Your browser doesn't support javascript.
loading
Benefits and challenges in implementation of artificial intelligence in colonoscopy: World Endoscopy Organization position statement.
Mori, Yuichi; East, James E; Hassan, Cesare; Halvorsen, Natalie; Berzin, Tyler M; Byrne, Michael; von Renteln, Daniel; Hewett, David G; Repici, Alessandro; Ramchandani, Mohan; Al Khatry, Maryam; Kudo, Shin-Ei; Wang, Pu; Yu, Honggang; Saito, Yutaka; Misawa, Masashi; Parasa, Sravanthi; Matsubayashi, Carolina Ogawa; Ogata, Haruhiko; Tajiri, Hisao; Pausawasdi, Nonthalee; Dekker, Evelien; Ahmad, Omer F; Sharma, Prateek; Rex, Douglas K.
Afiliação
  • Mori Y; Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway.
  • East JE; Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway.
  • Hassan C; Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan.
  • Halvorsen N; Translational Gastroenterology Unit, John Radcliffe Hospital, University of Oxford, Oxford, UK.
  • Berzin TM; NIHR Oxford Biomedical Research Centre, Oxford, UK.
  • Byrne M; Division of Gastroenterology and Hepatology, Mayo Clinic Healthcare, London, UK.
  • von Renteln D; Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy.
  • Hewett DG; Endoscopy Unit, Humanitas Clinical and Research Center - IRCCS, Rozzano, Italy.
  • Repici A; Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway.
  • Ramchandani M; Division of Gastroenterology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, USA.
  • Al Khatry M; Department of Medicine, The University of British Columbia, Vancouver, Canada.
  • Kudo SE; Division of Gastroenterology, University of Montreal Medical Center (CHUM) and Research Center (CRCHUM), Montreal, Canada.
  • Wang P; School of Medicine, The University of Queensland, Brisbane, Australia.
  • Yu H; Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy.
  • Saito Y; Endoscopy Unit, Humanitas Clinical and Research Center - IRCCS, Rozzano, Italy.
  • Misawa M; Asian Institute of Gastroenterology, Hyderabad, India.
  • Parasa S; Department of Gastroenterology, Obaidulla Hospital, Ras Al Khaimah, United Arab Emirates.
  • Matsubayashi CO; Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan.
  • Ogata H; Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, China.
  • Tajiri H; Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.
  • Pausawasdi N; Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan.
  • Dekker E; Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan.
  • Ahmad OF; Swedish Medical Center, Seattle, USA.
  • Sharma P; Gastrointestinal Endoscopy Unit, Gastroenterology Department, University of São Paulo Medical School, São Paulo, Brazil.
  • Rex DK; Center for Diagnostic and Therapeutic Endoscopy, School of Medicine, Keio University, Tokyo, Japan.
Dig Endosc ; 35(4): 422-429, 2023 May.
Article em En | MEDLINE | ID: mdl-36749036
ABSTRACT
The number of artificial intelligence (AI) tools for colonoscopy on the market is increasing with supporting clinical evidence. Nevertheless, their implementation is not going smoothly for a variety of reasons, including lack of data on clinical benefits and cost-effectiveness, lack of trustworthy guidelines, uncertain indications, and cost for implementation. To address this issue and better guide practitioners, the World Endoscopy Organization (WEO) has provided its perspective about the status of AI in colonoscopy as the position statement. WEO Position Statement Statement 1.1 Computer-aided detection (CADe) for colorectal polyps is likely to improve colonoscopy effectiveness by reducing adenoma miss rates and thus increase adenoma detection; Statement 1.2 In the short term, use of CADe is likely to increase health-care costs by detecting more adenomas; Statement 1.3 In the long term, the increased cost by CADe could be balanced by savings in costs related to cancer treatment (surgery, chemotherapy, palliative care) due to CADe-related cancer prevention; Statement 1.4 Health-care delivery systems and authorities should evaluate the cost-effectiveness of CADe to support its use in clinical practice; Statement 2.1 Computer-aided diagnosis (CADx) for diminutive polyps (≤5 mm), when it has sufficient accuracy, is expected to reduce health-care costs by reducing polypectomies, pathological examinations, or both; Statement 2.2 Health-care delivery systems and authorities should evaluate the cost-effectiveness of CADx to support its use in clinical practice; Statement 3 We recommend that a broad range of high-quality cost-effectiveness research should be undertaken to understand whether AI implementation benefits populations and societies in different health-care systems.
Assuntos
Palavras-chave

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Colorretais / Pólipos do Colo Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Colorretais / Pólipos do Colo Idioma: En Ano de publicação: 2023 Tipo de documento: Article