Trending Topics in Gastroenterology | GastroAGI

GEMA-AI: Gender-Equity Model for Liver Transplant Waiting List Prioritization

GEMA-AI, or the Gender-Equity Model for Liver Transplant Waiting List Prioritization, is an innovative artificial intelligence-based model designed to improve fairness and accuracy in liver transplant allocation, with a specific focus on addressing gender disparities. Below is a detailed explanation of the model, its features, and its potential impact: ### 1. **Goal of GEMA-AI** - The primary goal of GEMA-AI is to reduce disparities in access to liver transplants, particularly for women, who have historically been disadvantaged under traditional prioritization models. - The model aims to improve equity and ensure that transplant candidates with the highest clinical urgency are prioritized appropriately. ### 2. **Key Features of GEMA-AI** - **Same Inputs as Existing Models**: GEMA-AI uses the same core laboratory inputs as widely used liver allocation scores, such as MELD-Na and MELD 3.0. These inputs include INR (International Normalized Ratio), bilirubin, sodium, and RFH-GFR (a renal function estimator). - **Explainable Artificial Intelligence (AI)**: Unlike traditional "black-box" AI models, GEMA-AI is built as an explainable artificial neural network. This ensures transparency in decision-making, which is critical for clinical applications. - **Nonlinear Modeling**: GEMA-AI captures nonlinear relationships between variables and mortality risk, such as the "U-shaped" risk associated with sodium levels. This allows it to more accurately assess patients with extreme lab values or severe clinical conditions. - **International Development and Validation**: The model was developed using UK liver transplant registry data and externally validated using Australian transplant cohorts, ensuring its robustness across different populations. - **Large-Scale Cohorts**: The combined dataset used for training and validation included 9,320 adult liver transplant candidates, providing a solid foundation for the model's development. ### 3. **Clinical Endpoint and Performance** - **Primary Clinical Endpoint**: The model focuses on predicting 90-day mortality or delisting due to clinical deterioration, which reflects the urgency of a patient’s need for a transplant. - **Improved Discrimination**: GEMA-AI demonstrated better discriminatory performance compared to existing models like MELD-Na, MELD 3.0, and GEMA-Na. This means it can more accurately predict which patients are at the highest risk. - **Stronger Benefit for Women**: The model’s advantage was particularly pronounced among women, addressing a known bias in previous allocation systems. This could lead to a significant reduction in gender disparities in transplant access. ### 4. **Handling Extreme Cases** - **Better Calibration for High-Risk Patients**: GEMA-AI showed superior calibration in patients with extreme lab values or severe clinical features, such as ascites (fluid buildup in the abdomen) and poor renal function. - **Extreme Lab Values**: Traditional linear models often mis-rank the sickest patients due to their inability to handle extreme values. GEMA-AI’s nonlinear approach avoids this issue, ensuring fair prioritization for these individuals. ### 5. **Impact on Patient Prioritization** - **Meaningful Reclassification**: GEMA-AI was able to re-rank patients by clinically meaningful score differences compared to existing models. This reprioritization often benefited patients with more severe clinical profiles. - **Potential Mortality Reduction**: Modeling suggests that GEMA-AI could prevent a significant number of waiting-list deaths, with the greatest impact observed among women. - **Prioritizing Sicker Profiles**: Patients with worse renal function and more severe symptoms were often prioritized higher by GEMA-AI, reflecting its ability to identify those in greatest need. ### 6. **Implementation Considerations** - **Reassessment Over Time**: The authors recommend reassessing patients at least every three months to ensure that prioritization remains accurate as clinical conditions change. - **Flexibility in Implementation**: GEMA-AI’s performance remained strong even when certain variables (e.g., ascites) were excluded, suggesting it can be adapted to different healthcare systems. - **Further Validation**: While the model has shown strong results in the UK and Australia, additional validation is needed before it can be adopted in other regions or healthcare systems. ### 7. **Broader Implications** - **Advancing Organ Allocation**: GEMA-AI demonstrates the potential of explainable machine learning to improve fairness and accuracy in organ allocation. By addressing biases and leveraging nonlinear modeling, it represents a significant advancement over traditional regression-based systems. - **Gender Equity in Healthcare**: The model underscores the importance of addressing gender disparities in medical decision-making and provides a framework for achieving more equitable outcomes in other areas of healthcare. ### 8. **Conclusion** GEMA-AI is a groundbreaking approach to liver transplant prioritization that combines explainable AI, robust modeling, and a focus on equity. By addressing longstanding gender disparities and improving the prioritization of critically ill patients, it has the potential to save lives and set a new standard for fairness in organ allocation systems worldwide.

