How Artificial Intelligence Will Transform Clinical Care, Research, and Trials for Inflammatory Bowel Disease.

Artificial intelligence (AI) refers to computer-based methodologies which use data to teach a computer to solve pre-defined tasks; these methods can be applied to identify patterns in large multi-modal data sources. AI applications in inflammatory bowel disease (IBD) includes predicting response to therapy, disease activity scoring of endoscopy, drug discovery, and identifying bowel damage in images. As a complex disease with entangled relationships between genomics, metabolomics, microbiome, and the environment, IBD stands to benefit greatly from methodologies that can handle this complexity. We describe current applications, critical challenges, and propose future directions of AI in IBD.

Machine learning methods in automated detection of CT enterography findings in Crohn's disease: A feasibility study.

PURPOSE: Qualitative findings in Crohn's disease (CD) can be challenging to reliably report and quantify. We evaluated machine learning methodologies to both standardize the detection of common qualitative findings of ileal CD and determine finding spatial localization on CT enterography (CTE). MATERIALS AND METHODS: Subjects with ileal CD and a CTE from a single center retrospective study between 2016 and 2021 were included. 165 CTEs were reviewed by two fellowship-trained abdominal radiologists for the presence and spatial distribution of five qualitative CD findings: mural enhancement, mural stratification, stenosis, wall thickening, and mesenteric fat stranding. A Random Forest (RF) ensemble model using automatically extracted specialist-directed bowel features and an unbiased convolutional neural network (CNN) were developed to predict the presence of qualitative findings. Model performance was assessed using area under the curve (AUC), sensitivity, specificity, accuracy, and kappa agreement statistics. RESULTS: In 165 subjects with 29,895 individual qualitative finding assessments, agreement between radiologists for localization was good to very good (κ = 0.66 to 0.73), except for mesenteric fat stranding (κ = 0.47). RF prediction models had excellent performance, with an overall AUC, sensitivity, specificity of 0.91, 0.81 and 0.85, respectively. RF model and radiologist agreement for localization of CD findings approximated agreement between radiologists (κ = 0.67 to 0.76). Unbiased CNN models without benefit of disease knowledge had very similar performance to RF models which used specialist-defined imaging features. CONCLUSION: Machine learning techniques for CTE image analysis can identify the presence, location, and distribution of qualitative CD findings with similar performance to experienced radiologists.

Challenges in IBD Research 2024: Novel Technologies.

Novel technology is one of the five focus areas of the Challenges in Inflammatory Bowel Disease (IBD) Research 2024 document. Building off the Challenges in IBD Research 2019 document, the Foundation aims to provide a comprehensive overview of current gaps in IBD research and deliver actionable approaches to address them with a focus on how these gaps can lead to advancements in interception, remission, and restoration for these diseases. The document is the result of a multidisciplinary collaboration from scientists, clinicians, patients, and funders and represents a valuable resource for patient-centric research prioritization. Specifically, the Novel Technologies section focuses on addressing key research gaps to enable interception and improve remission rates in IBD. This includes testing predictions of disease onset and progression, developing novel technologies tailored to specific phenotypes, and facilitating collaborative translation of science into diagnostics, devices, and therapeutics. Proposed priority actions outlined in the document include real-time measurement of biological changes preceding disease onset, more effective quantification of fibrosis, exploration of technologies for local treatment of fistulas, and the development of drug delivery platforms for precise, location-restricted therapies. Additionally, there is a strong emphasis on fostering collaboration between various stakeholders to accelerate progress in IBD research and treatment. Addressing these research gaps necessitates the exploration and implementation of bio-engineered novel technologies spanning a spectrum from materials to systems. By harnessing innovative ideas and technologies, there's a collective effort to enhance patient care and outcomes for individuals affected by IBD.

Technology drives medical progress, solving clinical challenges and enhancing patient care in inflammatory bowel disease (IBD). Collaborative efforts focus on addressing research gaps to improve interception, restoration, and remission rates, utilizing innovative technologies for better patient outcomes.

Legalization of Cannabis Does Not Reduce Opioid Prescribing in Patients With Inflammatory Bowel Disease: A Difference-in-Difference Analysis.

