DOI QR코드

DOI QR Code

Strengths and Weaknesses of Korean Medicine as Syndrome-centered Medicine in the Era of Artificial Intelligence

AI시대 증후중심 치료의학으로서 한의학의 장단점

  • Received : 2024.11.26
  • Accepted : 2024.12.24
  • Published : 2024.12.31

Abstract

Objectives : Applied Artificial Intelligence (AI) is increasingly utilized in medicine, while traditional medicines, such as Korean Medicine (KM), persist within medical systems. A feature of KM is its diagnostic approach, known as syndrome-centered medicine. Despite its widespread use, it was challenging to utilize AI with KM. Therefore, this study aims to compare strengths and weaknesses of KM in the era of AI. Results : KM faces challenges in data utilization due to the reliance on syndrome-centered medicine, the restoration of data through patient inquiries, and the uncertainty surrounding syndromes. Despite these challenges, syndrome-centered medicine offers strengths. It provides immediacy, simultaneity, and safety for patients and cost-effectiveness, physician-led initiatives, patient satisfaction, and rapid responsiveness for doctors. However, there are significant weaknesses. For patients, it may be inefficient for severe uncertain indicates, and limited data access. Academically, KM's foundation in traditional theories complicates the pursuit of quantitative research. This hinders objectivity, standardization, and certainty, undermining confidence in its efficacy. Socially, it has isolated KM, creating educational difficulties and worsening its credibility and the relationship. diseases, ambiguous, and harmful due to reliance on patient initiative. For doctors, it involves risks as incorrectness, overdependence on patients, increased workload, ambiguity, inter-professional conflicts, Conclusion : This study highlights the strengths and weaknesses of syndrome-centered medicine in KM. Henceforth, data-driven approaches and transitioning to disease-centered medicine are indispensable. A paradigm shift could facilitate data collection, publish evidence-based policies, and restore public confidence.