AI-assisted versus conventional reading in pan-intestinal capsule endoscopy

The comparison between AI-assisted pan-intestinal capsule endoscopy (AI-PCE) and conventional reading of pan-intestinal capsule endoscopy (CR-PCE) highlights significant advancements in the diagnostic capabilities of AI technology in the context of suspected mid-lower gastrointestinal bleeding (MLGIB). Here’s a detailed breakdown: ### Overview of Pan-Intestinal Capsule Endoscopy (PCE) PCE is a minimally invasive diagnostic tool used to evaluate the gastrointestinal (GI) tract, particularly for detecting potentially haemorrhagic lesions (PHLs) in cases of suspected MLGIB. While effective, the traditional method of reading PCE (CR-PCE) is labor-intensive, time-consuming, and prone to variability and missed lesions due to human error. ### AI-Assisted PCE (AI-PCE) AI-PCE employs artificial intelligence, specifically a convolutional neural network (CNN), to assist in detecting lesions within the GI tract. This technology automates the lesion detection process, potentially improving diagnostic accuracy and efficiency. --- ### Key Findings from the Study #### 1. **Improved Sensitivity and Negative Predictive Value (NPV)** AI-PCE demonstrated significantly higher sensitivity and NPV compared to CR-PCE: - **Sensitivity**: AI-PCE achieved a sensitivity of 95% overall, compared to 67% for CR-PCE. This means AI-PCE was much more effective at detecting lesions. - **Negative Predictive Value (NPV)**: AI-PCE had an NPV of 92% versus 63% for CR-PCE, indicating a reduced likelihood of missing lesions. #### 2. **Performance by Intestinal Segment** - **Small Bowel**: - Sensitivity: 96% (AI-PCE) vs. 59% (CR-PCE). - NPV: 97% (AI-PCE) vs. 76% (CR-PCE). - **Colon**: - Sensitivity: 90% (AI-PCE) vs. 68% (CR-PCE). - NPV: 94% (AI-PCE) vs. 86% (CR-PCE). #### 3. **Lesion Detection** AI-PCE outperformed CR-PCE in detecting various lesion types: - **Vascular Lesions**: 51% detection rate with AI-PCE vs. 33% with CR-PCE. - **Ulcers/Erosions**: 16% detection rate with AI-PCE vs. 7% with CR-PCE. - **Protuberant Lesions**: Comparable detection rates (5% vs. 4%). - **Active Bleeding**: Comparable detection rates (7% vs. 7%). #### 4. **Comparison with Colonoscopy** AI-PCE also outperformed traditional colonoscopy: - Sensitivity: 90% (AI-PCE) vs. 32% (colonoscopy). - Positive Predictive Value (PPV): 100% (AI-PCE) vs. 65% (colonoscopy). - NPV: 94% (AI-PCE) vs. 65% (colonoscopy). --- ### Advantages of AI-PCE Over CR-PCE and Colonoscopy 1. **Higher Diagnostic Accuracy**: AI-PCE provides significantly better sensitivity and NPV, reducing the risk of missed lesions. 2. **Minimally Invasive**: Unlike colonoscopy, PCE is non-invasive, making it a more comfortable option for patients. 3. **Consistency and Reliability**: AI reduces reader dependency, variability, and the likelihood of human error in lesion detection. 4. **Time Efficiency**: Automating the review process with AI can save time for clinicians. --- ### Implications for Research, Practice, and Policy 1. **Redefining Diagnostic Standards**: AI-PCE may become the first-line diagnostic tool for suspected MLGIB, reducing the reliance on invasive procedures like colonoscopy. 2. **Improved Patient Outcomes**: By reducing false negatives, AI-PCE can decrease the need for repeated procedures and enhance early detection of critical lesions. 3. **Integration of AI into Clinical Workflows**: The study supports the incorporation of AI into capsule endoscopy to improve diagnostic accuracy and efficiency. 4. **Future Research**: Further validation studies and cost-effectiveness analyses are necessary to confirm the widespread applicability of AI-PCE. --- ### Conclusion AI-assisted PCE represents a significant advancement over conventional reading methods and even colonoscopy in the diagnosis of suspected MLGIB. With its superior sensitivity, reliability, and minimally invasive nature, AI-PCE has the potential to revolutionize the diagnostic approach to gastrointestinal bleeding and set a new standard for clinical practice. However, further research is needed to validate these findings and address the cost implications of integrating AI into routine diagnostic workflows.

