How Do AI-Assisted Diagnostic Tools Impact Clinical Decision-Making And Patient Outcomes In Acute Gastrointestinal Bleeding Cases? : A Comprehensive Systematic Review
Keywords:
artificial intelligence, acute gastrointestinal bleeding, diagnostic accuracy, clinical decision-making, systematic reviewAbstract
Introduction: Acute gastrointestinal bleeding (GIB) is a life-threatening condition requiring prompt diagnosis and intervention. Despite advancements in endoscopy, challenges like diagnostic variability and resource allocation persist. Artificial intelligence (AI)-assisted tools have shown promise in improving detection rates and workflow efficiency in gastroenterology, but their impact on clinical decision-making and patient outcomes in acute GIB remains underexplored. Methods: This systematic review adhered to PRISMA 2020 guidelines. Eligible studies included those evaluating AI-based diagnostic tools in acute GIB cases, with outcomes such as diagnostic accuracy, clinical decision-making changes, or patient outcomes. Databases like PubMed, Sagepub, and Google Scholar were searched using Boolean MeSH keywords. Data extraction focused on study design, AI tool characteristics, diagnostic performance, and clinical impact. Results: Among 40 included studies, AI demonstrated high diagnostic accuracy, with sensitivities and specificities exceeding 90% in lesion detection. For instance, convolutional neural networks achieved 95.4% accuracy in identifying ulcers and hemorrhages. However, only one study reported a modest improvement in predicting endoscopic intervention needs (AUC: 0.68). While AI reduced reading time by 30% in some studies, its impact on patient outcomes (e.g., mortality, rebleeding) was rarely addressed. Most evidence came from retrospective studies or meta-analyses, with limited prospective or randomized controlled trials. Discussion: AI enhances diagnostic accuracy and workflow efficiency but lacks robust evidence linking it to improved patient outcomes in acute GIB. Key limitations include methodological heterogeneity, scarce safety data, and a focus on non-acute settings. Prospective studies are needed to evaluate AI's real-world clinical impact. Conclusion: AI shows potential as an adjunct tool in acute GIB management but requires further validation to confirm its clinical utility. Future research should prioritize patient-centered outcomes and standardized reporting.References
A. Ebigbo, and H. Messmann. “Artificial Intelligence in the Upper GI Tract: The Future Is Fast Approach- ing.” Gastrointestinal Endoscopy, 2021.
A. Kahn, and C. Leggett. “Artificial Intelligence in the Age of Cognitive Endoscopy.” Gastrointestinal Endoscopy, 2020.
A. Murino, and A. Rimondi. “Automated Artificial Intelligence Scoring Systems for the Endoscopic Assess- ment of Ulcerative Colitis: How Far Are We from Clinical Application?” Gastrointestinal Endoscopy, 2022.
Anastasios Koulaouzidis, W. Marlicz, Hagen Wenzek, G. Koulaouzidis, R. Eliakim, and E. Toth. “Returning to Digestive Endoscopy Normality Will Be Slow and Must Include Novelty and Telemedicine.” Digestive and Liver Disease, 2020.
B. Mohan, Shahab R. Khan, L. Kassab, S. Ponnada, S. Chandan, T. Ali, P. Dulai, D. Adler, and G. Kochhar. “High Pooled Performance of Convolutional Neural Networks in Computer-Aided Diagnosis of GI Ulcers and/or Hemorrhage on Wireless Capsule Endoscopy Images: A Systematic Review and Meta-Analysis.” Gastrointestinal Endoscopy, 2020.
B. Vadhwana, M. Tarazi, and Vanash Patel. “The Role of Artificial Intelligence in Prospective Real-Time His- tological Prediction of Colorectal Lesions During Colonoscopy: A Systematic Review and Meta-Analysis.” Diagnostics, 2023.
Bing Lv, Lihong Ma, Yanping Shi, Tao Tao, and Yanting Shi. “A Systematic Review and Meta-Analysis of Artificial Intelligence-Diagnosed Endoscopic Remission in Ulcerative Colitis.” iScience, 2023.
C. Hassan, M. Spadaccini, A. Iannone, R. Maselli, M. Jovani, V. Chandrasekar, G. Antonelli, et al. “Per- formance of Artificial Intelligence for Colonoscopy Regarding Adenoma and Polyp Detection: A Meta- Analysis.” Gastrointestinal Endoscopy, 2020.
C. S. Bang, J. Lee, and G. Baik. “Artificial Intelligence for the Prediction of Helicobacter Pylori Infection in Endoscopic Images: Systematic Review and Meta-Analysis Of Diagnostic Test Accuracy.” Journal of Medical Internet Research, 2020.
———. “Computer-Aided Diagnosis of Gastrointestinal Ulcer and Hemorrhage Using Wireless Capsule En- doscopy: Systematic Review and Diagnostic Test Accuracy Meta-Analysis.” Journal of Medical Internet Research, 2021.
