REFERENCES

1. Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74:229-63.

2. Bretthauer M, Løberg M, Wieszczy P, et al; NordICC Study Group. Effect of colonoscopy screening on risks of colorectal cancer and related death. N Engl J Med. 2022;387:1547-56.

3. Brenner H, Heisser T, Cardoso R, Hoffmeister M. Reduction in colorectal cancer incidence by screening endoscopy. Nat Rev Gastroenterol Hepatol. 2024;21:125-33.

4. Zheng S, Schrijvers JJA, Greuter MJW, Kats-Ugurlu G, Lu W, de Bock GH. Effectiveness of colorectal cancer (CRC) screening on all-cause and CRC-specific mortality reduction: a systematic review and meta-analysis. Cancers. 2023;15:1948.

5. Click B, Pinsky PF, Hickey T, Doroudi M, Schoen RE. Association of colonoscopy adenoma findings with long-term colorectal cancer incidence. JAMA. 2018;319:2021-31.

6. Hao Y, Wang Y, Qi M, He X, Zhu Y, Hong J. Risk factors for recurrent colorectal polyps. Gut Liver. 2020;14:399-411.

7. Hassan C, Antonelli G, Dumonceau JM, et al. Post-polypectomy colonoscopy surveillance: European Society of Gastrointestinal Endoscopy (ESGE) Guideline - update 2020. Endoscopy. 2020;52:687-700.

8. Issaka RB, Chan AT, Gupta S. AGA clinical practice update on risk stratification for colorectal cancer screening and post-polypectomy surveillance: expert review. Gastroenterology. 2023;165:1280-91.

9. Zauber AG, Winawer SJ, O’Brien MJ, et al. Colonoscopic polypectomy and long-term prevention of colorectal-cancer deaths. N Engl J Med. 2012;366:687-96.

10. Corley DA, Jensen CD, Marks AR, et al. Adenoma detection rate and risk of colorectal cancer and death. N Engl J Med. 2014;370:2539-41.

11. Djinbachian R, Dubé AJ, Durand M, et al. Adherence to post-polypectomy surveillance guidelines: a systematic review and meta-analysis. Endoscopy. 2019;51:673-83.

12. Krist AH, Jones RM, Woolf SH, et al. Timing of repeat colonoscopy: disparity between guidelines and endoscopists’ recommendation. Am J Prev Med. 2007;33:471-8.

13. Kruse GR, Khan SM, Zaslavsky AM, Ayanian JZ, Sequist TD. Overuse of colonoscopy for colorectal cancer screening and surveillance. J Gen Intern Med. 2015;30:277-83.

14. Fraiman J, Brownlee S, Stoto MA, Lin KW, Huffstetler AN. An estimate of the US rate of overuse of screening colonoscopy: a systematic review. J Gen Intern Med. 2022;37:1754-62.

15. Murphy CC, Sandler RS, Grubber JM, Johnson MR, Fisher DA. Underuse and overuse of colonoscopy for repeat screening and surveillance in the Veterans Health Administration. Clin Gastroenterol Hepatol. 2016;14:436-444.e1.

16. Saini SD, Powell AA, Dominitz JA, et al. Developing and testing an electronic measure of screening colonoscopy overuse in a large integrated healthcare system. J Gen Intern Med. 2016;31 Suppl 1:53-60.

17. Mittal S, Lin YL, Tan A, Kuo YF, El-Serag HB, Goodwin JS. Limited life expectancy among a subgroup of medicare beneficiaries receiving screening colonoscopies. Clin Gastroenterol Hepatol. 2014;12:443-450.e1.

18. Kim SY, Kim HS, Park HJ. Adverse events related to colonoscopy: global trends and future challenges. World J Gastroenterol. 2019;25:190-204.

19. Vader JP, Pache I, Froehlich F, et al. Overuse and underuse of colonoscopy in a European primary care setting. Gastrointest Endosc. 2000;52:593-99.