Machine learning in GI Endoscopy

Machine learning (ML) has revolutionized gastrointestinal (GI) endoscopy by improving diagnostic precision, reducing variability, and streamlining workflows. ML algorithms analyze endoscopic images or videos to detect patterns, identify lesions, and assist in diagnosing, characterizing, or localizing GI diseases such as polyps, cancers, inflammatory bowel disease, and ulcers. This integration enhances lesion detection rates, predicts disease progression, and standardizes examination quality, reducing operator-dependent variability. Key applications of ML in GI endoscopy include: 1. **Computer-Assisted Detection (CADe):** Identifying lesions such as polyps or bleeding during endoscopy. 2. **Computer-Assisted Diagnosis (CADx):** Characterizing and classifying lesions to aid in diagnosis. 3. **Therapeutic Assistance:** Supporting interventional decisions, such as predicting lymph node metastasis (LNM) in colorectal cancer (CRC) to reduce unnecessary surgeries. 4. **Technical Support:** Enhancing procedural performance, such as scope insertion guidance for more precise and comfortable colonoscopies. Despite these advancements, challenges remain. High-quality, diverse, and annotated training datasets are essential, but current datasets often lack diversity, contain biases, and underrepresent rare diseases. Manual annotation is time-consuming, and low-quality images or artefacts can hinder accuracy. Additionally, balancing model accuracy with speed, cost, and computational efficiency is crucial. Ethical and regulatory concerns, such as patient privacy, algorithmic bias, data security, and transparency, are significant barriers to adoption. Real-time deployment is also limited by technical infrastructure, false positives, and workflow disruptions. Future success will require standardized protocols, clinician training, interdisciplinary collaboration, and advancements in explainable AI to build trust and ensure safe, effective integration of ML into GI endoscopy.

Submucosal vessel detection during third-space endoscopy - Role of AI

The role of artificial intelligence (AI) in submucosal vessel detection during third-space endoscopy, such as endoscopic submucosal dissection (ESD) and peroral endoscopic myotomy (POEM), has been explored to enhance procedural safety and efficiency. These advanced endoscopic techniques involve navigating the submucosal layer, where unseen blood vessels pose a risk of accidental injury and significant bleeding. AI has shown promise in improving the detection of submucosal vessels during these procedures. By assisting endoscopists, AI enhances vessel identification accuracy and reduces the time required for detection, thereby facilitating smoother procedural workflows. While AI may occasionally generate false positives, these are brief and unlikely to disrupt the procedure. The findings suggest that AI can serve as a valuable tool for improving safety and reducing complications in complex therapeutic endoscopy. Although further clinical trials are necessary, this study highlights AI's potential to support endoscopists and improve outcomes in third-space endoscopic procedures.