Keywords

References

  1. McCarthy J, Minsky ML, Rochester N, Shannon CE. "A proposal for the dartmouth summer research project on artificial intelligence.". 1955. http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html. (Retrieved on Nov. 22, 2024).
  2. Gartner. Artificial Intelligence. http://blogs.gartner.com/it-glossary/artificial-intelligence/. (Retrieved on Nov. 22, 2024).
  3. 장보형. 한의학 CDSS 구축을 위한 데이터 수집용 스마트 EMR 시스템 개발. 경희대학교. 2021. 40.
  4. Shoja MM, Van de Ridder JMM, Rajput V. The Emerging Role of Generative Artificial Intelligence in Medical Education, Research, and Practice. Cureus. 2023;15:e40883. doi: 10.7759/cureus.40883.
  5. World Health Organization. WHO traditional medicine strategy: 2014-2023. Geneva: Switzerland. 2013. 76.
  6. 이봉교, 박영배, 김태희. 한방진단학-Ⅱ.변증. 2004:41.
  7. 김기왕. 인체질환의 인식과 해석(Ⅱ): 증의 개념. 고등교육 교수학습자료 공동활용 체제. 2014.
  8. 장재순, 조승모, 김기왕. 현직 한의사의 한의진단 현황 조사 보고. 대한한의진단학회지. 2013 17(2):156-168.
  9. 여민경, 박기현, 장은수, 이영섭. 전국 한방의료기관 한의사 대상 한의 변증활용 현황 조사. 대한예방한의학회지. 2015;19(3):45-55.
  10. 손미주, 김우영, 정의민, 박황진, 한창현. 한의사들이 원하는 한의학 지식체계에 대한 고찰. 대한예방한의학회지. 2012;16(3):89-105
  11. Lee JH, Lee SH, Kim JH, Park YS, Park SG, Chang GT. Survey of Clinical Practice Patterns of Korean Medicine Doctors for Anorexia in Children: A Preliminary Study for Clinical Practice Guidelines. Children (Basel). 2022;9(9):1409. https://doi.org/10.3390/children9091409
  12. Ko MM, Lee JA, Yun KJ, You SS, Lee MS. Perception of pattern identification in traditional medicine: a survey of Korean medical practitioners. Journal of Traditional Chinese Medicine. 2014;34(3):369-372. https://doi.org/10.1016/S0254-6272(14)60104-7
  13. 김경묵. 한의학 분야에서의 인공지능기술 연구 동향. 차세대융합기술학회논문지. 2024;8(6):1451-1458.
  14. 김준동, 이혜윤, 김지환, 김창업. Development of an LLM-based CPX Practicing Chatbot for Korean Medicine Education: Implementation of Automated Scoring and Feedback Generation Framework. 대한한의학회지. 2024;45(4):215-230. doi: 10.13048/jkm.24067.
  15. 양지혁, 우정아, 신동하, 박수호, 권영규. 진단전문가시스템을 이용한 한의 실습의 설문 조사를 통한 AI에 대한 인식 및 활용방안 고찰. 동의생리병리학회지. 2021;35(1):22-27.
  16. National Center for Biotechnology Information. National Library of Medicine. https://pubmed.ncbi.nlm.nih.gov/ (Retrieved on Nov. 22, 2024)
  17. 김기현. 변증논치의 허와 실. 제3의학. 1996;1(1):19-29.
  18. 이선동, 한유창, 권보인, 윤해창. 辨證 관련 연구 최신 동향 -2015~2023년 국내 연구를 중심으로-. 대한예방한의학회지. 2024;28(2):19-29. https://doi.org/10.25153/SPKOM.2024.28.2.002
  19. 안형식. 근거중심의학의 개요와 접근방법. 한국의료QA학회지. 2006;23(2):9-16.
  20. 정영호, 박하영, 권순만, 이견직, 고숙자. 보건의료시장의 특성과 문제점 및 제도개선 방향. 한국보건사회연구원. 2004:300.
  21. Russo M, Scarpa B. Learning in Medicine: The Importance of Statistical Thinking. Methods Mol Biol. 2022;2486:215-232. doi: 10.1007/978-1-0716-2265-0_11.
  22. Ahmed I, Ishtiaq S. Reliability and validity: Importance in Medical Research. J Pak Med Assoc. 2021 Oct;71(10):2401-2406. doi: 10.47391/JPMA.06-861.
  23. 김완희. 한의학원론. 성보사. 2001:420.
  24. Cosio D, Lin E. Traditional Chinese Medicine & Acupuncture. Pract Pain Manag. 2015;15(7).
  25. 김남권. 안면신경마비 후유증에 대한 임상적 고찰. 대한한의학회지. 2002;23(1):100-111
  26. Huang Z, Shi X, Nicholas S, Maitland E, Yang Y, Zhao W, Ma Y, Jiang Y. Use of Traditional Chinese Medicine and Its Impact on Medical Cost among Urban Ischemic Stroke Inpatients in China: A National Cross-Sectional Study. Evid Based Complement Alternat Med. 2021 Oct 29;2021:8554829. doi: 10.1155/2021/8554829.
  27. Tang MB, Kuo WY, Kung PT, Tsai WC. The survival and cost-effectiveness analysis of adjunctive Chinese medicine therapy for patients with non-small cell lung cancer: a nationwide cohort study in Taiwan. Front. Pharmacol. 2024;15:1378483. doi: https://doi.org/10.3389/fphar.2024.1378483.
  28. Taylor P, Pezzullo L, Grant SJ, Bensoussan A. Cost-effectiveness of Acupuncture for Chronic Nonspecific Low Back Pain. Pain Pract. 2014;14(7):599-606. doi: 10.1111/papr.12116.
  29. Won J, Lee JH, Bang H, Lee H. Safety of acupuncture by Korean Medicine Doctors: a prospective, practice-based survey of 37,490 consultations. BMC Complement Med Ther. 2022 Nov 18;22(1):300. doi: 10.1186/s12906-022-03782-z.
  30. Kim TH, Kim KH, Kang JW, Lee M, Kang KW, Kim JE, Kim JH, Lee S, Shin MS, Jung SY, Kim AR, Park HJ, Jung HJ, Song HS, Kim HJ, Choi JB, Hong KE, Choi SM. Moxibustion treatment for knee osteoarthritis: a multi-centre, non-blinded, randomised controlled trial on the effectiveness and safety of the moxibustion treatment versus usual care in knee osteoarthritis patients. PLoS One. 2014 Jul 25;9(7):e101973. doi: 10.1371/journal.pone.0101973.
  31. 양준모, 성동민, 김유진, 이선동. 장기간 한약복용이 부인과 환자의 간 및 신장기능에 미치는 영향: panel 연구. 대한한의학회지. 2020;41(1):84-92.
  32. 박요한, 강병갑, 신현규. 한의원의 경영 현황과 정책 인식도에 대한 조사 연구. 대한한의진단학회. 2014;18(3):205-215.
  33. Shmanske S. Information Asymmetries in Health Services: The Market Can Cope. The Independent Review. 1996;1(2):191–200. http://www.jstor.org/stable/24560940.
  34. Rosén A. Placebo effects in health and disease: how expectations shape treatment outcomes. Karolinska Institutet. 2016:48.
  35. Stetler C. Adherence, expectations and the placebo response: why is good adherence to an inert treatment beneficial?. Psychol Health. 2014;29(2):127-40. doi: 10.1080/08870446.2013.830721.
  36. 윤강재, 최지희, 조병희. 보건의료서비스 분야 소비자 위상과 권리. 한국보건사회연구원. 2013:220.
  37. 박윤성, 김형수, 김진숙. 질병별 의과와 한방 의료기관 이용 현황 분석 연구. 의료정책연구소. 2017:66.
  38. 기유종, 신선중, 한원영, 김현도, 한유창, 이선동. 辨證論治와 病證辨治 장단점의 비교고찰 -고혈압과 두통의 예를 중심으로-. 대한예방한의학회지. 2017;21(1):9-17 https://doi.org/10.25153/SPKOM.2017.21.1.002
  39. 전국한의과대학 심계내과학교실. 한방순환신경내과학. 군자출판사. 2010:590.
  40. 두호경. 동양의학은 어떤 학문인가?. 교학사. 2003:546.
  41. 이평수. 의사와 한의사의 업무갈등 사례분석 및 대응방안 연구. 의료정책포럼. 2012;10(1):119-123.
  42. Chen J. Review and Prospect of the Study of Traditional Chinese Medicine "Pattern". J Beijing University of Traditional Chinese Med. 1998;40-43.
  43. 한예진. 한의대생의 한의학 신뢰도에 대한 영향 요인 연구. 한국콘텐츠학회논문지. 2024;24(7):513-519.
  44. 아서 클라인먼. 우리의 아픔엔 서사가 있다. 사이북. 2022;476.