{"title":"Implementation of Artificial Intelligence in Colonoscopy Practice in Japan.","authors":"Masashi Misawa, Shin-Ei Kudo, Yuichi Mori","doi":"10.31662/jmaj.2024-0133","DOIUrl":null,"url":null,"abstract":"<p><p>This review outlines the implementation of artificial intelligence (AI) into colonoscopy procedures which includes its history, processes, and challenges. We highlight the importance of the collaborative effort between medical and computer science researchers in the development of AI tools in colonoscopy, particularly focusing on the roles of computer-aided detection (CADe) and computer-aided characterization (CADx) in a real time analysis of colonoscopy videos. Some of the proposed technologies are considered to improve the important clinical outcomes of patients such as adenoma detection rate in colonoscopy. Regulatory approval is considered mandatory before introducing AI tools into the market owing to the potential risks associated with the introduction of AI tools in healthcare. We share the experience of obtaining regulatory approval for EndoBRAIN in Japan, emphasizing the challenges in establishing examination criteria and performance levels at the period. Reimbursement is also identified as necessary for the widespread adoption of medical innovation. With the introduction of reimbursement for a CADe tool in Japan in 2024, we expect to accelerate implementation of AI in colonoscopy in general. Despite regulatory approval and reimbursement, concerns are raised with regard to the assessment of the balance between benefits and harms of AI in colonoscopy. Questions about its impact on cancer prevention, healthcare burden, patient acceptance, and effectiveness across different populations remain unsolved. The lack of clinical guidelines for AI in colonoscopy emphasizes the need for a rigorous assessment of available evidence in optimizing the adoption of AI in colonoscopy practice. While it is always exciting to strive for medical innovation, ensuring rigorous evaluation to optimize patient care is mandatory to improve the quality of health and society.</p>","PeriodicalId":73550,"journal":{"name":"JMA journal","volume":"8 1","pages":"60-63"},"PeriodicalIF":1.5000,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11799701/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"JMA journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31662/jmaj.2024-0133","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/8/30 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
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Abstract
This review outlines the implementation of artificial intelligence (AI) into colonoscopy procedures which includes its history, processes, and challenges. We highlight the importance of the collaborative effort between medical and computer science researchers in the development of AI tools in colonoscopy, particularly focusing on the roles of computer-aided detection (CADe) and computer-aided characterization (CADx) in a real time analysis of colonoscopy videos. Some of the proposed technologies are considered to improve the important clinical outcomes of patients such as adenoma detection rate in colonoscopy. Regulatory approval is considered mandatory before introducing AI tools into the market owing to the potential risks associated with the introduction of AI tools in healthcare. We share the experience of obtaining regulatory approval for EndoBRAIN in Japan, emphasizing the challenges in establishing examination criteria and performance levels at the period. Reimbursement is also identified as necessary for the widespread adoption of medical innovation. With the introduction of reimbursement for a CADe tool in Japan in 2024, we expect to accelerate implementation of AI in colonoscopy in general. Despite regulatory approval and reimbursement, concerns are raised with regard to the assessment of the balance between benefits and harms of AI in colonoscopy. Questions about its impact on cancer prevention, healthcare burden, patient acceptance, and effectiveness across different populations remain unsolved. The lack of clinical guidelines for AI in colonoscopy emphasizes the need for a rigorous assessment of available evidence in optimizing the adoption of AI in colonoscopy practice. While it is always exciting to strive for medical innovation, ensuring rigorous evaluation to optimize patient care is mandatory to improve the quality of health and society.