INTRODUCTION: Cannabis may provide inflammatory bowel disease (IBD) patients with an alternative to opioids for pain. METHODS: We conducted a difference-in-difference analysis using MarketScan. Changes over time in rates of opioid prescribing were compared in states with legalized cannabis to those without. RESULTS: We identified 6,240 patients with IBD in states with legalized cannabis and 79,272 patients with IBD in states without legalized cannabis. The rate of opioid prescribing decreased over time in both groups and were not significantly different (attributed differential = 0.34, confidence interval -13.02 to 13.70, P = 0.96). DISCUSSION: Opioid prescribing decreased from 2009 to 2016 among patients with IBD in both states with legalized and state without legalized cannabis, similar to what has been observed nationally across a variety of diseases. Cannabis legalization was not associated with a lower rate of opioid prescribing for patients with IBD.

MRI and Blood-based Biomarkers Are Associated With Surgery in Children and Adults With Ileal Crohn's Disease.

BACKGROUND: Despite advances in medical therapy, many children and adults with ileal Crohn's disease (CD) progress to fibrostenosis requiring surgery. We aimed to identify MRI and circulating biomarkers associated with the need for surgical management. METHODS: This prospective, multicenter study included pediatric and adult CD cases undergoing ileal resection and CD controls receiving medical therapy. Noncontrast research MRI examinations measured bowel wall 3-dimensional magnetization transfer ratio normalized to skeletal muscle (normalized 3D MTR), modified Look-Locker inversion recovery (MOLLI) T1 relaxation, intravoxel incoherent motion (IVIM) diffusion-weighted imaging metrics, and the simplified magnetic resonance index of activity (sMaRIA). Circulating biomarkers were measured on the same day as the research MRI and included CD64, extracellular matrix protein 1 (ECM1), and granulocyte-macrophage colony-stimulating factor (GM-CSF) autoantibodies (Ab). Associations between MRI and circulating biomarkers and need for ileal resection were tested using univariate and multivariable LASSO regression. RESULTS: Our study sample included 50 patients with CD undergoing ileal resection and 83 patients with CD receiving medical therapy; mean participant age was 23.9 ±â€…13.1 years. Disease duration and treatment exposures did not vary between the groups. Univariate biomarker associations with ileal resection included log GM-CSF Ab (odds ratio [OR], 2.87; P = .0009), normalized 3D MTR (OR, 1.05; P = .002), log MOLLI T1 (OR, 0.01; P = .02), log IVIM perfusion fraction (f; OR, 0.38; P = .04), and IVIM apparent diffusion coefficient (ADC; OR, 0.3; P = .001). The multivariable model for surgery based upon corrected Akaike information criterion included age (OR, 1.03; P = .29), BMI (OR, 0.91; P = .09), log GM-CSF Ab (OR, 3.37; P = .01), normalized 3D MTR (OR, 1.07; P = .007), sMaRIA (OR, 1.14; P = .61), luminal narrowing (OR, 10.19; P = .003), log C-reactive protein (normalized; OR, 2.75; P = .10), and hematocrit (OR, 0.90; P = .13). CONCLUSION: After accounting for clinical and MRI measures of severity, normalized 3D MTR and GM-CSF Ab are associated with the need for surgery in ileal CD.

Despite advances in medical therapy, many patients with ileal Crohn's disease progress to fibrostenosis requiring surgery. Our study has shown that GM-CSF autoantibodies and MRI biomarker sequences are associated with the need for ileal resection and may help guide management decisions.

Local Assessment and Small Bowel Crohn's Disease Severity Scoring using AI.

RATIONALE AND OBJECTIVES: We present a machine learning and computer vision approach for a localized, automated, and standardized scoring of Crohn's disease (CD) severity in the small bowel, overcoming the current limitations of manual measurements CT enterography (CTE) imaging and qualitative assessments, while also considering the complex anatomy and distribution of the disease. MATERIALS AND METHODS: Two radiologists introduced a severity score and evaluated disease severity at 7.5 mm intervals along the curved planar reconstruction of the distal and terminal ileum using 236 CTE scans. A hybrid model, combining deep-learning, 3-D CNN, and Random Forest model, was developed to classify disease severity at each mini-segment. Precision, sensitivity, weighted Cohen's score, and accuracy were evaluated on a 20% hold-out test set. RESULTS: The hybrid model achieved precision and sensitivity ranging from 42.4% to 84.1% for various severity categories (normal, mild, moderate, and severe) on the test set. The model's Cohen's score (κ = 0.83) and accuracy (70.7%) were comparable to the inter-observer agreement between experienced radiologists (κ = 0.87, accuracy = 76.3%). The model accurately predicted disease length, correlated with radiologist-reported disease length (r = 0.83), and accurately identified the portion of total ileum containing moderate-to-severe disease with an accuracy of 91.51%. CONCLUSION: The proposed automated hybrid model offers a standardized, reproducible, and quantitative local assessment of small bowel CD severity and demonstrates its value in CD severity assessment.