CADe colonoscopy in colorectal cancer screening - ESGE Postional Statement

The ESGE (European Society of Gastrointestinal Endoscopy) Position Statement on computer-assisted detection (CADe) in colonoscopy for colorectal cancer (CRC) screening and post-polyp surveillance provides a cautious but favorable recommendation for its use. Here is a comprehensive summary of the statement and its implications: ### What is CADe in Colonoscopy? CADe, or computer-assisted detection, is an artificial intelligence (AI) tool designed to enhance the detection of polyps during colonoscopy procedures. Polyps are growths in the colon or rectum that can sometimes develop into colorectal cancer if left untreated. CADe systems analyze the video feed from the colonoscope in real-time, helping endoscopists identify polyps that might otherwise be missed, particularly small or flat polyps that are more challenging to detect. ### ESGE's Evaluation Process The ESGE Position Statement was developed through a structured evaluation process, which included: 1. **Systematic Reviews**: A thorough review of existing evidence on CADe's effectiveness and safety. 2. **Microsimulation Modeling**: Simulations to predict the potential impact of CADe on colorectal cancer incidence and mortality. 3. **Patient Values and Preferences**: Consideration of patient perspectives regarding the use of this technology in screening and surveillance. A panel of European experts assessed the evidence, weighing the potential benefits and harms of CADe use during colonoscopy. The final vote on December 18, 2024, showed a majority (68.4%, or 13 out of 19 members) in favor of recommending CADe, while 31.6% (6 members) voted against it, highlighting ongoing uncertainty in the field. ### ESGE's Recommendation The ESGE issued a **weak but favorable recommendation** for the use of CADe in colonoscopy for CRC screening and post-polyp surveillance. The key points of the recommendation are as follows: 1. **Potential Benefits**: - **Improved Polyp Detection**: CADe enhances the detection of small and flat polyps, which are often missed during traditional colonoscopy. This improvement could help prevent the progression of these polyps into colorectal cancer. - **Reduction in CRC Incidence and Mortality**: Evidence suggests that CADe may lead to a small but meaningful reduction in the rates of colorectal cancer and related deaths. 2. **Concerns and Limitations**: - **Limited Evidence**: The current body of research supporting CADe is still limited and carries significant uncertainty. - **Modest Absolute Benefit**: While CADe shows promise, the overall reduction in cancer cases and deaths is considered modest. - **Risk of Overdiagnosis**: CADe increases the detection of non-threatening polyps, which could lead to overdiagnosis, unnecessary follow-up colonoscopies, and increased patient burden. 3. **Patient Preferences**: - The panel concluded that most well-informed patients who have already decided to undergo colonoscopy for screening or surveillance would likely prefer CADe-assisted procedures, given the potential for improved detection. ### Overall Conclusion The ESGE cautiously supports the use of CADe during colonoscopy, recognizing its potential to improve polyp detection and reduce colorectal cancer incidence and mortality. However, the recommendation is classified as weak due to the limited strength of current evidence, the modest absolute benefits, and the potential downsides, such as overdiagnosis and increased surveillance burden. The ESGE emphasizes the importance of balancing these benefits with patient preferences and the risks of unnecessary procedures. Further high-quality research is needed to strengthen the evidence base and address the uncertainties surrounding CADe's role in colorectal cancer screening and surveillance.

AI for submucosal vessel detection during third-space endoscopy

Submucosal vessel detection is critically important in third-space endoscopy because this advanced procedure involves creating a pathway within the layers of the gastrointestinal wall to treat conditions like achalasia, tumors, or gastrointestinal leaks. During this process, endoscopists work near delicate blood vessels embedded in the submucosal layer. If these vessels are accidentally injured, it can lead to severe bleeding, complications, or even life-threatening situations. Accurate identification of submucosal vessels helps the endoscopist navigate safely, avoid vessel injury, and perform the procedure with greater precision. Artificial intelligence (AI) has the potential to significantly enhance vessel detection during third-space endoscopy. AI algorithms, trained on large datasets of endoscopic images, can automatically identify and outline submucosal vessels in real-time. This reduces the cognitive burden on endoscopists, allowing them to focus on the procedure while relying on AI to highlight high-risk areas. AI can improve safety by offering consistent vessel detection even in challenging conditions like poor visibility or anatomical variations. Additionally, AI could assist less experienced trainees by acting as a "second set of eyes," enhancing their ability to recognize vessels accurately. However, for AI to be effectively integrated into clinical practice, it must demonstrate high accuracy, robustness, and reliability through rigorous testing in real-world scenarios.

