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Volumen 34, Número 1, 2023
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ABSTRACT
Articial intelligence (AI) has shown signicant promise as a prognostic tool
in various gastrointestinal tract pathologies, including colorectal cancer,
esophageal disorders, inammatory bowel disease (IBD), liver diseases,
and pancreatic disorders. AI algorithms analyze patient data to provide
insights into disease progression, treatment response, and prognosis. In
gastroenterology, AI has excelled in upper gastrointestinal endoscopy,
surpassing human performance in detecting esophageal and gastric
cancer. It offers benets like cancer screening and automated report
generation. In IBD, AI predicts disease are-ups, assesses therapy response,
and tailors treatments for individual patients. For liver diseases, AI identies
subtle radiographic features, monitors brosis progression, and evaluates
treatment response patterns. In pancreatic disorders, AI models predict
outcomes, optimize surgeries, and enable targeted therapies. Integrating
AI as a prognostic tool brings advantages like processing vast data
quickly, enhancing diagnostic accuracy, and aiding in risk stratication
and treatment planning. Challenges include ethical considerations and
the need for validation through larger studies. Overall, AI has the potential
to revolutionize managing gastrointestinal tract pathologies, improving
patient outcomes and quality of life.
RESUMO
A inteligência articial (IA) tem mostrado um grande potencial como
ferramenta prognóstica em diversas patologias do trato gastrointestinal,
incluindo câncer colorretal, distúrbios esofágicos, doenças inamatórias
intestinais (DII), doenças hepáticas e distúrbios pancreáticos. Algoritmos de
IA analisam dados do paciente para fornecer insights sobre a progressão
da doença, resposta ao tratamento e prognóstico. Na gastroenterologia,
a IA tem se destacado na endoscopia do trato gastrointestinal superior,
superando o desempenho humano na detecção de câncer esofágico
e gástrico. Ela oferece benefícios como triagem de câncer e geração
automatizada de relatórios. Nas DII, a IA prevê crises da doença, avalia a
resposta à terapia e personaliza tratamentos para pacientes individuais.
Para doenças hepáticas, a IA identica características radiográcas
sutis, monitora a progressão da brose e avalia padrões de resposta
ao tratamento. Em distúrbios pancreáticos, modelos de IA preveem
resultados, otimizam cirurgias e permitem terapias direcionadas. A
integração da IA como ferramenta prognóstica traz vantagens como
processamento rápido de grandes volumes de dados, aumento da
precisão diagnóstica e auxílio na estraticação de riscos e planejamento
de tratamento. Desaos incluem considerações éticas e a necessidade
de validação por meio de estudos maiores. No geral, a IA tem o potencial
de revolucionar o manejo de patologias do trato gastrointestinal,
melhorando os resultados e a qualidade de vida dos pacientes.
EDITORIAL
ARTIFICIAL INTELLIGENCE AS A PROGNOSTIC TOOL FOR
GASTROINTESTINAL TRACT PATHOLOGIES.
INTELIGÊNCIA ARTIFICIAL COMO FERRAMENTA PROGNÓSTICA PARA
PATOLOGIAS DO TRATO GASTROINTESTINAL
1 University of South Wales, Program of Acute Medici-
ne and Gastroenterology, United Kingdom.
2 Postgraduate Program in Medicine: Pathology, School
of Medicine, Universidade Federal de Ciências da
Saúde de Porto Alegre, Brasil
ORCID ID:
Soldera Jonathan:
orcid.org/0000-0001-6055-4783
*Corresponding author: Soldera Jonathan
E-mail: jonathansoldera@gmail.com
Soldera Jonathan 1,2*
Este artículo está bajo una
licencia de Creative Com-
mons de tipo Reconocimien-
to – No comercial – Sin obras
derivadas 4.0 International.
Citation:
Soldera J. Artificial Intelligence as a
Prognostic Tool for Gastrointestinal
Tract Pathologies. Med Vozandes. 2023;
34 (1): 9 - 14
Received: 07 – Apr – 2023
Accepted: 04 – Jun – 2023
Publish: 01 – Jul – 2023
Article history
Palavras-chave: Inteligência articial; Doenças
gastrointestinais; Prognóstico; Endoscopia; Tratamento
personalizado.
Keywords: Articial intelligence; Gastrointesti-
nal diseases; Prognosis; Endoscopy; Personalized
treatment.
