Position statement on priorities for artificial intelligence in GI endoscopy: a report by the ASGE Task Force

High-performance medicine: the convergence of human and artificial intelligence

Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450 · see citing works

LabelMe: A Database and Web-Based Tool for Image Annotation

Russell, B. C., Torralba, A., Murphy, K. P., & Freeman, W. T. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-007-0090-8 · Cited by 4,210 · see citing works

MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports

Johnson, A. E. W., Pollard, T. J., Berkowitz, S. J., Greenbaum, N. R., Lungren, M. P., Deng, C.-Y., Mark, R. G., & Horng, S. (2019) · Scientific Data · doi:10.1038/s41597-019-0322-0 · Cited by 1,816 · see citing works

Untitled work

(2019) · Cited by 986 · see citing works

Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study

Wang, P., Berzin, T. M., Glissen Brown, J. R., Bharadwaj, S., Becq, A., Xiao, X., Liu, P., Li, L., Song, Y., Zhang, D., et al. (2019) · Gut · doi:10.1136/gutjnl-2018-317500 · Cited by 865 · see citing works

Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy

Urban, G., Tripathi, P. V., Alkayali, T., Mittal, M., Jalali, F., Karnes, W. E., & Baldi, P. (2018) · Gastroenterology · doi:10.1053/j.gastro.2018.06.037 · Cited by 746 · see citing works

Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model

Byrne, M. F., Chapados, N., Soudan, F., Oertel, C., Linares Pérez, M., Kelly, R., Iqbal, N., Chandelier, F., & Rex, D. K. (2019) · Gut · doi:10.1136/gutjnl-2017-314547 · Cited by 635 · see citing works

The American Society for Gastrointestinal Endoscopy PIVI (Preservation and Incorporation of Valuable Endoscopic Innovations) on real-time endoscopic assessment of the histology of diminutive colorectal polyps

Rex, D. K., Kahi, C., O'Brien, M., Levin, T. R., Pohl, H., Rastogi, A., Burgart, L., Imperiale, T., Ladabaum, U., Cohen, J., et al. (2011) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2011.01.023 · Cited by 629 · see citing works

Artificial intelligence in supply chain management: theory and applications

Min, H. (2009) · International Journal of Logistics Research and Applications · doi:10.1080/13675560902736537 · Cited by 575 · see citing works

Physician stress and burnout: the impact of health information technology

Gardner, R. L., Cooper, E., Haskell, J., Harris, D. A., Poplau, S., Kroth, P. J., & Linzer, M. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy145 · Cited by 566 · see citing works

Real-Time Use of Artificial Intelligence in Identification of Diminutive Polyps During Colonoscopy: A Prospective Study

Mori, Y., Kudo, S.-E., Misawa, M., Saito, Y., Ikematsu, H., Hotta, K., Ohtsuka, K., Urushibara, F., Kataoka, S., Ogawa, Y., et al. (2018) · Annals of Internal Medicine · doi:10.7326/m18-0249 · Cited by 509 · see citing works

Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy

Wang, P., Xiao, X., Brown, J. R. G., Berzin, T. M., Tu, M., Xiong, F., Hu, X., Liu, P., Song, Y., Zhang, D., et al. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0301-3 · Cited by 484 · see citing works

Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study

Wang, P., Liu, X., Berzin, T. M., Glissen Brown, J. R., Liu, P., Zhou, C., Lei, L., Li, L., Guo, Z., Lei, S., et al. (2020) · The Lancet Gastroenterology & Hepatology · doi:10.1016/s2468-1253(19)30411-x · Cited by 415 · see citing works

Detection of colorectal adenomas with a real-time computer-aided system (ENDOANGEL): a randomised controlled study

Gong, D., Wu, L., Zhang, J., Mu, G., Shen, L., Liu, J., Wang, Z., Zhou, W., An, P., Huang, X., et al. (2020) · The Lancet Gastroenterology & Hepatology · doi:10.1016/s2468-1253(19)30413-3 · Cited by 393 · see citing works

A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop

Langlotz, C. P., Allen, B., Erickson, B. J., Kalpathy-Cramer, J., Bigelow, K., Cook, T. S., Flanders, A. E., Lungren, M. P., Mendelson, D. S., Rudie, J. D., et al. (2019) · Radiology · doi:10.1148/radiol.2019190613 · Cited by 368 · see citing works

Randomised controlled trial of WISENSE, a real-time quality improving system for monitoring blind spots during esophagogastroduodenoscopy