20. Aziz M, Fatima R, Dong C, Lee-Smith W, Nawras A. The impact of deep convolutional neural network-based artificial intelligence on colonoscopy outcomes: a systematic review with meta-analysis. J Gastroenterol Hepatol. 2020;35:1676-83.

21. Huang D, Shen J, Hong J, et al. Effect of artificial intelligence-aided colonoscopy for adenoma and polyp detection: a meta-analysis of randomized clinical trials. Int J Colorectal Dis. 2022;37:495-506.

22. Barua I, Vinsard DG, Jodal HC, et al. Artificial intelligence for polyp detection during colonoscopy: a systematic review and meta-analysis. Endoscopy. 2021;53:277-84.

23. Taghiakbari M, Mori Y, von Renteln D. Artificial intelligence-assisted colonoscopy: a review of current state of practice and research. World J Gastroenterol. 2021;27:8103-22.

24. Mitsala A, Tsalikidis C, Pitiakoudis M, Simopoulos C, Tsaroucha AK. Artificial intelligence in colorectal cancer screening, diagnosis and treatment. A new era. Curr Oncol. 2021;28:1581-607.

25. Tricco AC, Lillie E, Zarin W, et al. PRISMA extension for Scoping Reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018;169:467-73.

26. Arksey H, O’malley L. Scoping studies: towards a methodological framework. Int J Soc Res Method. 2005;8:19-32.

27. Peters MD, Godfrey CM, Khalil H, McInerney P, Parker D, Soares CB. Guidance for conducting systematic scoping reviews. Int J Evid Based Healthc. 2015;13:141-6.

28. Imler TD, Morea J, Imperiale TF. Clinical decision support with natural language processing facilitates determination of colonoscopy surveillance intervals. Clin Gastroenterol Hepatol. 2014;12:1130-6.

29. Karwa A, Patell R, Parthasarathy G, Lopez R, McMichael J, Burke CA. Development of an automated algorithm to generate guideline-based recommendations for follow-up colonoscopy. Clin Gastroenterol Hepatol. 2020;18:2038-2045.e1.

30. Peterson E, May FP, Kachikian O, et al. Automated identification and assignment of colonoscopy surveillance recommendations for individuals with colorectal polyps. Gastrointest Endosc. 2021;94:978-87.

31. Bae JH, Han HW, Yang SY, et al. Natural language processing for assessing quality indicators in free-text colonoscopy and pathology reports: development and usability study. JMIR Med Inform. 2022;10:e35257.

32. Wu L, Shi C, Li J, et al. Development and evaluation of a surveillance system for follow-up after colorectal polypectomy. JAMA Netw Open. 2023;6:e2334822.

33. Lim DYZ, Tan YB, Koh JTE, et al. ChatGPT on guidelines: providing contextual knowledge to GPT allows it to provide advice on appropriate colonoscopy intervals. J Gastroenterol Hepatol. 2024;39:81-106.

34. Chang PW, Amini MM, Davis RO, et al. ChatGPT4 outperforms endoscopists for determination of postcolonoscopy rescreening and surveillance recommendations. Clin Gastroenterol Hepatol. 2024;22:1917-1925.e17.

35. Lieberman DA, Rex DK, Winawer SJ, Giardiello FM, Johnson DA, Levin TR. Guidelines for colonoscopy surveillance after screening and polypectomy: a consensus update by the US Multi-Society Task Force on Colorectal Cancer. Gastroenterology. 2012;143:844-57.

36. Gupta S, Lieberman D, Anderson JC, et al. Recommendations for follow-up after colonoscopy and polypectomy: a consensus update by the US Multi-Society Task Force on Colorectal Cancer. Am J Gastroenterol. 2020;115:415-34.

37. Rutter MD, East J, Rees CJ, et al. British Society of Gastroenterology/Association of Coloproctology of Great Britain and Ireland/Public Health England post-polypectomy and post-colorectal cancer resection surveillance guidelines. Gut. 2020;69:201-23.