DOI: 10.48018/rmv.v34.i1.e
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for gastrointestinal tract pathologies, such as
inammatory bowel disease, colorectal cancer, and
gastroesophageal reux disease. By analyzing extensive
patient data, including medical records, imaging
studies, and laboratory results, AI algorithms can provide
valuable insights into disease progression, treatment
response, and overall prognosis [4].
The application of AI in upper gastrointestinal
endoscopy has gained signicant attention in
recent years. AI systems have been developed to
assist in the assessment of (pre-)cancerous lesions
of the gastrointestinal tract. These systems have
shown promising results in several areas, including
the detection, characterization, and delineation of
esophageal and gastric cancer, prediction of tumor
invasion, and detection of Helicobacter pylori [5]. AI
algorithms have demonstrated high accuracy rates of
up to 99% in detecting supercial and advanced upper
GI cancers, surpassing the performance of trainee
and experienced endoscopists [5]. Additionally, AI has
outperformed mid-level and trainee endoscopists,
although not expert endoscopists, in the detection
of esophageal lesions and atrophic gastritis [5]. The
integration of AI in upper gastrointestinal endoscopy holds
the potential to improve early diagnosis of esophageal
and gastric cancer and enhance the identication of
patients suitable for endoscopic resection [5].
The eld of gastrointestinal endoscopy has witnessed
signicant advancements in the application of AI. AI-
assisted endoscopy has the potential to revolutionize
the practice of gastroenterologists, offering benets
such as cancer screening and automated report
generation [6]. Over the years, AI has made remarkable
progress in endoscopy, with the development of
sophisticated models and potential applications that
encompass various aspects of modern endoscopic
practice [6]. These AI models have the ability to enhance
diagnostic accuracy, risk stratication, and pathologic
identication, paving the way for improved patient
care in gastroenterology [6]. However, it is important to
acknowledge the limitations of AI and understand its
role as a tool to enhance human decision-making rather
than replace healthcare professionals [6].
Colorectal Cancer
Colorectal cancer (CRC) is a signicant contributor to
morbidity and mortality on a global scale. The application
of AI algorithms has demonstrated remarkable success
in analyzing medical images, including colonoscopy
and histopathological slides. These AI systems assist
in the early detection of precancerous lesions and
accurate staging of tumors, enabling clinicians to make
informed decisions regarding treatment strategies,
personalized therapies, and prognostic predictions.
The advancements in AI technology, such as machine
learning and deep learning, have revolutionized
the eld of medicine, offering promising prospects
for various applications, including medical image
INTRODUCTION
The advent of Articial Intelligence (AI) has brought
about remarkable transformations across various
industries, including the eld of medicine. AI has the
potential to revolutionize healthcare in gastroenterology
by serving as a prognostic tool for gastrointestinal tract
pathologies. The history of AI in medicine dates back to
1950, with early models facing limitations that hindered
their widespread acceptance and application in
medicine [1]. However, advancements in deep learning
algorithms in the early 2000s overcame many of these
limitations, enabling AI systems to analyze complex
algorithms and self-learn [1]. This marked a new era in
medicine where AI can be applied to clinical practice,
improving diagnostic accuracy, workow efciency,
and risk assessment models [1].
The evolution of AI in medicine has been signicant in
recent years, with major applications in gastroenterology
and endoscopy [1]. AI-powered medical technologies
are rapidly evolving and becoming applicable solutions
for clinical practice, leveraging deep learning algorithms
to handle large amounts of data from wearables,
smartphones, and other mobile monitoring sensors [2].
The application of AI in specic clinical settings, such
as the detection of atrial brillation, epilepsy seizures,
and hypoglycemia, as well as the diagnosis of diseases
through histopathological examination or medical
imaging, has shown promising results [2]. However, the
implementation of augmented medicine and the
integration of AI in clinical practice face challenges,
including resistance from physicians and the need for
validation through traditional clinical trials [2]. Despite
these challenges, the benets of AI applications in
clinical practice are becoming increasingly apparent,
leading to discussions about its future opportunities
and risks on physicians, healthcare institutions, medical
education, and bioethics [2].
