BENEFITS AND CHALLENGES IN IMPLEMENTATION OF ARTIFICIAL INTELLIGENCE IN COLONOSCOPY: WORLD ENDOSCOPY ORGANIZATION POSITION STATEMENT
Mori Y., East JE., Hassan C., Halvorsen N., Berzin TM., Byrne M., Renteln DV., Hewett DG., Repici A., Ramchandani M., Khatry MA., Kudo SE., Wang P., Yu H., Saito Y., Misawa M., Parasa S., Matsubayashi CO., Ogata H., Tajiri H., Pausawasdi N., Dekker E., Ahmad OF., Sharma P., Rex DK.
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 costeffectiveness 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 costeffectiveness research should be undertaken to understand whether AI implementation benefits populations and societies in different health-care systems.