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By now, it is widely accepted that artificial intelligence (AI) will reshape contemporary medicine. The question is simply when this hypothetical will become an everyday reality. For gastroenterologists involved in the management of inflammatory bowel disease (IBD), the waiting period may be ending.
AI “is the next step in clinical care,” Jacob Kurowski, MD, medical director of pediatric inflammatory bowel diseases at Cleveland Clinic Children’s in Cleveland, Ohio, said in an interview.
“In terms of technological breakthroughs, this is like going from some of the more rigid endoscopies to high-definition and white-light endoscopy or the upgrade from paper charts to the electronic medical record (EMR), but instead of making your life more difficult, it will actually make it a lot easier,” said Dr. Kurowski, who has researched and lectured on AI applications in IBD.
Simply put, “AI is when algorithms use data to simulate human intelligence,” said Seth A. Gross, MD, clinical chief in the Division of Gastroenterology and Hepatology at NYU Langone Health and a professor at NYU Grossman School of Medicine, New York City, who has studied the use of AI for polyp detection.
IBD is ideally served by AI because to diagnose and manage the disease, gastroenterologists must gather, analyze, and weave together a particularly heterogeneous mix of information — from blood tests and imaging to patient-reported symptoms and family history — often stored in different places or formats. And to ensure patient participation in their care plans, gastroenterologists also need to help them understand this complex disease.
Because of their potential to aid gastroenterologists with these tasks, three core AI technologies — some of which already have commercial applications — are likely to become foundational in clinical practice in the coming years: Image analysis and processing, natural language processing (NLP), and generative AI, according to experts familiar with AI research in IBD.
Image Analysis and Processing
One of AI’s most promising applications for IBD care is in medical image and video processing and analysis. Emerging AI tools convert the essential elements of medical images into mathematical features, which they then use to train and refine themselves. The ultimate goal is to provide fast, accurate, and granular results without inter- and intraobserver variation and human potential for bias.
Today’s techniques don’t quantify IBD very well because they’re qualitative and subjective, Ryan Stidham, MD, associate professor of gastroenterology and computational medicine and bioinformatics at the University of Michigan, Ann Arbor, and a leading researcher in AI applications in IBD, said in an interview.
“Even standardized scoring systems used by the US Food and Drug Administration and the European Medicines Agency to assess disease severity and measure therapeutic response are still pretty crude systems — not because of the gastroenterologists interpreting them, who are smart — but because it’s a very difficult task to quantify these features on imaging,” he said.
Another appeal of the technology in IBD care is that it has capabilities, including complex pattern recognition, beyond those of physicians.
“What we can’t do is things such as tediously measure every single ulcer, count how many different disease features are seen throughout the entire colon, where they are and how they’re spatially correlated, or what are their color patterns,” Dr. Stidham said. “We don’t have the time, feasibility, or, frankly, the energy and cognitive attention span to be able to do that for one patient, let alone every patient.”
AI-based disease activity assessments have yielded promising results across multiple imaging systems. The technology has advanced rapidly in the last decade and is beginning to demonstrate the ability to replicate near perfectly the endoscopic interpretation of human experts.
In separate studies, AI models had high levels of agreement with experienced reviewers on Mayo endoscopic scores and ulcerative colitis endoscopic index of severity scores, and they reduced the review time of pan-enteric capsule endoscopy among patients with suspected Crohn’s disease from a range of 26-39 minutes to 3.2 minutes per patient.
A report from the PiCaSSO study showed that an AI-guided system could distinguish remission/inflammation using histologic assessments of ulcerative colitis biopsies with an accuracy rate close to that of human reviewers.
In Crohn’s disease, research indicates that cross-sectional enterography imaging could potentially be made more precise with AI, providing hope that radiologists will be freed from this time-consuming task.
“As of today, several commercial companies are producing tools that can take an endoscopic image or a full-motion video and more or less give you a standardized score that would be akin to what an expert would give you on review of a colonoscopy,” Dr. Stidham said.
This is not to say there isn’t room for improvement.
“There’s probably still a bit of work to do when looking for the difference between inflammation and adenoma,” said Dr. Kurowski. “But it’s coming sooner rather than later.”