Artificial Intelligence for Quantifying Cumulative Small Bowel Disease Severity on CT-Enterography in Crohn's Disease.

INTRODUCTION: Assessing the cumulative degree of bowel injury in ileal Crohn's disease (CD) is difficult. We aimed to develop machine learning (ML) methodologies for automated estimation of cumulative ileal injury on computed tomography-enterography (CTE) to help predict future bowel surgery. METHODS: Adults with ileal CD using biologic therapy at a tertiary care center underwent ML analysis of CTE scans. Two fellowship-trained radiologists graded bowel injury severity at granular spatial increments along the ileum (1 cm), called mini-segments. ML segmentation methods were trained on radiologist grading with predicted severity and then spatially mapped to the ileum. Cumulative injury was calculated as the sum (S-CIDSS) and mean of severity grades along the ileum. Multivariate models of future small bowel resection were compared with cumulative ileum injury metrics and traditional bowel measures, adjusting for laboratory values, medications, and prior surgery at the time of CTE. RESULTS: In 229 CTE scans, 8,424 mini-segments underwent analysis. Agreement between ML and radiologists injury grading was strong (κ = 0.80, 95% confidence interval 0.79-0.81) and similar to inter-radiologist agreement (κ = 0.87, 95% confidence interval 0.85-0.88). S-CIDSS (46.6 vs 30.4, P = 0.0007) and mean cumulative injury grade scores (1.80 vs 1.42, P < 0.0001) were greater in CD biologic users that went to future surgery. Models using cumulative spatial metrics (area under the curve = 0.76) outperformed models using conventional bowel measures, laboratory values, and medical history (area under the curve = 0.62) for predicting future surgery in biologic users. DISCUSSION: Automated cumulative ileal injury scores show promise for improving prediction of outcomes in small bowel CD. Beyond replicating expert judgment, spatial enterography analysis can augment the personalization of bowel assessment in CD.

Artificial Intelligence and IBD: Where are We Now and Where Will We Be in the Future?

PURPOSE OF REVIEW: Artificial intelligence (AI) is quickly demonstrating the ability to address problems and challenges in the care of IBD. This review with commentary will highlight today's advancements in AI applications for IBD in image analysis, understanding text, and replicating clinical knowledge and experience. RECENT FINDINGS: Advancements in machine learning methods, availability of high-performance computing, and increasing digitization of medical data are providing opportunities for AI to assist in IBD care. Multiple groups have demonstrated the ability of AI to replicate expert endoscopic scoring in IBD, with expansion into automated capsule endoscopy, enterography, and histologic interpretations. Further, AI image analysis is being used to develop new endoscopic scoring with more granularity and detail than is possible using conventional methods. Advancements in natural language processing are proving to reduce laborious tasks required in the care of IBD, including documentation, information searches, and chart review. Finally, large language models and chatbots that can understand language and generate human-like replies are beginning to exhibit clinical intelligence that will revolutionize how we deliver IBD care. Today, AI is being deployed to replicate expert judgement in specific tasks where disagreement, subjectivity, and bias are common. However, the near future will herald contributions of AI doing what we cannot, including new detailed measures of IBD, enhanced analysis of images, and perhaps even fully automating care. As we speculate on future technologic capabilities that may improve how we care for IBD, this review will also consider how we will implement and fairly use AI in practice.

Effectiveness of Upadacitinib for Patients With Acute Severe Ulcerative Colitis: A Multicenter Experience.

INTRODUCTION: A significant proportion of patients with acute severe ulcerative colitis (ASUC) require colectomy. METHODS: Patients with ASUC treated with upadacitinib and intravenous corticosteroids at 5 hospitals are presented. The primary outcome was 90-day colectomy rate. Secondary outcomes included frequency of steroid-free clinical remission, adverse events, and all-cause readmissions. RESULTS: Of the 25 patients with ASUC treated with upadacitinib, 6 (24%) patients underwent colectomy, 15 (83%) of the 18 patients with available data and who did not undergo colectomy experienced steroid-free clinical remission (1 patient did not have complete data), 1 (4%) patient experienced a venous thromboembolic event, while 5 (20%) patients were readmitted. DISCUSSION: Upadacitinib along with intravenous corticosteroids may be an effective treatment for ASUC.