As laboratory medicine continues to evolve from a mainly
manual profession to a highly automated discipline,
generating vast amounts of diagnostic data, AI algorithms
are poised to play a crucial role in structuring and making
sense of this data, merging the worlds of the laboratory
and clinics [3]. By providing valuable suggestions in
diagnosis, prognosis, and therapeutic options, AI can
enhance the efciency and accuracy of medical
decision-making, freeing physicians’ time to focus more
on patient care [3]. However, the implementation of AI
algorithms in healthcare also poses challenges and
requires careful consideration of the ethical implications
and potential impact on healthcare workers [3].
This editorial delves into the role of AI in prognostication
for these conditions, highlighting its benets, challenges,
and future prospects.
AI in Gastrointestinal Tract Pathologies
In the domain of gastroenterology, AI algorithms
have shown great potential as prognostic tools
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EDITORIAL
recognition, biotechnology, auxiliary diagnosis, drug research
and development, and nutrition [7-9].
CRC is a common gastrointestinal malignancy that poses a
serious threat to human health, with high mortality rates. Many
CRC cases arise from the malignant transformation of colorectal
polyps. Therefore, early diagnosis and treatment play a crucial
role in determining the prognosis of CRC. Diagnostic methods
for CRC include imaging diagnosis, endoscopy, and pathology
diagnosis, while treatment options encompass endoscopic
treatment, surgical treatment, and drug treatment [7].
AI technology, although currently in its nascent stage,
predominantly focuses on image recognition and auxiliary
analysis, lacking in-depth communication capabilities with
patients. Nonetheless, its potential to revolutionize CRC
diagnosis, treatment, and prognosis is signicant. By leveraging
AI, clinicians can identify high-risk patients, devise precise and
personalized treatment plans, and predict prognoses with
greater accuracy [8].
The integration of AI technology in the diagnosis, treatment,
and prognosis of CRC holds immense promise for improving
patient outcomes. Further research and development in this
eld are needed to harness the full potential of AI in CRC
management [7-9]. One practical application of AI in CRC
prognostication is the development of predictive models
that utilize machine learning algorithms to analyze clinical
and molecular data. These models can help clinicians assess
the likelihood of disease progression, recurrence, and patient
survival based on individual patient characteristics and tumor
features. By integrating diverse data sources and considering
multiple variables, AI-driven prognostic models have the
potential to enhance treatment decision-making and improve
patient outcomes in CRC. [8]
Esophageal Disorders
Esophageal disorders encompass a wide range of conditions,
including gastroesophageal reux disease (GERD) and
esophageal cancer. The integration of articial intelligence
(AI) models has shown signicant potential in predicting
disease progression, identifying high-risk individuals, and
guiding therapeutic interventions for patients with esophageal
disorders. These AI models analyze clinical data, such as patient
history, endoscopic ndings, and imaging studies, to optimize
treatment plans and improve long-term outcomes [10-13].
AI has shown promise in the prognostication of esophageal
disorders by utilizing advanced predictive modeling techniques.
By analyzing a combination of clinical data, including patient
demographics, medical history, and diagnostic test results, AI
algorithms can generate personalized prognostic assessments.
These assessments provide valuable insights into disease
progression, treatment response, and long-term outcomes,
enabling clinicians to make informed decisions and optimize
patient care strategies [10-13].
Several studies have demonstrated the effectiveness of AI
techniques, particularly deep learning and convolutional
neural networks, in analyzing endoscopic images and videos
for the early detection and characterization
of esophageal neoplasia [10,13]. Furthermore,
AI-based approaches have the potential to
facilitate pathological diagnosis, gene diagnosis,
and risk stratication models, although further
clinical research is needed for validation [12,13].
The use of AI-powered decision support systems
empowers clinicians to enhance their treatment
strategies and achieve better results for patients
with esophageal disorders [11].
Inammatory Bowel Disease
Inammatory Bowel Disease (IBD), encompassing
Crohn’s disease and ulcerative colitis, is
characterized by chronic inammation of the
gastrointestinal tract. The management of IBD
can be challenging due to its multifactorial
nature, involving factors such as host genetics, the
immune system, environmental inuences, and
the gut microbiome. To address these challenges,
AI-based algorithms have been developed
to integrate multiple data sources, including
patient-reported outcomes, genetic proles, and
biomarkers. These algorithms have shown promise
in predicting disease are-ups, assessing therapy
response, and tailoring treatment strategies for
individual patients [14,15].