NLP
NLP — a subset of applied machine learning that essentially teaches computers to read — enables automated systems to go through existing digital information, including text like clinical notes, and extract, interpret, and quantify it in a fraction of the time required by clinicians.
One area this type of AI can help in IBD care is by automating EMR chart reviews. Currently, clinicians often must conduct time-consuming reviews to gather and read all the information they need to manage the care of patients with the disease.
Evidence suggests that this task takes a considerable toll. In a 2023 report, gastroenterologists cited hassles with EMRs and too much time spent at work among the main contributors to burnout.
NLP used on entire EMR systems could be used to improve overall IBD care.
“We have 30-40 years of EMRs available at our fingertips. These reams of clinical data are just sitting out there and provide a longitudinal narrative of what’s happened to every patient and the changes in their treatment course,” Dr. Stidham said.
Results from several studies involving NLP are promising. Automated chart review models enhanced with NLP have been shown to be better at identifying patients with Crohn’s disease or ulcerative colitis and at detecting and inferring the activity status of extraintestinal manifestations of IBD than models using only medical codes.
Additional examples of NLP applications that could save physicians’ time and energy in everyday practice include automatically generating clinical notes, summarizing patient interactions, and flagging important information for follow-up.
For time-strapped, overburdened clinicians, NLP may even restore the core aspects of care that first attracted them to the profession, Dr. Kurowski noted.
“It might actually be the next best step to get physicians away from the computer and back to being face to face with the patient, which I think is one of the biggest complaints of everybody in the modern EMR world in that we live in,” he said.
Generative AI
Patient education likely will be reshaped by emerging AI applications that can generate digital materials in a conversational tone. These generative AI tools, including advanced chatbots, are powered by large-language models, a type of machine learning that is trained on vast amounts of text data to understand and generate natural language.
This technology will be familiar to anyone who has interacted with OpenAI’s ChatGPT, which after getting a “prompt” — a question or request — from a user provides a conversational-sounding reply.
“Chatbots have been around for a while, but what’s new is that they now can understand and generate language that’s far more realistic,” Dr. Stidham said. “Plus, they can be trained on clinical scenarios so that it can put individual patients into context when having that digital, AI-powered conversation.”
In IBD, chatbots are being used to educate patients, for example, by answering their questions before they undergo colonoscopy. In a recent analysis, the best performer of three chatbots answered 91.4% of common precolonoscopy questions accurately. Other research determined that chatbot responses to colonoscopy questions were comparable with those provided by gastroenterologists.
Dr. Stidham and colleagues have seen the technology’s potential firsthand at the University of Michigan, where they’ve successfully deployed commercial chatbots to interact with patients prior to colonoscopy.
“It’s a force multiplier, in that these chatbots are essentially allowing us to expand our staff without bringing in more humans,” he said.
Despite fears that AI will threaten healthcare jobs, that isn’t an issue in today’s environment where “we can’t hire enough help,” Dr. Stidham said.
However, this technology isn’t fully ready for large-scale implementation, he added.
“ChatGPT may be ready for general medicine, but it’s not taking care of my gastroenterology patients (yet),” Dr. Stidham and coauthors wrote in a recent article. Among their concerns was the inability of ChatGPT versions 3 and 4 to pass the American College of Gastroenterology’s self-assessment test.
Preparing for the Future of AI
One proactive step is engaging with professional societies and initiatives aimed at guiding AI implementation.
One such initiative is the American Society for Gastrointestinal Endoscopy’s AI Task Force, which is led by Dr. Gross.
“The AI Task Force, which has recently evolved into an AI institute, believes in responsible AI,” Dr. Gross said. “The group highlights the importance of transparency and partnership with all key stakeholders to ensure that AI development and integration deliver improved care to GI patients.”
Dr. Kurowski, for one, believes that as AI gets even better at quantifying patient data, it will usher in the long-sought era of personalized care.
“I think it actually moves us into the realm of talking about a cure for certain people with IBD, for certain subtypes of the disease,” he said. “AI is going to be much more your friend and less of your foe than anything else you’ve seen in the modern era of medicine.”
A version of this article first appeared on Medscape.com.