Using Computer Vision to Improve Endoscopic Disease Quantification in Therapeutic Clinical Trials of Ulcerative Colitis.

BACKGROUND & AIMS: Endoscopic assessment of ulcerative colitis (UC) typically reports only the maximum severity observed. Computer vision methods may better quantify mucosal injury detail, which varies among patients. METHODS: Endoscopic video from the UNIFI clinical trial (A Study to Evaluate the Safety and Efficacy of Ustekinumab Induction and Maintenance Therapy in Participants With Moderately to Severely Active Ulcerative Colitis) comparing ustekinumab and placebo for UC were processed in a computer vision analysis that spatially mapped Mayo Endoscopic Score (MES) to generate the Cumulative Disease Score (CDS). CDS was compared with the MES for differentiating ustekinumab vs placebo treatment response and agreement with symptomatic remission at week 44. Statistical power, effect, and estimated sample sizes for detecting endoscopic differences between treatments were calculated using both CDS and MES measures. Endoscopic video from a separate phase 2 clinical trial replication cohort was performed for validation of CDS performance. RESULTS: Among 748 induction and 348 maintenance patients, CDS was lower in ustekinumab vs placebo users at week 8 (141.9 vs 184.3; P < .0001) and week 44 (78.2 vs 151.5; P < .0001). CDS was correlated with the MES (P < .0001) and all clinical components of the partial Mayo score (P < .0001). Stratification by pretreatment CDS revealed ustekinumab was more effective than placebo (P < .0001) with increasing effect in severe vs mild disease (-85.0 vs -55.4; P < .0001). Compared with the MES, CDS was more sensitive to change, requiring 50% fewer participants to demonstrate endoscopic differences between ustekinumab and placebo (Hedges' g = 0.743 vs 0.460). CDS performance in the JAK-UC replication cohort was similar to UNIFI. CONCLUSIONS: As an automated and quantitative measure of global endoscopic disease severity, the CDS offers artificial intelligence enhancement of traditional MES capability to better evaluate UC in clinical trials and potentially practice.

Vedolizumab more likely to be discontinued than ustekinumab in anti-TNF-experienced patients with fistulizing Crohn's disease.

Background: Data on the performance of newer biologics in patients with fistulizing Crohn's disease (CD) are limited. Objective: Our study's objective was to evaluate the response to ustekinumab (UST) and vedolizumab (VDZ) in patients with fistulizing CD. Design: Retrospective cohort. Methods: We used natural language processing of electronic medical record data to identify a retrospective cohort of individuals with fistulizing CD at a single academic tertiary-care referral center and then performed a chart review. Individuals were eligible for inclusion if a fistula was present at the time of UST or VDZ initiation. Outcomes included medication discontinuation, surgical intervention, development of a new fistula, and fistula closure. Groups were compared with unadjusted analyses and competing risk analyses using multi-state survival models. Results: In all, 68 patients were included (48 UST and 20 VDZ). Most patients had one fistula (79%) and had prior anti-tumor necrosis factor-α treatment (98% in UST group, 80% in VDZ group, p = 0.01). VDZ was significantly more likely to be discontinued than UST (p < 0.0001), most frequently due to inadequate clinical response. Those on UST had a longer median time to surgery for CD than those on VDZ (p = 0.008). In those without surgical fistula repair, 79% on UST and 100% on VDZ still had an active fistula at 1 year (p = 0.30). Conclusion: In individuals with fistulizing CD, our data suggest that UST has better clinical utility than VDZ based on lower rates of discontinuation, though the sample size is small. These findings highlight the importance of further research on the treatment of perianal fistulizing Crohn's disease.

Efficacy and Safety of Dual Targeted Therapy for Partially or Non-responsive Inflammatory Bowel Disease: A Systematic Review of the Literature.