The application of AI in IBD research has been
facilitated by technological advancements
such as next-generation sequencing, high-
throughput omics data generation, and
molecular networks [15]. Machine learning and
systems biology, which are subsets of AI, enable
the efcient integration and interpretation of
large datasets, leading to the discovery of
clinically relevant knowledge. For example,
machine learning approaches can facilitate
patient stratication, prediction of disease
progression, and therapy responses, ultimately
allowing for personalized treatment options that
improve patient outcomes and reduce costs [15,16].
Studies have demonstrated the potential of AI
in various aspects of IBD care. For instance, AI
has been applied in the diagnosis, follow-up,
treatment selection, and prognosis of IBD. It has
also been utilized in the analysis of histopathology,
endoscopy, and imaging, highlighting its role
in improving disease assessment [17,18]. AI-driven
predictive models have shown promise in
identifying features and patterns across diverse
datasets, leading to insights that can enhance
disease management. These models have the
potential to transform clinical practice by providing
personalized metrics for disease outcomes [19].
Despite the potential benets, there are
challenges associated with the implementation
of AI in clinical IBD research. Technical barriers,
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targeted therapies[25-28]. For instance, AI can be utilized
in endoscopic ultrasound (EUS) for pancreatic disorders,
where it aids in computer-aided diagnosis (CAD) by
extracting and selecting features from imaging data
and utilizing deep learning-based algorithms[25]. By
leveraging AI’s predictive capabilities, clinicians can
intervene at an earlier stage, potentially improving
survival rates and patient outcomes[25-28]. Additionally, AI
can contribute to early detection of pancreatic cancer
through the use of medical images, pathological
examination, biomarkers, and other aspects, leading to
the early screening of high-risk groups and lesions[28]. It
can also predict prognosis, recurrence risk, metastasis,
and therapy response, inuencing patient outcomes[28].
Furthermore, AI nds applications in pancreatic cancer
health records, estimating medical imaging parameters,
and developing computer-aided diagnosis systems[28].
The advancement of AI applications in pancreatic
disorders requires collaboration among clinicians,
researchers, and engineers, as well as continuous efforts
to overcome limitations and harness the power of
computing in the ght against pancreatic cancer[27,28].
By incorporating AI into the management of pancreatic
disorders, healthcare professionals can potentially
improve the detection, treatment, and overall prognosis
of patients.
Benets of AI in Prognostication
The integration of AI as a prognostic tool for
gastrointestinal tract pathologies provides several
signicant advantages. AI enables the processing of
vast amounts of data in a shorter timeframe, leading
to timely and accurate prognostic information for
clinicians. AI algorithms have the capability to detect
subtle patterns and associations that may go unnoticed
by humans, thereby enhancing diagnostic accuracy
and prognostic precision. This can aid in risk stratication,
treatment planning, and optimizing patient outcomes [29].
Recent advancements in computing technology and
the application of AI in various elds, including medical
practice, have increased the potential for improved
outcomes [30]. AI algorithms can process complex
mathematical data, allowing for the consideration
of multiple parameters and sophisticated formulas
to determine conclusions that would be impractical
or impossible for humans alone [30]. This individualized
approach could lead to more tailored treatments for
each patient. However, it is important to note that most
studies conducted so far are retrospective and further
evaluation through large-scale prospective studies is
needed [30]. In addition to medical challenges, the
implementation of AI on a large scale raises ethical and
nancial considerations [30].
AI, particularly deep learning, has the potential to
enhance GI endoscopy in various areas, including
lesion detection, classication, quality metrics, and
documentation [31]. Computer vision in endoscopy
powered by advanced machine learning algorithms
can improve prediction and treatment outcomes for
bias within datasets, and the need for validation in
larger cohorts are among the obstacles that need to be
addressed. Ethical considerations related to the use of
AI in healthcare and data interpretation/validation are
also important aspects to be taken into account [18,20].
AI-based approaches have the potential to revolutionize
the management of IBD by integrating diverse data
sources and providing personalized insights for patients.
By leveraging machine learning and systems biology, AI
algorithms can enhance disease assessment, prediction,
and treatment selection, ultimately improving patient
outcomes and quality of life.