By now, it is widely accepted that artificial intelligence (AI) will reshape contemporary medicine. The question is simply when this hypothetical will become an everyday reality. For gastroenterologists involved in the management of inflammatory bowel disease (IBD), the waiting period may be ending.
AI “is the next step in clinical care,” Jacob Kurowski, MD, medical director of pediatric inflammatory bowel diseases at Cleveland Clinic Children’s in Cleveland, Ohio, said in an interview.
“In terms of technological breakthroughs, this is like going from some of the more rigid endoscopies to high-definition and white-light endoscopy or the upgrade from paper charts to the electronic medical record (EMR), but instead of making your life more difficult, it will actually make it a lot easier,” said Dr. Kurowski, who has researched and lectured on AI applications in IBD.
Simply put, “AI is when algorithms use data to simulate human intelligence,” said Seth A. Gross, MD, clinical chief in the Division of Gastroenterology and Hepatology at NYU Langone Health and a professor at NYU Grossman School of Medicine, New York City, who has studied the use of AI for polyp detection.
IBD is ideally served by AI because to diagnose and manage the disease, gastroenterologists must gather, analyze, and weave together a particularly heterogeneous mix of information — from blood tests and imaging to patient-reported symptoms and family history — often stored in different places or formats. And to ensure patient participation in their care plans, gastroenterologists also need to help them understand this complex disease.
Because of their potential to aid gastroenterologists with these tasks, three core AI technologies — some of which already have commercial applications — are likely to become foundational in clinical practice in the coming years: Image analysis and processing, natural language processing (NLP), and generative AI, according to experts familiar with AI research in IBD.
Image Analysis and Processing
One of AI’s most promising applications for IBD care is in medical image and video processing and analysis. Emerging AI tools convert the essential elements of medical images into mathematical features, which they then use to train and refine themselves. The ultimate goal is to provide fast, accurate, and granular results without inter- and intraobserver variation and human potential for bias.
Today’s techniques don’t quantify IBD very well because they’re qualitative and subjective, Ryan Stidham, MD, associate professor of gastroenterology and computational medicine and bioinformatics at the University of Michigan, Ann Arbor, and a leading researcher in AI applications in IBD, said in an interview.
“Even standardized scoring systems used by the US Food and Drug Administration and the European Medicines Agency to assess disease severity and measure therapeutic response are still pretty crude systems — not because of the gastroenterologists interpreting them, who are smart — but because it’s a very difficult task to quantify these features on imaging,” he said.
Another appeal of the technology in IBD care is that it has capabilities, including complex pattern recognition, beyond those of physicians.
“What we can’t do is things such as tediously measure every single ulcer, count how many different disease features are seen throughout the entire colon, where they are and how they’re spatially correlated, or what are their color patterns,” Dr. Stidham said. “We don’t have the time, feasibility, or, frankly, the energy and cognitive attention span to be able to do that for one patient, let alone every patient.”
AI-based disease activity assessments have yielded promising results across multiple imaging systems. The technology has advanced rapidly in the last decade and is beginning to demonstrate the ability to replicate near perfectly the endoscopic interpretation of human experts.
In separate studies, AI models had high levels of agreement with experienced reviewers on Mayo endoscopic scores and ulcerative colitis endoscopic index of severity scores, and they reduced the review time of pan-enteric capsule endoscopy among patients with suspected Crohn’s disease from a range of 26-39 minutes to 3.2 minutes per patient.
A report from the PiCaSSO study showed that an AI-guided system could distinguish remission/inflammation using histologic assessments of ulcerative colitis biopsies with an accuracy rate close to that of human reviewers.
In Crohn’s disease, research indicates that cross-sectional enterography imaging could potentially be made more precise with AI, providing hope that radiologists will be freed from this time-consuming task.
“As of today, several commercial companies are producing tools that can take an endoscopic image or a full-motion video and more or less give you a standardized score that would be akin to what an expert would give you on review of a colonoscopy,” Dr. Stidham said.
This is not to say there isn’t room for improvement.
“There’s probably still a bit of work to do when looking for the difference between inflammation and adenoma,” said Dr. Kurowski. “But it’s coming sooner rather than later.”