38. Zhao S, Wang S, Pan P, et al. Expert consensus on management strategies for precancerous lesions and conditions of colorectal cancer in China. Chin J Dig Endosc. 2022;39:1-18.

39. Saito Y, Oka S, Kawamura T, et al. Colonoscopy screening and surveillance guidelines. Dig Endosc. 2021;33:486-519.

40. Chen X, Xie H, Wang FL, Liu Z, Xu J, Hao T. A bibliometric analysis of natural language processing in medical research. BMC Med Inform Decis Mak. 2018;18:14.

41. Hao T, Rusanov A, Boland MR, Weng C. Clustering clinical trials with similar eligibility criteria features. J Biomed Inform. 2014;52:112-20.

42. Velupillai S, Mowery D, South BR, Kvist M, Dalianis H. Recent advances in clinical natural language processing in support of semantic analysis. Yearb Med Inform. 2015;10:183-93.

43. Shen F, Liu S, Fu S, et al. Family history extraction from synthetic clinical narratives using natural language processing: overview and evaluation of a challenge data set and solutions for the 2019 National NLP Clinical Challenges (n2c2)/Open Health Natural Language Processing (OHNLP) competition. JMIR Med Inform. 2021;9:e24008.

44. Kalyan KS, Sangeetha S. SECNLP: a survey of embeddings in clinical natural language processing. J Biomed Inform. 2020;101:103323.

45. Rembacken B, Hassan C, Riemann JF, et al. Quality in screening colonoscopy: position statement of the European Society of Gastrointestinal Endoscopy (ESGE). Endoscopy. 2012;44:957-68.

46. Imler TD, Morea J, Kahi C, Imperiale TF. Natural language processing accurately categorizes findings from colonoscopy and pathology reports. Clin Gastroenterol Hepatol. 2013;11:689-94.

47. Raju GS, Lum PJ, Slack RS, et al. Natural language processing as an alternative to manual reporting of colonoscopy quality metrics. Gastrointest Endosc. 2015;82:512-9.

48. IJspeert JE, Bastiaansen BA, van Leerdam ME, et al; Dutch Workgroup serrAted polypS & Polyposis (WASP). Development and validation of the WASP classification system for optical diagnosis of adenomas, hyperplastic polyps and sessile serrated adenomas/polyps. Gut. 2016;65:963-70.

49. Goodwin JS, Singh A, Reddy N, Riall TS, Kuo YF. Overuse of screening colonoscopy in the Medicare population. Arch Intern Med. 2011;171:1335-43.

50. Brownlee S, Huffstetler AN, Fraiman J, Lin KW. An estimate of preventable harms associated with screening colonoscopy overuse in the U.S. AJPM Focus. 2025;4:100296.

51. Sabrie N, Khan R, Jogendran R, et al. Performance of natural language processing in identifying adenomas from colonoscopy reports: a systematic review and meta-analysis. iGIE. 2023;2:350-356.e7.

52. Harkema H, Chapman WW, Saul M, Dellon ES, Schoen RE, Mehrotra A. Developing a natural language processing application for measuring the quality of colonoscopy procedures. J Am Med Inform Assoc. 2011;18 Suppl 1:i150-6.

53. Gawron AJ, Thompson WK, Keswani RN, Rasmussen LV, Kho AN. Anatomic and advanced adenoma detection rates as quality metrics determined via natural language processing. Am J Gastroenterol. 2014;109:1844-9.

54. Hossain E, Rana R, Higgins N, et al. Natural language processing in electronic health records in relation to healthcare decision-making: a systematic review. Comput Biol Med. 2023;155:106649.

55. Seong D, Choi YH, Shin SY, Yi BK. Deep learning approach to detection of colonoscopic information from unstructured reports. BMC Med Inform Decis Mak. 2023;23:28.