Liver Diseases
Liver diseases, including non-alcoholic fatty liver
disease (NAFLD) and viral hepatitis, have a signicant
impact on global healthcare systems. Detecting
and managing these diseases is crucial for patient
outcomes. AI algorithms trained on extensive datasets
can help identify subtle radiographic features, monitor
brosis progression, and assess treatment response
patterns. By leveraging machine learning models,
healthcare professionals can identify patients at risk,
intervene in a timely manner, and make accurate
prognostic assessments to guide clinical management
decisions, such as liver transplantation. For example,
machine learning models have been developed to
predict major adverse cardiovascular events after
orthotopic liver transplantation, providing valuable
insights for recipient selection and identifying
individuals at elevated risk for post-transplantation
MACE [21]. Another study utilized a machine learning
algorithm to predict 30-day and 365-day mortality
after liver transplantation, achieving high accuracy
in predicting patient outcomes [22]. Furthermore, these
tools have demonstrated effectiveness in challenging
scenarios, such as predicting post-LT major adverse
cardiac events [23]. Additionally, machine learning
algorithms have been applied to predict rebleeding
and mortality for oesophageal variceal bleeding in
cirrhotic patients, outperforming other assessment
tools like CLIF-SOFA and MELD score [24]. Such tool has
been also prospectively validated [25]. Deep learning
techniques have also been employed to improve
the imaging diagnosis of hepatocellular carcinoma,
aiding in the detection and diagnosis of this type of
liver cancer [26]. This demonstrates the potential of AI
and machine learning in liver disease management and
highlight the value of integrating these technologies into
clinical practice.
Pancreatic Disorders
Pancreatic disorders, such as pancreatitis and
pancreatic cancer, often manifest with late-stage
symptoms and poor prognoses. The integration of AI
models can signicantly impact the eld of pancreatic
disease by analyzing clinical records, genetic
information, and medical imaging to predict disease
outcomes, optimize surgical interventions, and enable
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EDITORIAL
patients with GI disorders and cancer [31]. The availability of
large libraries of endoscopic images, such as “EndoNet,” can
facilitate the development and application of AI systems [31]. The
regulatory environment for AI implementation is evolving, with
colon polyp detection highlighted as a potential clinical trial
endpoint [31]. Ongoing collaboration among gastroenterologists,
industry experts, and regulatory agencies is crucial to ensure
rapid progress and meaningful clinical impact [31].
AI and machine learning also have potential applications in
the eld of liver disease, particularly in acute on chronic liver
failure (ACLF) [32]. AI methods, including predictive, prognostic,
probabilistic, and simulation modeling, can minimize cognitive
load and impact short-term and long-term patient outcomes [32].
However, ethical considerations and a lack of proven benets
temper the enthusiasm for AI implementation [32]. AI models
can contribute to the understanding of morbidity and mortality
mechanisms in ACLF [32]. The impact of AI on patient-centered
outcomes and other aspects of patient care is still uncertain [32].
In the eld of pancreatic cystic lesions (PCLs), radiomics
combined with machine learning and AI methods has the
potential to differentiate between benign and malignant
lesions, leading to improved clinical decision-making and
resource utilization [33]. Radiomics involves quantitative image
analysis to extract features and develop imaging biomarkers for
predicting high-risk PCLs [33]. However, further studies are needed
to validate the clinical application of radiomics [33].
AI and big data analysis have signicantly inuenced clinical
oncology and research [34]. Next-generation sequencing (NGS)
platforms, coupled with AI and machine learning, enable the
identication of novel biomarkers, therapeutic targets, and
accurate prognosis in cancer [34]. However, challenges and
limitations, such as data analysis and validation, remain [34].
While AI and ML promise transformative changes in healthcare,
there are provisos that need to be addressed. Reliability of
input data, interpretation of output data, data privacy, liability
issues, decreased human interaction, patient satisfaction,
affordability, and skepticism regarding cost-benet are
important considerations [34].
Challenges and Limitations
Despite the promising potential of AI in prognostication,
there are several challenges and limitations that need to be
addressed. The availability of high-quality data is crucial for
effectively training AI algorithms[35]. However, concerns related
to data privacy and ethical considerations surrounding the
use of patient information must be carefully addressed to
ensure patient condentiality and establish trust[35]. Improving
the interpretability and transparency of AI algorithms is also
necessary to enable clinicians to understand and validate the
reasoning behind the generated prognostic predictions[35].
In a study on AI in primary health care, experts reported
that AI has the potential to improve managerial and clinical
decisions and processes[35]. Common data standards would
facilitate the realization of this potential[35]. However, there
was no consensus among the experts regarding whether AI
applications should learn and adapt to clinician
preferences or behavior, and they did not agree
on the extent of AI’s potential harm to patients[35].