NLP
NLP — a subset of applied machine learning that essentially teaches computers to read — enables automated systems to go through existing digital information, including text like clinical notes, and extract, interpret, and quantify it in a fraction of the time required by clinicians.
One area this type of AI can help in IBD care is by automating EMR chart reviews. Currently, clinicians often must conduct time-consuming reviews to gather and read all the information they need to manage the care of patients with the disease.
Evidence suggests that this task takes a considerable toll. In a 2023 report, gastroenterologists cited hassles with EMRs and too much time spent at work among the main contributors to burnout.
NLP used on entire EMR systems could be used to improve overall IBD care.
“We have 30-40 years of EMRs available at our fingertips. These reams of clinical data are just sitting out there and provide a longitudinal narrative of what’s happened to every patient and the changes in their treatment course,” Dr. Stidham said.
Results from several studies involving NLP are promising. Automated chart review models enhanced with NLP have been shown to be better at identifying patients with Crohn’s disease or ulcerative colitis and at detecting and inferring the activity status of extraintestinal manifestations of IBD than models using only medical codes.
Additional examples of NLP applications that could save physicians’ time and energy in everyday practice include automatically generating clinical notes, summarizing patient interactions, and flagging important information for follow-up.
For time-strapped, overburdened clinicians, NLP may even restore the core aspects of care that first attracted them to the profession, Dr. Kurowski noted.
“It might actually be the next best step to get physicians away from the computer and back to being face to face with the patient, which I think is one of the biggest complaints of everybody in the modern EMR world in that we live in,” he said.
Generative AI
Patient education likely will be reshaped by emerging AI applications that can generate digital materials in a conversational tone. These generative AI tools, including advanced chatbots, are powered by large-language models, a type of machine learning that is trained on vast amounts of text data to understand and generate natural language.
This technology will be familiar to anyone who has interacted with OpenAI’s ChatGPT, which after getting a “prompt” — a question or request — from a user provides a conversational-sounding reply.
“Chatbots have been around for a while, but what’s new is that they now can understand and generate language that’s far more realistic,” Dr. Stidham said. “Plus, they can be trained on clinical scenarios so that it can put individual patients into context when having that digital, AI-powered conversation.”
In IBD, chatbots are being used to educate patients, for example, by answering their questions before they undergo colonoscopy. In a recent analysis, the best performer of three chatbots answered 91.4% of common precolonoscopy questions accurately. Other research determined that chatbot responses to colonoscopy questions were comparable with those provided by gastroenterologists.
Dr. Stidham and colleagues have seen the technology’s potential firsthand at the University of Michigan, where they’ve successfully deployed commercial chatbots to interact with patients prior to colonoscopy.
“It’s a force multiplier, in that these chatbots are essentially allowing us to expand our staff without bringing in more humans,” he said.
Despite fears that AI will threaten healthcare jobs, that isn’t an issue in today’s environment where “we can’t hire enough help,” Dr. Stidham said.
However, this technology isn’t fully ready for large-scale implementation, he added.
“ChatGPT may be ready for general medicine, but it’s not taking care of my gastroenterology patients (yet),” Dr. Stidham and coauthors wrote in a recent article. Among their concerns was the inability of ChatGPT versions 3 and 4 to pass the American College of Gastroenterology’s self-assessment test.
Preparing for the Future of AI
One proactive step is engaging with professional societies and initiatives aimed at guiding AI implementation.
One such initiative is the American Society for Gastrointestinal Endoscopy’s AI Task Force, which is led by Dr. Gross.
“The AI Task Force, which has recently evolved into an AI institute, believes in responsible AI,” Dr. Gross said. “The group highlights the importance of transparency and partnership with all key stakeholders to ensure that AI development and integration deliver improved care to GI patients.”
Dr. Kurowski, for one, believes that as AI gets even better at quantifying patient data, it will usher in the long-sought era of personalized care.
“I think it actually moves us into the realm of talking about a cure for certain people with IBD, for certain subtypes of the disease,” he said. “AI is going to be much more your friend and less of your foe than anything else you’ve seen in the modern era of medicine.”
A version of this article first appeared on Medscape.com.