BACKGROUND: Dual targeted therapy (DTT) has emerged as an attractive therapeutic option for select patients with active inflammatory bowel disease (IBD) who are unable to achieve remission with biologic or small molecule monotherapy. We conducted a systematic review of specific DTT combinations in patients with IBD. METHODS: We conducted a systematic search of MEDLINE, EMBASE, Scopus, CINAHL Complete, Web of Science Core Collection, and Cochrane Library to identify articles related to the use of DTT for the treatment of Crohn Disease (CD) or ulcerative colitis (UC) published before February 2021. RESULTS: Twenty-nine studies were identified comprising 288 patients started on DTT for partially or non-responsive IBD. We identified 14 studies with 113 patients receiving anti-tumor necrosis factor (TNF) and anti-integrin therapies (i.e., vedolizumab and natalizumab), 12 studies with 55 patients receiving vedolizumab and ustekinumab, nine studies with 68 patients receiving vedolizumab and tofacitinib, five studies with 24 patients receiving anti-TNF therapy and tofacitinib, six studies with 18 patients receiving anti-TNF therapy and ustekinumab, and three studies with 13 patients receiving ustekinumab and tofacitinib. CONCLUSION: DTT is a promising approach to improve IBD treatment for patients with incomplete responses to targeted monotherapy. Larger prospective clinical studies are needed to confirm these findings as is additional predictive modeling to identify the patient subgroups most likely to require and benefit from this approach.

Characteristics of Patients with Crohn's Disease With or Without Perianal Fistulae in the CorEvitas Inflammatory Bowel Disease Registry.

BACKGROUND: There are limited real-world data characterizing perianal fistulae in patients with Crohn's disease (CD). AIM: To describe characteristics of patients with CD with and without perianal fistulae. METHODS: In this cross-sectional study, characteristics, treatment history, and health outcomes of patients with CD enrolled in the CorEvitas IBD Registry were described according to perianal fistula status (current/previous or none). RESULTS: Eight hundred and seventy-eight patients were included. Compared with patients with no perianal fistulae (n = 723), patients with current/previous perianal fistulae (n = 155) had longer disease duration since CD diagnosis (mean 16.5 vs 12.3 years; difference 4.3 years; 95% CI, 2.0, 6.6) and fewer had Harvey-Bradshaw Index scores indicative of remission (0-4, 56.8% vs 69.6%; difference - 12.9%; 95% CI, - 21.6, - 4.2). More patients with current/previous fistulae reported a history of IBD-related emergency room visits (67.7% vs 56.1%; difference 11.6%; 95% CI, 3.4, 19.8), hospitalizations (76.1% vs 58.4%; difference 17.7%; 95% CI, 10.1, 25.4), and surgeries (59.4% vs 27.7%; difference 31.7%; 95% CI, 23.3, 40.1), and a history of treatment with tumor necrosis factor inhibitors (81.3% vs 60.7%; difference 20.6%; 95% CI, 13.5, 27.7), immunosuppressants (51.6% vs 31.2%; difference 20.4%; 95% CI, 11.9, 29.0), and antibiotics (50.3% vs 23.7%; difference 26.6%; 95% CI, 18.2, 35.1) than patients without perianal fistulae. CONCLUSIONS: Patients with CD with current/previous perianal fistulae have more symptomatic experiences of disease, higher medication use, hospitalization rates, and emergency room visits than patients without perianal fistulae. Interventions to prevent/reduce risk of developing fistulae may help improve outcomes in CD.

Identifying the Presence, Activity, and Status of Extraintestinal Manifestations of Inflammatory Bowel Disease Using Natural Language Processing of Clinical Notes.

BACKGROUND: Extraintestinal manifestations (EIMs) occur commonly in inflammatory bowel disease (IBD), but population-level understanding of EIM behavior is difficult. We present a natural language processing (NLP) system designed to identify both the presence and status of EIMs using clinical notes from patients with IBD. METHODS: In a single-center retrospective study, clinical outpatient electronic documents were collected in patients with IBD. An NLP EIM detection pipeline was designed to determine general and specific symptomatic EIM activity status descriptions using Python 3.6. Accuracy, sensitivity, and specificity, and agreement using Cohen's kappa coefficient were used to compare NLP-inferred EIM status to human documentation labels. RESULTS: The 1240 individuals identified as having at least 1 EIM consisted of 54.4% arthritis, 17.2% ocular, and 17.0% psoriasiform EIMs. Agreement between reviewers on EIM status was very good across all EIMs (κ = 0.74; 95% confidence interval [CI], 0.70-0.78). The automated NLP pipeline determining general EIM activity status had an accuracy, sensitivity, specificity, and agreement of 94.1%, 0.92, 0.95, and κ = 0.76 (95% CI, 0.74-0.79), respectively. Comparatively, prediction of EIM status using administrative codes had a poor sensitivity, specificity, and agreement with human reviewers of 0.32, 0.83, and κ = 0.26 (95% CI, 0.20-0.32), respectively. CONCLUSIONS: NLP methods can both detect and infer the activity status of EIMs using the medical document an information source. Though source document variation and ambiguity present challenges, NLP offers exciting possibilities for population-based research and decision support in IBD.

