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• W3200633654 abstract "We read with interest the article by Laique et al1Laique S.N. Hayat U. Sarvepalli S. et al.Application of optical character recognition with natural language processing for large-scale quality metric data extraction in colonoscopy reports.Gastrointest Endosc. 2021; 93: 750-757Abstract Full Text Full Text PDF PubMed Scopus (10) Google Scholar and the respective editorial by Pilonis and Kaminski2Pilonis N.D. Kaminski M.F. Will machines decipher colonoscopy quality from endoscopists' notes?.Gastrointest Endosc. 2021; 93: 758-760Abstract Full Text Full Text PDF PubMed Scopus (1) Google Scholar on the use of commercially available software tools for large-scale quality metric data extraction from colonoscopy and pathology reports. In both articles, the perspective is placed on an attainable streamlining of quality indicators extraction by the hybrid solution of optical character recognition (OCR) and natural language processing (NLP), thereby offsetting the possible lack of structured, nonauditable, GI electronic healthcare records (EHR). Considering the speed at which artificial intelligence (AI) technologies are evolving, there is a distinct possibility for worldwide deployment of such solutions in routine clinical practice.3Ahmad O.F. Stoyanov D. Lovat L.B. Barriers and pitfalls for artificial intelligence in gastroenterology: ethical and regulatory issues.Tech Innov Gastrointest Endosc. 2020; 22: 80-84Abstract Full Text Full Text PDF Scopus (12) Google Scholar Although salient as an idea and in line with the trend of AI integration in endoscopy, the proposed validation by Laique et al1Laique S.N. Hayat U. Sarvepalli S. et al.Application of optical character recognition with natural language processing for large-scale quality metric data extraction in colonoscopy reports.Gastrointest Endosc. 2021; 93: 750-757Abstract Full Text Full Text PDF PubMed Scopus (10) Google Scholar is against a known lesser reference standard: human performance in deciding, eg, colon cleanliness or extracting quality indicators from structured GI reporting systems. In an era when singularity4https://en.wikipedia.org/wiki/The_Singularity_Is_NearGoogle Scholar is much discussed, we need to reconsider our traditional ways and approaches. Maybe we simply need to immerse in the attainable potential of AI providing for us what is downright tedious yet necessary for high-quality, precise patient care. Perhaps we could ask our engineering colleagues to develop an interface that can filter and use optico-acoustic stimuli of the endoscopy room, including endoscopic images of pathologic features, voice-activated capture of, eg, colonoscopy landmarks, and transcription of room interactions much like the “black box” of airplanes cockpit. Pandemic-related issues have already jeopardized many ongoing colorectal cancer (CRC) screening programs.5Balzora S. Issaka R.B. Anyane-Yeboa A. et al.Impact of COVID-19 on colorectal cancer disparities and the way forward.Gastrointest Endosc. 2020; 92: 946-950Abstract Full Text Full Text PDF PubMed Scopus (29) Google Scholar This will have a severe future impact, especially in low-income and middle-income countries where healthcare budgets are seriously depleted. Even highly developed countries with well-established CRC screening will struggle to provide delayed higher colonoscopy demand,5Balzora S. Issaka R.B. Anyane-Yeboa A. et al.Impact of COVID-19 on colorectal cancer disparities and the way forward.Gastrointest Endosc. 2020; 92: 946-950Abstract Full Text Full Text PDF PubMed Scopus (29) Google Scholar let alone invest in the AI systems infrastructure required to provide large-scale data extractions. However, there is evidence that adaptation through the adoption of innovative solutions is on the rise.6Koulaouzidis G. Marlicz W. Koulaouzidis A. Telemedicine in the time of COVID-19: better late than never.Am J Gastroenterol. 2021; 116: 1088-1089Crossref PubMed Scopus (4) Google Scholar,7Koulaouzidis A. Marlicz W. Wenzek H. et al.Returning to digestive endoscopy normality will be slow and must include novelty and telemedicine.Dig Liver Dis. 2020; 52: 1099-1101Abstract Full Text Full Text PDF PubMed Scopus (8) Google Scholar Laique et al1Laique S.N. Hayat U. Sarvepalli S. et al.Application of optical character recognition with natural language processing for large-scale quality metric data extraction in colonoscopy reports.Gastrointest Endosc. 2021; 93: 750-757Abstract Full Text Full Text PDF PubMed Scopus (10) Google Scholar conducted their study in a large academic medical center, where AI technologies seem to work the best. GI endoscopy reporting systems and electronic healthcare records outside reference centers are heterogeneous and, if not outdated, surely not up to speed for ease in data subtraction. Therefore, external validation will be crucial to assess the robustness of their findings and map out impediments for implementing AI/OCR/NLP into daily endoscopy practices. As we are speeding down a path of human-robot/AI interface8Mori Y. Kudo S.E. Misawa M. Can artificial intelligence standardise colonoscopy quality?.Lancet Gastroenterol Hepatol. 2020; 5: 331-332Abstract Full Text Full Text PDF PubMed Scopus (3) Google Scholar in the provision of GI endoscopy, we shall not forget the patients’ perspective on GI screening with a preference toward noninvasive options. With these in mind, it will be ignominious not to consider jumping ahead with new technologies and keep thinking outside our daily “boxes.” Dr Koulaouzidis is a cofounder of AJM Medkicaps Ltd and iCERV Ltd and a consultant for Jinshan. He is also the recipient of a grant from GivenImaging Ltd; of material support for clinical research from SynMed/Intromedic; of honoraria and lecture fees from Jinshan, Dr FalkPharma UK, and Ferring; and of educational travel support from Aquilant, Jinshan, Dr FalkPharma, Almirall, and Ferring; and a participant in advisory board meetings for Tillots, Ankon, and Dr FalkPharmaUK. The other authors disclosed no financial relationships." @default.
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• W3200633654 title "Artificial intelligence or colonoscopy quality the likes of which have never been seen" @default.
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