Cancer Recurrence in Patients With CRC - Role of AI

The role of AI in detecting and analyzing cancer recurrence in patients with colorectal cancer (CRC) has been transformative, particularly with advancements like the DFCI-imaging-student model. Below is a detailed explanation of how AI contributes to this area: ### 1. **Automated Recurrence Detection Using NLP** - The DFCI-imaging-student model utilizes natural language processing (NLP) to extract cancer recurrence information from unstructured radiology reports. This eliminates the need for manual record reviews, which are often time-intensive and prone to human error. - By processing large volumes of radiology reports efficiently, the AI model ensures that recurrence data is captured with high precision and reliability. ### 2. **Study Design for Validation** - The model was applied to a cohort of 200 colorectal cancer patients diagnosed with stage III disease, alongside 200 breast cancer patients, as part of a longitudinal study at Kaiser Permanente Northern California. - Patients were followed for up to 19 years (2005–2024), providing a robust dataset for validating the AI model's performance in detecting recurrence. ### 3. **Model Performance in Colorectal Cancer** - The AI model demonstrated high sensitivity (94.3%) and specificity (86.9%) in detecting recurrence for colorectal cancer patients. - Sensitivity refers to the model's ability to correctly identify patients with recurrence, while specificity indicates its accuracy in identifying those without recurrence. These metrics confirm the reliability of the model in clinical settings. ### 4. **Precision in Estimating Time-to-Recurrence** - Among correctly identified recurrence cases in colorectal cancer patients, the median error in estimating the timing of recurrence was minimal—just 0.48 months. - This near-exact alignment with manual reviews highlights the model's ability to provide precise and actionable information regarding when recurrence occurs. ### 5. **Clinical and Research Impact** - The AI model enables efficient, large-scale analysis of recurrence data, which is critical for improving cancer surveillance and patient outcomes. - Real-time monitoring of recurrence trends allows clinicians to tailor treatment plans and follow-up strategies more effectively. - The model also supports multicenter oncology research, fostering collaboration and enabling the analysis of recurrence patterns across diverse patient populations. ### 6. **Future Implications** - AI-driven recurrence detection models like this one pave the way for advancements in personalized medicine. By understanding recurrence patterns, researchers can develop targeted therapies and interventions to prevent or manage recurrence more effectively. - Additionally, such models can be integrated into electronic health record (EHR) systems to provide automated alerts for clinicians, enhancing early detection and improving patient care. In summary, AI plays a crucial role in colorectal cancer recurrence detection by automating data extraction, providing accurate and timely insights, and supporting clinical decision-making and research efforts. The DFCI-imaging-student model exemplifies how AI can revolutionize oncology care, particularly in improving outcomes for CRC patients.

AI ML-based nomogram for mortality risk stratification in cirrhotic patients

The AI/ML-based nomogram developed in this study serves as a predictive tool for estimating in-hospital mortality risk among cirrhotic patients with sepsis. This retrospective single-center study analyzed data from 264 patients admitted between January 2018 and July 2025, dividing them into Survivor and Non-survivor groups, with 28.4% succumbing to the condition during hospitalization. To ensure robust model performance, the dataset was split into a training set (70%) and validation set (30%). Key predictors of mortality identified included alcoholic cirrhosis, Child-Pugh score, mechanical ventilation, total bilirubin (TBiL), and heart rate (HR). These factors were selected using LASSO regression and further optimized with multivariate logistic regression. A nomogram was constructed to visually quantify individual mortality risks based on the weighted contribution of these predictors. The model demonstrated excellent discrimination with an AUC of 0.81 in the training set and 0.83 in the validation set, while calibration plots showed strong agreement between predicted and observed outcomes. Alcoholic cirrhosis was a significant risk factor, with a 3.7 times higher mortality rate compared to non-alcoholic cases. Higher Child-Pugh scores indicated worsening prognosis, mechanical ventilation doubled mortality risk, and elevated TBiL levels reflected impaired hepatic function. Extreme heart rate abnormalities also correlated with poor outcomes. Infection complications like spontaneous bacterial peritonitis and esophageal variceal bleeding were more frequent among non-survivors. The nomogram provides clinicians with a quantitative tool for mortality risk stratification, enabling early intervention strategies. Its findings align with global trends in cirrhosis-related mortality, emphasizing its clinical relevance and utility in managing high-risk patients effectively.

