers but also the opportunities

ironically to use computers to allow us

to actually increase the humanity in

healthcare possibly by giving doctors

more time to do the things that humans

do best like Consulting with patients

and conducting shared decision making

and empathizing and understanding the

emotional components of Health Care and

so in this there's a table here of the

so-called outlandish expectations for

artificial intelligence and Healthcare a

partial list you know it's going to

outperform all doctors it's going to

diagnose the undiagnosable treat the

untreatable we're hearing a little bit

more of this these days and this book's

several years old now

but we are not going to lose our jobs to

computers in general uh if we figure out

how to continue to evolve as doctors the

doctors that don't use artificial

intelligence might end up getting

replaced faster than those who do and

that's a theme that comes up frequently

and why we're talking about this in this

conference now Chachi BT came out in uh

just recently in

um November and you may have seen this

it's pretty incredible so I just

literally typed it into it I'm giving a

talk

about the use of AI in gastroenterology

what should I discuss that's a question

I just posed to chat GPT and you can see

how quickly

and starts to answer the question the

use of AI and GI has been rapidly

evolving and here are some ideas you

might want to talk about automated polyp

detection and AI systems being trained

to detect polits and colonoscopy images

diagnostic support to help diagnose IBD

or Celiac or IBS as I'll show you today

risk prediction for various conditions

like predicting colon cancer risk or

Barrett's esophagus risk personalized

medicine using AI to predict how

patients will respond to different

treatments image analysis to look at

endoscopy images or capsule endoscopy

images okay so the computer has already

written my talk for me there are many

exciting developments in the use of a

ingi your talk could focus on any of

these topics so there you go so let's

get started here with that introduction

talking about endodermatology you know

we think of GI in a way as like

Dermatology but we're looking at the

inside so we know that ai's been very

effective in uh x extrodermatology what

about endodermatology so we always spend

time looking at these lumps and bumps

and figuring out which ones are

concerning and which ones maybe not so

and just think about all the

opportunities for computers to help

assist gastroenterologists in evaluating

in real time those images this is an

article published in the red Journal a

couple years ago looking at the promises

and the pitfalls of AI and endoscopy and

so there's been a lot of efforts to use

AI to characterize the surface features

of polyps and estimate a probability of

it being a tubular adenoma or anything

else to help us decide whether to even

remove it although I think we're going

to for the most part remove these

whether maybe we can discard them

without sending them to pathology if the

computer are so sure that something is

let's say a hyperplastic polyp these are

open questions so there are many

opportunities for GI with

endodermatology Barrett's esophagus

gastric cancers small bowel ulcers colon

pulse IBD even IBS as I'll show you in a

second so I'm going to talk about a few

of these today starting with the top

looking for Barrett's esophagus

associate neoplasia using wide area

transepithelial sampling and AI program

so a computer can actually look at the

histopathology and quickly identify

suspicious suspicious histopathology and

in this randomized trial in which there

is AI assisted review of the histopath

it increased the diagnosis of high grade

dysplasia and cancer by over 14 percent

that's really quite remarkable and

something we need to be thinking about

to support our Pathologists this is a

study using convolutional neural

networks to diagnose The Invasion depth

of gastric cancer again using images

based these are conventional Endoscopy

in this case quite sophisticated Ai and

once again looking at the area under the

curve comparing to let's say experience

endoscopy or Junior endoscopus computer

did pretty much just as well as the

experience endoscopus and estimating the

depth of penetration based upon the

surface features so again really

important opportunities for clinical

gastroenterology with a very high AUC

what about for detecting early gastric

cancer this is a study that evaluated uh

AI for early gastric cancer and the

study looks at a number of outcomes but

I just want to highlight some of the key

features in this text here as it says

here there remains considerable

selection bias amongst the images that

have been used so this is an opportunity

to improve outcomes and in addition it

says that only high quality images were

selected performance may suffer when the

software is confronted with the varying

image quality and distractors an

endoscopus routinely encounter in real

life so this is just pointing out that

we need to think about these caveats

before we go Hook Line and Sinker with

all of these studies there are really

some issues that need to be addressed

and they Pro they propose some steps to

really help improve some of the research

here the first is to use real world

quality images rather than highly

selected images and also to do

prospective evaluation so we need to do

more of that

now another area that has achieved some

interest is using automated detection of

erosions and ulcerations with wireless

capsule endoscopy again using this deep

convolutional neural network approach

again using surface Imaging in this

study the capsule can look at all sorts

of interesting features and once again

has a very high AUC in terms of

predicting evidence of ulcers and

erosions and that can be a big deal

somebody's on NSAIDs or has

surreptitious bleeding and we've got you

know 50 000 images to go through

wouldn't it be nice if the computer can

really do the work for us and so this is

an example showing that that can be

achieved with a very high AUC another

study here showing the same thing

now what about using AI to identify

histologic inflammation in the setting

of ulcerative colitis this is another

study where they used an endocytoscope

to help look for evidence of ulcerative

colitis using artificial intelligence to

distinguish active from healing

ulcerative colitis and so the

endocytoscope here has this a camera

that presses right into the mucosa and

creates these beautiful images you can

see on the right it almost looks like an

actual histopathologic specimen and

using this approach which has been

around all for a long time but

augmenting it with artificial

intelligence these investigators found a

pretty high sensitivity and specificity

and very high positive negative

predictive value and accuracy for

diagnosing or distinguishing active

inflammation versus non-active

now here we're going to turn our

attention to the use of artificial

intelligence in colonoscopy

um and so there's been a lot of very

fascinating work being done here some of