By now, it is widely accepted that artificial intelligence (AI) will reshape contemporary medicine. The question is simply when this hypothetical will become an everyday reality. For gastroenterologists involved in the management of inflammatory bowel disease (IBD), the waiting period may be ending.
AI “is the next step in clinical care,” Jacob Kurowski, MD, medical director of pediatric inflammatory bowel diseases at Cleveland Clinic Children’s in Cleveland, Ohio, said in an interview.
“In terms of technological breakthroughs, this is like going from some of the more rigid endoscopies to high-definition and white-light endoscopy or the upgrade from paper charts to the electronic medical record (EMR), but instead of making your life more difficult, it will actually make it a lot easier,” said Dr. Kurowski, who has researched and lectured on AI applications in IBD.
Simply put, “AI is when algorithms use data to simulate human intelligence,” said Seth A. Gross, MD, clinical chief in the Division of Gastroenterology and Hepatology at NYU Langone Health and a professor at NYU Grossman School of Medicine, New York City, who has studied the use of AI for polyp detection.
IBD is ideally served by AI because to diagnose and manage the disease, gastroenterologists must gather, analyze, and weave together a particularly heterogeneous mix of information — from blood tests and imaging to patient-reported symptoms and family history — often stored in different places or formats. And to ensure patient participation in their care plans, gastroenterologists also need to help them understand this complex disease.
Because of their potential to aid gastroenterologists with these tasks, three core AI technologies — some of which already have commercial applications — are likely to become foundational in clinical practice in the coming years: Image analysis and processing, natural language processing (NLP), and generative AI, according to experts familiar with AI research in IBD.
Image Analysis and Processing
One of AI’s most promising applications for IBD care is in medical image and video processing and analysis. Emerging AI tools convert the essential elements of medical images into mathematical features, which they then use to train and refine themselves. The ultimate goal is to provide fast, accurate, and granular results without inter- and intraobserver variation and human potential for bias.
Today’s techniques don’t quantify IBD very well because they’re qualitative and subjective, Ryan Stidham, MD, associate professor of gastroenterology and computational medicine and bioinformatics at the University of Michigan, Ann Arbor, and a leading researcher in AI applications in IBD, said in an interview.
“Even standardized scoring systems used by the US Food and Drug Administration and the European Medicines Agency to assess disease severity and measure therapeutic response are still pretty crude systems — not because of the gastroenterologists interpreting them, who are smart — but because it’s a very difficult task to quantify these features on imaging,” he said.
Another appeal of the technology in IBD care is that it has capabilities, including complex pattern recognition, beyond those of physicians.
“What we can’t do is things such as tediously measure every single ulcer, count how many different disease features are seen throughout the entire colon, where they are and how they’re spatially correlated, or what are their color patterns,” Dr. Stidham said. “We don’t have the time, feasibility, or, frankly, the energy and cognitive attention span to be able to do that for one patient, let alone every patient.”
AI-based disease activity assessments have yielded promising results across multiple imaging systems. The technology has advanced rapidly in the last decade and is beginning to demonstrate the ability to replicate near perfectly the endoscopic interpretation of human experts.
In separate studies, AI models had high levels of agreement with experienced reviewers on Mayo endoscopic scores and ulcerative colitis endoscopic index of severity scores, and they reduced the review time of pan-enteric capsule endoscopy among patients with suspected Crohn’s disease from a range of 26-39 minutes to 3.2 minutes per patient.
A report from the PiCaSSO study showed that an AI-guided system could distinguish remission/inflammation using histologic assessments of ulcerative colitis biopsies with an accuracy rate close to that of human reviewers.
In Crohn’s disease, research indicates that cross-sectional enterography imaging could potentially be made more precise with AI, providing hope that radiologists will be freed from this time-consuming task.
“As of today, several commercial companies are producing tools that can take an endoscopic image or a full-motion video and more or less give you a standardized score that would be akin to what an expert would give you on review of a colonoscopy,” Dr. Stidham said.
This is not to say there isn’t room for improvement.
“There’s probably still a bit of work to do when looking for the difference between inflammation and adenoma,” said Dr. Kurowski. “But it’s coming sooner rather than later.”