Deep learning : Predicting HCC surgery success with multimodal imaging

Deep learning is a subset of machine learning that uses artificial neural networks to model and analyze complex data patterns. It is particularly effective in tasks involving large datasets, such as image recognition, natural language processing, and medical diagnostics. Deep learning algorithms excel at automatically extracting meaningful features from raw data, making them highly suitable for applications in healthcare, such as predicting hepatocellular carcinoma (HCC) surgery success. ### Predicting HCC Surgery Success with Multimodal Imaging #### **What is Multimodal Imaging?** Multimodal imaging refers to the integration of multiple imaging techniques to provide comprehensive information about a patient's condition. In the context of HCC, multimodal imaging may involve combining triphasic computed tomography (CT), magnetic resonance imaging (MRI), and other imaging modalities. Each modality provides unique insights into tumor characteristics, vascular involvement, and surrounding tissue conditions. Triphasic CT, for instance, captures images in three phases (arterial, venous, and delayed), highlighting liver vascularization and tumor perfusion patterns. MRI, on the other hand, can provide detailed information about soft tissue and molecular composition. By combining these imaging modalities, clinicians can gain a more holistic understanding of tumor behavior and treatment outcomes. #### **Role of Deep Learning in Predicting HCC Surgery Success** Deep learning models like **Recur-NET** leverage multimodal imaging data to predict the likelihood of HCC recurrence after surgical resection. The integration of multimodal imaging and deep learning offers several advantages: 1. **Automated Feature Extraction**: - Traditional radiomics methods rely on manually defined features, which might miss subtle patterns in imaging data. Deep learning models autonomously extract meaningful features from multimodal imaging data, capturing complex patterns that might escape human detection. - For example, Recur-NET uses a residual neural network (ResNet) architecture to analyze triphasic CT images and identify imaging biomarkers associated with recurrence risk. 2. **Improved Accuracy**: - Deep learning models like Recur-NET have demonstrated high accuracy in predicting microvascular invasion (MVI), a key factor influencing HCC recurrence. The model achieved AUROC (Area Under the Receiver Operating Characteristic) values ranging from 0.77 to 0.86, showcasing its ability to discriminate recurrence risks effectively. 3. **Integration of Clinical Variables**: - In addition to imaging data, Recur-NET incorporates clinical variables such as patient demographics, tumor size, and liver function. This multimodal approach enhances the model's predictive power, as it accounts for both imaging and non-imaging factors affecting surgical outcomes. 4. **Risk Stratification**: - Deep learning models can stratify patients into different risk categories based on recurrence likelihood. This enables personalized post-surgical management, such as tailoring surveillance protocols or additional therapies for high-risk patients. 5. **Precision Medicine**: - By analyzing multimodal imaging data and clinical inputs, deep learning models contribute to precision medicine. They help refine treatment planning, improve surgical outcomes, and reduce recurrence rates, ultimately enhancing patient survival and quality of life. #### **Challenges and Future Directions** While deep learning models like Recur-NET show great promise, there are challenges to overcome for widespread clinical adoption: 1. **Generalizability**: - Recur-NET was trained on data primarily from Asian populations and triphasic CT imaging. Validation in Western populations and the incorporation of MRI-based models are needed to ensure broader applicability. 2. **Infrastructure Requirements**: - Clinical implementation requires robust infrastructure for processing large imaging datasets, integrating multimodal data, and validating models across diverse healthcare systems. 3. **Ethical and Regulatory Considerations**: - The use of AI in healthcare involves addressing ethical concerns, such as data privacy, algorithmic bias, and regulatory approval. #### **Future Implications** The integration of deep learning and multimodal imaging represents a significant advancement in predicting HCC surgery success. Models like Recur-NET exemplify the potential of AI in precision medicine, paving the way for: - Enhanced recurrence prediction. - Personalized treatment planning. - Improved surgical outcomes. - Reduced mortality rates in HCC patients. As AI technologies continue to evolve, deep learning models will play an increasingly vital role in transforming HCC management and advancing healthcare as a whole.

Artificial intelligence-assisted colonoscopy improves adenoma detection rates

Yes, artificial intelligence (AI)-assisted colonoscopy has been shown to improve adenoma detection rates (ADR). Adenoma detection is a critical measure in colonoscopy, as higher ADRs are directly linked to reduced colorectal cancer (CRC) incidence and mortality. Despite advancements, up to 20% of adenomas are still missed due to human limitations in visual detection. A study conducted at Hallym University Dongtan Sacred Heart Hospital evaluated AI-assisted colonoscopy using the SmartEndo CADe system, a deep-learning tool that highlights potential polyps in real time through bounding boxes and auditory alerts. The study included 474 patients in the AI-assisted group and compared them with 474 patients undergoing standard colonoscopy. Propensity score matching ensured both groups were balanced for clinical and procedural characteristics. The AI-assisted colonoscopy significantly improved ADR (35.9% vs. 26.4%, p=0.002), demonstrating its effectiveness in routine practice. It also increased the polyp detection rate (53.2% vs. 46.2%, p=0.038) and the mean adenomas per colonoscopy (0.69 vs. 0.43, p<0.001). AI systems were particularly beneficial for trainees, boosting their ADR from 27.2% to 51.5% (p=0.023). Additionally, AI-assisted colonoscopy proved valuable in detecting subtle, small, or flat lesions, even among high-performing endoscopists. While AI improves detection, challenges such as false positives and increased procedure time persist. However, AI assistance standardizes detection quality, reduces operator variability, and enhances early CRC prevention. Despite limitations like the study’s retrospective nature, AI-assisted colonoscopy represents a significant advancement in improving colonoscopy quality and outcomes.