Extraintestinal manifestations of inflammatory bowel disease impact on patient experience, but are poorly captured by electronic health records. Natural language processing systems are capable of not only detecting extraintestinal manifestations, but also inferring activity information by automated analysis of clinical notes.

Development of the Endoscopic Pouch Score for Assessment of Inflammatory Conditions of the Pouch.

Pouchoscopy provides a critical objective measure in the evaluation of patients with suspected inflammatory conditions of the pouch; however, there remain significant gaps in the reliability of the endoscopic scales used in the assessment of these conditions.1,2 Reliability and reproducibility in the assessment of patients after ileal pouch-anal anastomosis (IPAA) are critical, as evidenced by recent efforts to improve standardization in the evaluation of patients with pouch-related disorders.3.

Vaccination Against Measles, Mumps, Rubella and Incident Inflammatory Bowel Disease in a National Cohort of Privately Insured Children.

BACKGROUND: Infection is believed to be a potential trigger for inflammatory bowel disease (IBD). Whether vaccination against childhood infections including measles, mumps, and rubella may reduce risk of IBD is uncertain. METHODS: We conducted a retrospective cohort study using de-identified claims data from a national private payer (Optum Clinformatics Data Mart). Eligible infants were born between 2001 and 2018 and were continuously enrolled with medical and pharmacy coverage from birth for at least 2 years (n = 1 365 447). Measles, mumps, and rubella vaccination or MMR is administered beginning at 12 months of age. Cox proportional hazard regression models were used to compare time with incident disease in children by category of vaccination, after adjustment for sex, birth year, region of country, history of allergy to vaccines, and seizure history. RESULTS: The incidence of early pediatric IBD increased between 2001 and 2018. Ten percent (n = 141 230) of infants did not receive MMR, and 90% (n = 1 224 125) received at least 1 dose of MMR. There were 334 cases of IBD, 219 cases of Crohn's disease, and 164 cases of ulcerative colitis. Children who had received at least 1 dose of MMR had lower risk for IBD than children who did not (hazard ratio, 0.71; 95% confidence interval, 0.59-0.85). These associations did not change after further adjustment for childhood comorbid conditions, preterm birth, or older siblings affected with IBD. Similar associations were observed for MMR with Crohn's disease and ulcerative colitis, although these did not reach statistical significance. CONCLUSION: MMR is associated with decreased risk for childhood IBD.

Childhood vaccination against measles, mumps, and rubella was associated with decreased risk of childhood inflammatory bowel disease in a national administrative claims database.

A Systematic Temporal Extraction Pipeline for Medical Concepts in Clinical Notes.

With increased application of natural language processing (NLP) in medicine, many NLP models are being developed for uncovering relevant clinical features from electronic health records. Temporal information plays a key role in understanding the context, significance, and interpretation of medical concepts extracted from clinical notes. This is particularly true in situations where the behavior, value, or status of a medical concept changes over time. In this paper, we introduce a systematic framework, NLP annotation-Relaxation-Generation (NRG). NRG compiles incidents of medical concept changes from status annotations and timestamps of multiple clinical notes. We demonstrate the effectiveness of the NRG pipeline by applying it to two medical concepts related to patients with inflammatory bowel disease: extra-intestinal manifestations and medications. We show that the NRG pipeline offers not only insights into medical concept changes over time, but can help convey longitudinal changes in clinical features at both individual and population level.

Artificial Intelligence for Disease Assessment in Inflammatory Bowel Disease: How Will it Change Our Practice?

Artificial intelligence (AI) has arrived and it will directly impact how we assess, monitor, and manage inflammatory bowel disease (IBD). Advances in the machine learning methodologies that power AI have produced astounding results for replicating expert judgment and predicting clinical outcomes, particularly in the analysis of imaging. This review will cover general concepts for AI in IBD, with descriptions of common machine learning methods, including decision trees and neural networks. Applications of AI in IBD will cover recent achievements in endoscopic image interpretation and scoring, new capabilities for cross-sectional image analysis, natural language processing for automated understanding of clinical text, and progress in AI-powered clinical decision support tools. In addition to detailing current evidence supporting the capabilities of AI for replicating expert clinical judgment, speculative commentary on how AI may advance concepts of disease activity assessment, care pathways, and pathophysiologic mechanisms of IBD will be addressed.