Assessing the impact of AI-based applications
on continuity and coordination of care was
found to be more challenging[35].
In the eld of surgery, AI has shown promise but
also comes with certain limitations[36]. Machine
learning, articial neural networks, natural
language processing, and computer vision
are the main subelds of AI with applications in
surgery[36]. These applications include big data
analytics and clinical decision support systems[36].
Surgeons have the opportunity to integrate
AI into modern practice, partnering with data
scientists to capture data and provide clinical
context[36]. This collaboration has the potential to
revolutionize surgery and improve patient care.
In drug discovery, AI has gained traction and
is increasingly being used to accelerate the
process[36,37]. AI techniques, such as machine
learning and deep learning, have been
applied to various aspects of drug discovery,
including quantitative structure-activity/
property relationship modeling, de novo
molecular design, and chemical synthesis
prediction[37]. These AI-driven approaches have
the potential to address some of the challenges
in CNS drug discovery[38]. They can aid in target
identication, compound screening, hit/lead
generation and optimization, drug response
and synergy prediction, de novo drug design,
and drug repurposing[38]. However, there are still
limitations and challenges to be overcome, such
as the need for large amounts of annotated
data and issues with interpretability[37,38].
In the eld of nuclear medicine, AI has the potential
to impact various aspects of the profession[39].
It can be applied to different stages of the
imaging workow, including planning, scanning,
interpretation, and reporting[39]. However, current
AI techniques have limitations, such as the need
for interpretability and the requirement for large
amounts of annotated data[39].
Future Prospects
The future of AI in prognostication for
gastrointestinal tract pathologies holds immense
potential. As AI algorithms continue to evolve and
improve, their integration with electronic health
records and imaging systems can enhance real-
time decision-making and facilitate personalized
treatment approaches[35,39]. Collaboration
between clinicians, data scientists, and AI experts
is essential to develop robust and reliable AI
models specically tailored to gastrointestinal
diseases [35,39]. For example, in the eld of peptic
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Transparency in AI algorithms and effective
communication of their limitations to patients and
healthcare providers are essential[35]. Respecting patient
autonomy, privacy, and informed consent are crucial
during the development and implementation of AI-
based prognostic tools[35]. It is important to ensure that
AI complements clinical decision-making and does not
replace the human touch and personalized care that
patients deserve[35].
CONCLUSION
The integration of Articial Intelligence as a prognostic tool
for gastrointestinal tract pathologies holds tremendous
promise in revolutionizing disease management and
treatment strategies. AI has the potential to improve
diagnostic accuracy, enhance prognostic precision,
and ultimately optimize patient outcomes. However,
addressing challenges such as data quality, ethical
concerns, and algorithm transparency is vital for the
successful implementation of AI in routine clinical
practice. Through collaborative efforts and ongoing
research, AI can become an invaluable asset in the ght
against gastrointestinal diseases, equipping clinicians
with powerful tools for prognostication and patient-
centered care.
ulcers, AI has shown promising applications in pathogenic
factor identication, diagnosis, and management[35].
AI can aid in the identication of Helicobacter pylori
(Hp) infection, differential diagnosis, and management
of complications such as bleeding, obstruction,
perforation, and canceration[35]. These AI-based tools
have the potential to improve the management of
peptic ulcer patients[35]. Similarly, in colonoscopy, AI
has the potential to enhance the early identication,
resection, and treatment of precancerous adenoma
and early-stage cancer, leading to a reduction in
colorectal cancer prevalence and mortality[40]. AI
technologies, such as computer-aided detection and
diagnosis (CAD), can provide decision support during
colonoscopy, increasing the adenoma detection rate
and improving the effectiveness of screening[41]. The
future prospects of AI in colonoscopy include improving
diagnostic performance, reducing costs, optimizing
endoscopic schedules, and addressing limitations and
challenges[41]. AI also holds potential in the management
of inammatory bowel disease (IBD)[42]. AI applications in
IBD range from genomics to endoscopic applications,
enabling disease classication, stratication, self-
monitoring, and personalized management[42]. The
practical applications of AI in IBD are already being
used, and the future holds further potential for AI to
enhance patient care[42].
As AI becomes increasingly embedded in healthcare,
ethical considerations must remain at the forefront[35].
ARTIFICIAL INTELLIGENCE AS A PROGNOSTIC TOOL
FOR GASTROINTESTINAL TRACT PATHOLOGIES
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