NLP
NLP — a subset of applied machine learning that essentially teaches computers to read — enables automated systems to go through existing digital information, including text like clinical notes, and extract, interpret, and quantify it in a fraction of the time required by clinicians.
One area this type of AI can help in IBD care is by automating EMR chart reviews. Currently, clinicians often must conduct time-consuming reviews to gather and read all the information they need to manage the care of patients with the disease.
Evidence suggests that this task takes a considerable toll. In a 2023 report, gastroenterologists cited hassles with EMRs and too much time spent at work among the main contributors to burnout.
NLP used on entire EMR systems could be used to improve overall IBD care.
“We have 30-40 years of EMRs available at our fingertips. These reams of clinical data are just sitting out there and provide a longitudinal narrative of what’s happened to every patient and the changes in their treatment course,” Dr. Stidham said.
Results from several studies involving NLP are promising. Automated chart review models enhanced with NLP have been shown to be better at identifying patients with Crohn’s disease or ulcerative colitis and at detecting and inferring the activity status of extraintestinal manifestations of IBD than models using only medical codes.
Additional examples of NLP applications that could save physicians’ time and energy in everyday practice include automatically generating clinical notes, summarizing patient interactions, and flagging important information for follow-up.
For time-strapped, overburdened clinicians, NLP may even restore the core aspects of care that first attracted them to the profession, Dr. Kurowski noted.
“It might actually be the next best step to get physicians away from the computer and back to being face to face with the patient, which I think is one of the biggest complaints of everybody in the modern EMR world in that we live in,” he said.
Generative AI
Patient education likely will be reshaped by emerging AI applications that can generate digital materials in a conversational tone. These generative AI tools, including advanced chatbots, are powered by large-language models, a type of machine learning that is trained on vast amounts of text data to understand and generate natural language.
This technology will be familiar to anyone who has interacted with OpenAI’s ChatGPT, which after getting a “prompt” — a question or request — from a user provides a conversational-sounding reply.
“Chatbots have been around for a while, but what’s new is that they now can understand and generate language that’s far more realistic,” Dr. Stidham said. “Plus, they can be trained on clinical scenarios so that it can put individual patients into context when having that digital, AI-powered conversation.”
In IBD, chatbots are being used to educate patients, for example, by answering their questions before they undergo colonoscopy. In a recent analysis, the best performer of three chatbots answered 91.4% of common precolonoscopy questions accurately. Other research determined that chatbot responses to colonoscopy questions were comparable with those provided by gastroenterologists.
Dr. Stidham and colleagues have seen the technology’s potential firsthand at the University of Michigan, where they’ve successfully deployed commercial chatbots to interact with patients prior to colonoscopy.
“It’s a force multiplier, in that these chatbots are essentially allowing us to expand our staff without bringing in more humans,” he said.
Despite fears that AI will threaten healthcare jobs, that isn’t an issue in today’s environment where “we can’t hire enough help,” Dr. Stidham said.
However, this technology isn’t fully ready for large-scale implementation, he added.
“ChatGPT may be ready for general medicine, but it’s not taking care of my gastroenterology patients (yet),” Dr. Stidham and coauthors wrote in a recent article. Among their concerns was the inability of ChatGPT versions 3 and 4 to pass the American College of Gastroenterology’s self-assessment test.
Preparing for the Future of AI
One proactive step is engaging with professional societies and initiatives aimed at guiding AI implementation.
One such initiative is the American Society for Gastrointestinal Endoscopy’s AI Task Force, which is led by Dr. Gross.
“The AI Task Force, which has recently evolved into an AI institute, believes in responsible AI,” Dr. Gross said. “The group highlights the importance of transparency and partnership with all key stakeholders to ensure that AI development and integration deliver improved care to GI patients.”
Dr. Kurowski, for one, believes that as AI gets even better at quantifying patient data, it will usher in the long-sought era of personalized care.
“I think it actually moves us into the realm of talking about a cure for certain people with IBD, for certain subtypes of the disease,” he said. “AI is going to be much more your friend and less of your foe than anything else you’ve seen in the modern era of medicine.”
A version of this article first appeared on Medscape.com.