56. Stidham RW, Yu D, Zhao X, et al. Identifying the presence, activity, and status of extraintestinal manifestations of inflammatory bowel disease using natural language processing of clinical notes. Inflamm Bowel Dis. 2023;29:503-10.

57. Montoto C, Gisbert JP, Guerra I, et al; PREMONITION-CD Study Group. Evaluation of natural language processing for the identification of crohn disease-related variables in Spanish electronic health records: a validation study for the PREMONITION-CD project. JMIR Med Inform. 2022;10:e30345.

58. Gomollón F, Gisbert JP, Guerra I, et al; Premonition-CD Study Group. Clinical characteristics and prognostic factors for Crohn’s disease relapses using natural language processing and machine learning: a pilot study. Eur J Gastroenterol Hepatol. 2022;34:389-97.

59. OpenAI, Achiam J, Adler S, et al. GPT-4 technical report. arXiv 2024; arXiv:2303.08774. Available from https://doi.org/10.48550/arXiv.2303.08774 [accessed 5 November 2025].

60. Bommasani R, Hudson DA, Adeli E, et al. On the opportunities and risks of foundation models. arXiv 2022; arXiv.2108.07258. Available from https://doi.org/10.48550/arXiv.2108.07258 [accessed 5 November 2025].

61. Ghersin I, Weisshof R, Koifman E, et al. Comparative evaluation of a language model and human specialists in the application of European guidelines for the management of inflammatory bowel diseases and malignancies. Endoscopy. 2024;56:706-9.

62. Lusetti F, Maimaris S, La Rosa GP, et al. Applications of generative artificial intelligence in inflammatory bowel disease: a systematic review. Dig Liver Dis. 2025;57:1883-9.

63. Laoveeravat P, Simonetto DA. AI (artificial intelligence) as an IA (intelligent assistant): ChatGPT for surveillance colonoscopy questions. Gastro Hep Adv. 2023;2:1138-9.

64. Lee TC, Staller K, Botoman V, Pathipati MP, Varma S, Kuo B. ChatGPT answers common patient questions about colonoscopy. Gastroenterology. 2023;165:509-511.e7.

65. Levartovsky A, Ben-Horin S, Kopylov U, Klang E, Barash Y. Towards AI-augmented clinical decision-making: an examination of ChatGPT’s utility in acute ulcerative colitis presentations. Am J Gastroenterol. 2023;118:2283-9.

66. Henson JB, Glissen Brown JR, Lee JP, Patel A, Leiman DA. Evaluation of the potential utility of an artificial intelligence chatbot in gastroesophageal reflux disease management. Am J Gastroenterol. 2023;118:2276-9.

67. Sattler SS, Chetla N, Chen M, et al. Evaluating the diagnostic accuracy of ChatGPT-4 Omni and ChatGPT-4 Turbo in identifying melanoma: comparative study. JMIR Dermatol. 2025;8:e67551.

68. Saraiva MM, Ribeiro T, Agudo B, et al. Evaluating ChatGPT-4 for the interpretation of images from several diagnostic techniques in gastroenterology. J Clin Med. 2025;14:572.

69. Qin Y, Chang J, Li L, Wu M. Enhancing gastroenterology with multimodal learning: the role of large language model chatbots in digestive endoscopy. Front Med. 2025;12:1583514.

70. Jeyaraman M, Balaji S, Jeyaraman N, Yadav S. Unraveling the ethical enigma: artificial intelligence in healthcare. Cureus. 2023;15:e43262.

71. Elendu C, Amaechi DC, Elendu TC, et al. Ethical implications of AI and robotics in healthcare: a review. Medicine. 2023;102:e36671.

72. Chen Y, Esmaeilzadeh P. Generative AI in medical practice: in-depth exploration of privacy and security challenges. J Med Internet Res. 2024;26:e53008.

73. Alowais SA, Alghamdi SS, Alsuhebany N, et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educ. 2023;23:689.

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