إعطاء الأولوية لتعاون الإنسان والذكاء الاصطناعي في الرعاية الصحية: إطار عمل TRIAD للحوكمة الموثوقة، والقيمة الحقيقية، والنشر التكيفي المتكامل

  • role: First author第一作者
  • Affiliation:

    Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China

Jia Li1,  
  • Affiliation:

    Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China

Zi-Chun Zhou1,  
  • role: Corresponding author通信作者
  • Affiliation:

    Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China

    Department of Health Data Science, School of Medical Technology, Capital Medical University, Beijing 100050, China

    Precision and Intelligence Medical Imaging Lab, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China

  • Email:cjr.wzhch@vip.163.com
  • Introduction:* Zhen-Chang Wang cjr.wzhch@vip.163.com
Zhen-Chang Wang123*,  
  • role: Corresponding author通信作者
  • Affiliation:

    Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China

    Department of Health Data Science, School of Medical Technology, Capital Medical University, Beijing 100050, China

    Precision and Intelligence Medical Imaging Lab, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China

  • Email:chrislvhan@126.com
  • Introduction:Han Lv chrislvhan@126.com
Han Lv123*

ملخص

الذكاء الاصطناعي (AI) والبيانات الضخمة يعيدان تشكيل مشهد الرعاية الصحية. ومع ذلك، تعتمد القيمة السريرية على مدى جودة دعم الأنظمة للأطباء وملاءمتها لسير العمل الروتيني. لهذا الغرض، نقدم إطار TRIAD: الحوكمة الموثوقة، القيمة السريرية في العالم الحقيقي، والنشر التكيفي المتكامل، لتوجيه تطوير، تحقق، ونشر الذكاء الاصطناعي السريري. يتطلب TRIAD إثبات مصدر البيانات المقصود، وتدقيق الإنصاف، وعدم اليقين المعاير. يقيم هذا الإطار فريق الإنسان والذكاء الاصطناعي في سير العمل الحقيقي باستخدام مقاييس على مستوى الفريق، تشمل الدقة، السلامة، عبء العمل، وأنماط القبول، التحرير، والتجاوز. يتم النشر عبر مراحل مع حراس مسجلين مسبقاً ورصد مستمر للأداء وتأثير المجموعة الفرعية. يرى TRIAD الذكاء كخاصية لفريق الإنسان والذكاء الاصطناعي وليس للنموذج الذاتي فقط. إن مواءمة الحوكمة، التقييم، والنشر حول الأطباء والمرضى يمكن من تحقيق مكاسب دائمة في السلامة، العدالة، الكفاءة، والتجربة، مما يرفع القيمة السريرية.

مفهوم

TRIAD;الذكاء الاصطناعي (AI);تعاون الإنسان-الذكاء الاصطناعي;آليات الثقة;دعم اتخاذ القرار السريري