Daisuke Yamaguchi, R. Shimoda, K. Miyahara, Takahiro Yukimoto, Y. Sakata, A. Takamori, Yumi Mizuta, et al. “Impact of an Artificial Intelligence‐aided Endoscopic Diagnosis System on Improving Endoscopy Quality for Trainees in Colonoscopy: Prospective, Randomized, Multicenter Study.” Digestive Endoscopy, 2023.
De Luo, Fei Kuang, Juan Du, Mengjia Zhou, Xiangdong Liu, Xinchen Luo, Yong Tang, Bo Li, and S. Su. “Artificial Intelligence–Assisted Endoscopic Diagnosis of Early Upper Gastrointestinal Cancer: A Systematic Review and Meta-Analysis.” Frontiers in Oncology, 2022.
Di Chen, Lianlian Wu, Yanxia Li, Jun Zhang, Jun Liu, Li Huang, Xiaoda Jiang, et al. “Comparing Blind Spots of Unsedated Ultrafine, Sedated, and Unsedated Conventional Gastroscopy with and Without Artificial Intelligence: A Prospective, Single-Blind, 3-Parallel-Group, Randomized, Single-Center Trial.” Gastrointestinal Endoscopy, 2020.
Fatima Faqar- Uz-Zaman, N. Filmann, D. Mahkovic, Michael von Wagner, Charlotte Detemble, Ulf Kippke, Ursula Marschall, et al. “Study Protocol for a Prospective, Double-Blinded, Observational Study Investigating the Diagnostic Accuracy of an App-Based Diagnostic Health Care Application in an Emergency Room Setting: The eRadaR Trial.” BMJ Open, 2021.
Han-Fang Zhang, Xinyu Yang, Ye Tao, Xinyi Zhang, and Xuan Huang. “Diagnostic Accuracy of Endocy- toscopy via Artificial Intelligence in Colorectal Lesions: A Systematic Review and Meta‑analysis.” PLoS ONE, 2023.
I. Alzahrani, K. Onizuka, Z. Duhailib, Fatema Alalkim, Yuhong Yuan, Clara Lu, F. Tse, G. Leontiadis, and W. Alhazzani. “Mo1010 SOMATOSTATIN AND ANALOGUES FOR ACUTE NON-VARICEAL UPPER GASTROINTESTINAL BLEEDING: A SYSTEMATIC REVIEW AND METAANALYSIS OF RANDOMIZED TRIALS,” 2020.
I. Barua, D. Vinsard, Henriette C. Jodal, M. Løberg, M. Kalager, Ø. Holme, M. Misawa, M. Bretthauer, and Y. Mori. “Artificial Intelligence for Polyp Detection During Colonoscopy: A Systematic Review and Meta-Analysis.” Endoscopy, 2020.
I. Veisman, A. Oppenheim, R. Maman, Nadav Kofman, Ilan Edri, L. Dar, E. Klang, et al. “A Novel Prediction Tool for Endoscopic Intervention in Patients with Acute Upper Gastro-Intestinal Bleeding.” Journal of Clinical Medicine, 2022.
J. Arribas, G. Antonelli, L. Frazzoni, L. Fuccio, A. Ebigbo, F. van der Sommen, N. Ghatwary, et al. “Stan- dalone Performance of Artificial Intelligence for Upper GI Neoplasia: A Meta-Analysis.” Gut, 2020.
J. Limdi, and F. Farraye. “Automated Endoscopic Assessment in Ulcerative Colitis: The Next Frontier.” Gastrointestinal Endoscopy, 2021
———. “Real-Time Use of Artificial Intelligence at Colonoscopy Predicts Relapse in Ulcerative Colitis: Predicting with ”Intelligence”.” Gastrointestinal Endoscopy, 2022.
Jonathan Makar, Jonathan Abdelmalak, D. Con, Bilal Hafeez, and Mayur Garg. “Use of Artificial In- telligence Improves Colonoscopy Performance in Adenoma Detection: A Systematic Review and Meta- Analysis.” Gastrointestinal Endoscopy, 2024.
M. Meinikheim, R. Mendel, Christoph Palm, Andreas Probst, A. Muzalyova, M. Scheppach, Sandra Nagl, et al. “Influence of Artificial Intelligence on the Diagnostic Performance of Endoscopists in the Assessment of Barrett’s Esophagus: A Tandem Randomized and Video Trial.” Endoscopy, 2023.
M. Wallace, Prateek Sharma, P. Bhandari, J. East, G. Antonelli, R. Lorenzetti, M. Vieth, et al. “Impact of Artificial Intelligence on Miss Rate of Colorectal Neoplasia.” Gastroenterology, 2022.