Wu, L., Zhang, J., Zhou, W., An, P., Shen, L., Liu, J., Jiang, X., Huang, X., Mu, G., Wan, X., et al. (2019) · Gut · doi:10.1136/gutjnl-2018-317366 · Cited by 319 · see citing works

Application of Synchronous Text-Based Dialogue Systems in Mental Health Interventions: Systematic Review

Hoermann, S., McCabe, K. L., Milne, D. N., & Calvo, R. A. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.7023 · Cited by 263 · see citing works

Exploring Large-scale Public Medical Image Datasets

Oakden-Rayner, L. (2020) · Academic Radiology · doi:10.1016/j.acra.2019.10.006 · Cited by 250 · see citing works

Physician Time Spent Using the Electronic Health Record During Outpatient Encounters

Overhage, J. M., & McCallie, D. P. (2020) · Annals of Internal Medicine · doi:10.7326/m18-3684 · Cited by 249 · see citing works

Ten Ways Artificial Intelligence Will Transform Primary Care

Lin, S. Y., Mahoney, M. R., & Sinsky, C. A. (2019) · Journal of General Internal Medicine · doi:10.1007/s11606-019-05035-1 · Cited by 248 · see citing works

Artificial intelligence using convolutional neural networks for real-time detection of early esophageal neoplasia in Barrett’s esophagus (with video)

Hashimoto, R., Requa, J., Dao, T., Ninh, A. Q., Tran, E., Mai, D., Lugo, M., Chehade, N. E.-H., Chang, K. J., Karnes, W. E., et al. (2020) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2019.12.049 · Cited by 237 · see citing works

A multimodality test to guide the management of patients with a pancreatic cyst

Springer, S., Masica, D. L., Dal Molin, M., Douville, C., Thoburn, C. J., Afsari, B., Li, L., Cohen, J. D., Thompson, E., Allen, P. J., et al. (2019) · Science Translational Medicine · doi:10.1126/scitranslmed.aav4772 · Cited by 217 · see citing works

Real-time automated diagnosis of precancerous lesions and early esophageal squamous cell carcinoma using a deep learning model (with videos)

Guo, L., Xiao, X., Wu, C., et al. (2019) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2019.08.018 · Cited by 202 · see citing works

Artificial intelligence using a convolutional neural network for automatic detection of small‐bowel angioectasia in capsule endoscopy images

Tsuboi, A., Oka, S., Aoyama, K., Saito, H., Aoki, T., Yamada, A., Matsuda, T., Fujishiro, M., Ishihara, S., Nakahori, M., et al. (2019) · Digestive Endoscopy · doi:10.1111/den.13507 · Cited by 165 · see citing works

A Clinician's Guide to Artificial Intelligence: How to Critically Appraise Machine Learning Studies

Faes, L., Liu, X., Wagner, S. K., Fu, D. J., Balaskas, K., Sim, D. A., Bachmann, L. M., Keane, P. A., & Denniston, A. K. (2020) · Translational Vision Science & Technology · doi:10.1167/tvst.9.2.7 · Cited by 162 · see citing works

A novel artificial intelligence system for the assessment of bowel preparation (with video)

Zhou, J. N., Wu, L., Wan, X., Shen, L., Liu, J., Zhang, J., Jiang, X., Wang, Z., Yu, S., Kang, J., et al. (2019) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2019.11.026 · Cited by 146 · see citing works

Quality assurance of computer-aided detection and diagnosis in colonoscopy

Vinsard, D. G., Mori, Y., Misawa, M., Kudo, S., Rastogi, A. N., Bağcı, U., Rex, D. K., & Wallace, M. B. (2019) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2019.03.019 · Cited by 141 · see citing works

The digital scribe

Coiera, E., Kocaballi, B., Halamka, J., & Laranjo, L. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0066-9 · Cited by 132 · see citing works

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

Chen, D., Wu, L., Li, Y., Zhang, J., Liu, J., Huang, L., Jiang, X., Huang, X., Mu, G., Hu, S., et al. (2019) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2019.09.016 · Cited by 96 · see citing works

Natural language processing as an alternative to manual reporting of colonoscopy quality metrics

Raju, G. S., Lum, P. J., Slack, R. S., Thirumurthi, S., Lynch, P. M., Miller, E., Weston, B. R., Davila, M. L., Bhutani, M. S., Shafi, M. A., et al. (2015) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2015.01.049 · Cited by 88 · see citing works

Multi-Center Colonoscopy Quality Measurement Utilizing Natural Language Processing