you may have used this system before in

your own Practice A system that will put

a box around polyps in real time now as

I look at these polyps I feel like I

would have seen them but some of the

smaller ones you can sort of Miss

obviously there can be false negatives

false positives true negatives true

positives two by two table but the idea

is this is pretty unobtrusive and can

help quickly identify lesions as we're

doing the procedure and so in this study

this was a study finding real-time 96

accuracy during screening colonoscopy in

identifying polyps and this was a

randomized prospective trial to see

whether use of the system can improve

the yield of endoscopy or colonoscopy

and importantly what they did find is a

higher adenoma detection rate of 29 in

the the AI supported colonoscopy versus

only 20 now both these are pretty low so

one questions if more experienced

endoscopists perhaps would find quite as

big of a Delta but nonetheless in this

randomized trial they did find a

difference including the mean number of

adenomas detected overall

now uh this is another study looking at

predictions of findings at screening

colonoscopy using a machine learning

algorithm based on complete blood counts

so this is a machine learning algorithm

out of Israel that simply looks at the

CBC through the electronic health record

is pulls up the CBC and all of its

components including you know the

differential and from that can put an

estimate on whether there might be colon

cancer this is a pretty incredible

potentially high yield way to screen for

colon cancer you can imagine there might

be false negatives false positives but

they did find some accuracy compared to

colonoscopy so we're don't even need to

just rely on colonoscopy colonoscopy

colonoscopic image analysis but also on

other types of data available to us

uh and this is a sort of review looking

at AI for the determination and

management strategy for diminutive

colorectal polyps hope hype hype hope or

help and this is a study that suggests

that we could use this to support a risk

resect and discard strategy in other

words if the Imaging at the time of

polypectomy can reliably distinguish

whether a lesion needs uh

histopathologic review or not that would

be really helpful if you look down here

the 97 not negative predictive value

meaning that if this if the AI was said

there was no problem you could pretty

much assume there's no problem and

potentially does it discard and save a

lot of money

now this was an interesting study

looking at Interventional endoscopy and

seeing if AI could be used to reduce

radiation exposure so they use an AI

system to decide when radiation should

be used or not based upon the images

that are on the screen and they're able

to demonstrate a significant reduction

in radiation exposure to both patients

and providers in the use of AI for this

interesting application

now I'm just about done but I want to

point out just over the weekend saw this

tweet that came from kenwen SIA who's on

Twitter and he just pointed out this new

study that is using I colonoscopy with

AI to diagnose IBS and that's quite

interesting because we usually think IBS

as a normal colonoscopy well that's not

necessarily true there's been some

research going back several years that

some patients with IBS may have

erythematous patches maybe they have an

increased risk of

diverticulosis for example but they used

a computer to distinguish and with a

high degree of accuracy with an area of

the curve of 0.95 could distinguish IBS

versus no IBS and so that seems really

interesting but then there's certain

sort of you know key opinion leaders

that sort of weigh in this is Alex Ford

from the UK who says well well you know

given that we should not even be

performing colonoscopies and people with

IBS uh this seems like a pointless

exercise

well I don't know if it's pointless I I

honestly feel that's short-sighted

because yes we're not suggesting no

one's suggesting right now that we

should be doing colonoscopies in people

with IBS this is a research study but

isn't it interesting if it's true that

there might actually be

abnormalities that are otherwise

imperceptible to human eyes and if so

what is the biological consequences of

that what can we learn about the biology

of IBS for example if it turns out there

are interesting differences that a

computer can identify and maybe we don't

need to do a colonoscopy in everyone but

maybe we can simply do a flex Sig in the

office and quickly diagnose IBS and

avoid a whole bunch of future tests the

point is that no one's going to change

How We Do you know IBS work up right now

but that shouldn't hold us back from

thinking creatively about using AI to

hopefully rigorously distinguish among

different conditions so I think whenever

you hear this is pointless or this is

useless you know you got to think about

about the technology adoption life cycle

we're very early in Ai and there are

certain innovators that will put forward

ideas and then there's sort of a Chasm

you need to have a certain number of

innovators using something before early

adopters get on board then the early

majority of the late majority and the

so-called laggards who really kind of

ossified want to keep the status quo

honestly I think this is about how open

are you to new ideas if you're not real

open to new ideas you'll say something

like that last study was just quote

pointless and of no use at all I think

that's really short-sighted to be honest

all right well we have many challenges

so we need more prospective validation

studies we need to demonstrate the

impact on patient outcomes we need to

evaluate whether it's cost effective

study how to implement this into routine

workflow and these are all things that

are not yet a clear some use cases are

more advanced than others I will say

that I recently posed a series of

questions from the the GI board exam

based upon our books that we've written

these acing the GI board books to chat

GPT and um well it definitely struggled

with some of the questions but it did

really well on others and so we're doing

a study now comparing it to humans so if

you guys are willing to take an

anonymous quiz

um I'm going to actually distribute that

on my Twitter account and you can go in

and take the quiz we're going to see how

you do compared to the computer it's

Anonymous so it doesn't No One's Gonna

know how you did just don't look up the

answers we want to see how uh computer

the computer compares to the humans I

actually subjected

um chat GPT to that quiz it really

didn't do great so I asked it to draw an

oil painting of itself struggling

through a medical board exam and it

actually Drew this depiction on the

right of itself taking the test which I

find absolutely fascinating that it can

draw a picture like that all right I'm

going to end there thank you very much I

hope this was a useful albeit brief

overview of artificial intelligence in

general and for gastroenterology a lot

more to come we'll be hearing incredible

updates over the next year and Beyond

and stay tuned to this space thanks so

much