M. Wei, Shmuel Fay, Diana Yung, U. Ladabaum, and U. Kopylov. “Artificial Intelligence–Assisted Colonoscopy in Real-World Clinical Practice: A Systematic Review and Meta-Analysis.” Clinical and Translational Gastroenterology, 2023.
Na Li, Jian Yang, Xiaodong Li, Yanting Shi, and Kunhong Wang. “Accuracy of Artificial Intelligence- Assisted Endoscopy in the Diagnosis of Gastric Intestinal Metaplasia: A Systematic Review and Meta- Analysis.” PLoS ONE, 2024.
Pu Wang, T. Berzin, Jeremy R. Glissen Brown, Shishira S. Bharadwaj, A. Becq, Xun Xiao, Peixi Liu, et al. “Real-Time Automatic Detection System Increases Colonoscopic Polyp and Adenoma Detection Rates: A Prospective Randomised Controlled Study.” Gut, 2019.
Ryosuke Kikuchi, Kazuaki Okamoto, Tsuyoshi Ozawa, J. Shibata, S. Ishihara, and Tomohiro Tada. “Endo- scopic Artificial Intelligence for Image Analysis in Gastrointestinal Neoplasms.” Digestion, 2024.
S. Chidambaram, V. Sounderajah, N. Maynard, and S. Markar. “Diagnostic Performance of Artificial Intelligence-Centred Systems in the Diagnosis and Postoperative Surveillance of Upper Gastrointesti- nal Malignancies Using Computed Tomography Imaging: A Systematic Review and Meta-Analysis of Diagnostic Accuracy.” Annals of Surgical Oncology, 2021.
S. Gross, and Matthew B. McNeil. “Siri Here, Cecum Reached, but Please Wash That Fold: Will Artificial Intelligence Improve Gastroenterology?” Gastrointestinal Endoscopy, 2020.
S. Nazarian, B. Glover, H. Ashrafian, A. Darzi, and J. Teare. “Diagnostic Accuracy of Artificial Intelligence and Computer-Aided Diagnosis for the Detection and Characterization of Colorectal Polyps: Systematic Review and Meta-Analysis.” Journal of Medical Internet Research, 2021.
S. Soffer, E. Klang, Orit Shimon, Noy Nachmias, R. Eliakim, S. Ben-Horin, U. Kopylov, and Y. Barash. “Deep Learning for Wireless Capsule Endoscopy: A Systematic Review and Meta-Analysis.” Gastroin- testinal Endoscopy, 2020.
Shenghan Lou, Fenqi Du, Wenjie Song, Yixiu Xia, Xinyu Yue, Da Yang, Binbin Cui, Yanlong Liu, and Peng Han. “Artificial Intelligence for Colorectal Neoplasia Detection During Colonoscopy: A Systematic Review and Meta-Analysis of Randomized Clinical Trials.” EClinicalMedicine, 2023.
T. K. Lui, V. Tsui, and W. Leung. “Accuracy of Artificial Intelligence-Assisted Detection of Upper GI Lesions: A Systematic Review and Meta-Analysis.” Gastrointestinal Endoscopy, 2020.
Thomas Y T Lam, Max Fk Cheung, Y. Munro, Kong Meng. Lim, Dennis Shung, and J. J. Sung. “Ran- domized Controlled Trials of Artificial Intelligence in Clinical Practice: Systematic Review.” Journal of Medical Internet Research, 2022
Tomonori Aoki, Atsuo Yamada, and K. Koike. “The Exceptional Performance of Deep Learning for Capsule Endoscopy: Will Such Quality Be Maintained in Clinical Scenarios?” Gastrointestinal Endoscopy, 2021.
Viveka Chinnasamy, Vorada Sakulsaengprapha, and Simon C. Mathews. “The Benefit of Artificial Intelligence-Based Diagnosis in Gastroenterology and Hepatology Is Highly Variable: A Diagnostic Need and Burden Analysis.” Gastroenterology, 2023.
Xi-Feng Jin, Yan-Hong Ma, Jun‐Wen Shi, and Jian-Ting Cai. “Efficacy of Artificial Intelligence in Reducing Miss Rates of Gastrointestinal Adenomas, Polyps, and Sessile Serrated Lesions: A Meta-Analysis of Randomized Controlled Trials.” Gastrointestinal Endoscopy, 2024.
Y. Mori, S. Kudo, M. Misawa, Yutaka Saito, H. Ikematsu, K. Hotta, K. Ohtsuka, et al. “Real-Time Use of Artificial Intelligence in Identification of Diminutive Polyps During Colonoscopy.” Annals of Internal Medicine, 2018.
Yanting Shi, Ning Wei, Kunhong Wang, Tao Tao, F. Yu, and Bing Lv. “Diagnostic Value of Artifi- cial Intelligence-Assisted Endoscopy for Chronic Atrophic Gastritis: A Systematic Review and Meta- Analysis.” Frontiers in Medicine, 2023.
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