Imler, T. D., Morea, J., Kahi, C. J., Cardwell, J., Johnson, C. S., Xu, H., Ahnen, D. J., Antaki, F., Ashley, C., Baffy, G., et al. (2015) · The American Journal of Gastroenterology · doi:10.1038/ajg.2015.51 · Cited by 85 · see citing works

Deep Learning to Classify Intraductal Papillary Mucinous Neoplasms Using Magnetic Resonance Imaging

Corral, J. E., Hussein, S., Kandel, P., Bolan, C. W., Bagci, U., & Wallace, M. B. (2019) · Pancreas · doi:10.1097/mpa.0000000000001327 · Cited by 78 · see citing works

Extension of the CONSORT and SPIRIT statements

Liu, X., Faes, L., Calvert, M. J., & Denniston, A. K. (2019) · The Lancet · doi:10.1016/s0140-6736(19)31819-7 · Cited by 65 · see citing works

Use of artificial intelligence-based analytics from live colonoscopies to optimize the quality of the colonoscopy examination in real time: proof of concept

Thakkar, S., Carleton, N. M., Rao, B., & Syed, A. (2020) · Gastroenterology · doi:10.1053/j.gastro.2019.12.035 · Cited by 47 · see citing works

Programming Tools for Messenger-Based Chatbot System Organization: Implication for Outpatient and Translational Medicines

Abashev, A., Grigoryev, R. A., Grigorian, K., & Boyko, V. (2016) · BioNanoScience · doi:10.1007/s12668-016-0376-9 · Cited by 45 · see citing works

HyperKvasir, A Comprehensive Multi-Class Image and Video Dataset for Gastrointestinal EndoscopyPreprint

Borgli, H., Thambawita, V. L., Smedsrud, P. H., et al. (2019) · doi:10.31219/osf.io/mkzcq · Cited by 37 · see citing works

GTCreator: a flexible annotation tool for image-based datasets

Bernal, J., Histace, A., Masana, M., et al. (2018) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-018-1864-x · Cited by 34 · see citing works

Weighting Primary Care Patient Panel Size: A Novel Electronic Health Record-Derived Measure Using Machine Learning

Rajkomar, A., Yim, J. W. L., Grumbach, K., & Parekh, A. (2016) · JMIR Medical Informatics · doi:10.2196/medinform.6530 · Cited by 31 · see citing works

The Role of the ACR Data Science Institute in Advancing Health Equity in Radiology

Allen, B., & Dreyer, K. J. (2019) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2018.12.038 · Cited by 28 · see citing works

Artificial Intelligence Can Accurately Detect Tools Used During Colonoscopy: Another Step Forward Toward Autonomous Report Writing: Presidential Poster Award

Samarasena, J., Yu, A. R., Torralba, E. J., et al. (2018) · The American Journal of Gastroenterology · doi:10.14309/00000434-201810001-01075 · Cited by 9 · see citing works

Experimental evidence of effective human–AI collaboration in medical decision-making

Reverberi, C., Rigon, T., Solari, A., Hassan, C., Cherubini, P., GI Genius CADx Study Group, Antonelli, G., Awadie, H., Bernhofer, S., Carballal, S., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-18751-2 · Cited by 235 · see citing works

Artificial intelligence in inflammatory bowel disease: implications for clinical practice and future directions

Ahmad, H. A., East, J. E., Panaccione, R., Travis, S., Canavan, J. B., Usiskin, K., & Byrne, M. F. (2023) · Intestinal Research · doi:10.5217/ir.2023.00020 · Cited by 37 · see citing works

Structural Analysis of the History of Early Greek and Early European Philosophy

Riegel, K. F. (1976) · doi:10.1007/978-1-4684-0763-1_10

LOOPS AND SUBSCRIPTED VARIABLES

SANDERSON, P. C. (1973) · Elsevier eBooks · doi:10.1016/b978-0-408-70528-8.50011-x

Inference and the Conditions of Its Correctness

Ajdukiewicz, K. (1974) · doi:10.1007/978-94-010-2109-8_9

The effectiveness of different model statement variants for eliciting information and cues to deceit

Leal, S., Vrij, A., Hudson, C. A., Capuozzo, P., & Deeb, H. (2021) · Legal and Criminological Psychology · doi:10.1111/lcrp.12200 · Cited by 10 · see citing works

On the Enduring and Fleeting Uses of the Teaching Statement

McElroy, K. (2023) · Canadian Journal of Academic Librarianship · doi:10.33137/cjal-rcbu.v9.40960