References

  1. 1..
    Li J, Zhou Z, Lyu H, Wang Z. Large language models-powered clinical decision support: enhancing or replacing human expertise?. Intell Med. 2025;5(1):1–4.
  2. 2..
    Sun J, Zeng N, Hui Y, Li J, Liu W, Zhao X, et al. Association of variability in body size with neuroimaging metrics of brain health: a population-based cohort study. Lancet Reg Health. 2024;44:101015.
  3. 3..
    Li C, Liu M, Xia J, Mei L, Yang Q, Shi F, et al. Individualized assessment of brain Aβ deposition with fMRI using deep learning. IEEE J Biomed Health Inform. 2023;27(11):5430–8.
  4. 4..
    Song J, Wang GC, Wang SC, He CR, Zhang YZ, Chen X, et al. Artificial intel-ligence in orthopedics: fundamentals, current applications, and future perspectives. Mil Med Res. 2025;12(1):42.
  5. 5..
    Jiang YL, Zhao G, Wang SH, Li N. Leveraging artificial intelligence for clini-cal decision support in personalized standard regimen recommendation for cancer. Mil Med Res. 2025;12(1):31.
  6. 6..
    Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatolo-gist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115–8.
  7. 7..
    Bhattacharya I, Khandwala YS, Vesal S, Shao W, Yang Q, Soerensen SJC,et al. A review of artificial intelligence in prostate cancer detection on imaging. Ther Adv Urol. 2022;14:17562872221128792.
  8. 8..
    Dai L, Wu L, Li H, Cai C, Wu Q, Kong H, et al. A deep learning system for detecting diabetic retinopathy across the disease spectrum. Nat Commun. 2021;12(1):3242.
  9. 9..
    Hua D, Petrina N, Sacks AJ, Young N, Cho JG, Smith R, et al. Towards human–AI collaboration in radiology: a multidimensional evaluation of the acceptability of AI for chest radiograph analysis in supporting pulmo-nary tuberculosis diagnosis. JAMIA Open. 2025;8(1): ooae151.
  10. 10..
    Van Booven DJ, Chen CB, Kryvenko ON, Punnen S, Sandoval V, Malpani S,et al. Mitigating bias in prostate cancer diagnosis using synthetic data for improved AI-driven Gleason grading. NPJ Precis Oncol. 2025;9(1):151.
  11. 11..
    Johnson LS, Zadrozniak P, Jasina G, Grotek-Cuprjak A, Andrade JG, Svennberg E, et al. Artificial intelligence for direct-to-physician reporting of ambulatory electrocardiography. Nat Med. 2025;31(3):925–31.
  12. 12..
    Sun J, Wang L, Gao Y, Hui Y, Chen S, Wu S, et al. Discovery of high-risk clinical factors that accelerate brain aging in adults: a population-based machine learning study. Research. 2024;7:0500.
  13. 13..
    Wang Z, Sun J, Liu H, Luo X, Li J, He W, et al. Development and perfor-mance of a large language model for the quality evaluation of multilanguage medical imaging guidelines and consensus. J Evid Based Med. 2025;18(2): e70020.
  14. 14..
    Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56.
  15. 15..
    He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical implemen-tation of artificial intelligence technologies in medicine. Nat Med. 2019;25(1):30–6.
  16. 16..
    Hong EK, Roh B, Park B, Jo JB, Bae W, Park JS, et al. Value of using a genera-tive AI model in chest radiography reporting: a reader study. Radiology. 2025;314(3): e241646.
  17. 17..
    Tanno R, Barrett DGT, Sellergren A, Ledsam JR, Batten B, Läänelaid P, et al. Collaboration between clinicians and vision-language models in radiol-ogy report generation. Nat Med. 2025;31(2):599–608.
  18. 18..
    Ziegelmayer S, Marka AW, Lenhart N, Nehls N, Reischl S, Harder F, et al. Evaluation of GPT-4 for chest X-ray impression generation: a reader study on performance and perception. J Med Internet Res. 2023;25: e50865.
  19. 19..
    Dembrower K, Crippa A, Colón E, Eklund M, Strand F. Artificial intelligence for breast cancer detection in screening mammography in Sweden: a prospective, population-based, paired-reader, non-inferiority study. Lancet Digit Health. 2023;5(10): e703–11.
  20. 20..
    Yacoub B, Varga-Szemes A, Schoepf UJ, Lee D, Halabi SS, Luetkens JA,et al. Impact of artificial intelligence assistance on chest CT interpreta-tion times: a prospective randomized study. AJR Am J Roentgenol. 2022;219(5):743–51.
  21. 21..
    Nam JG, Hwang EJ, Kim J, Lee JH, Park CM, Goo JM, et al. AI improves nodule detection on chest radiographs in a health screening population: a randomized controlled trial. Radiology. 2023;307(2): e221894.
  22. 22..
    Zöller N, Berger J, Lin I, Fu N, Komarneni J, Barabucci G, et al. Human-AI collectives most accurately diagnose clinical vignettes. Proc Natl Acad Sci U S A. 2025;122(24): e2426153122.
  23. 23..
    Barabucci G, Shia V, Chu E, Harack B, Laskowski K, Fu N. Combining multiple large language models improves diagnostic accuracy. NEJM AI. 2024;1(11): AIcs2400502.
  24. 24..
    Lu MY, Chen B, Williamson DFK, Jia G, Chen RJ, Mahmood F, et al. A multimodal generative AI copilot for human pathology. Nature. 2024;634(8033):466–73.
  25. 25..
    Rizzo PC, Caputo A, Maddalena E, Dell’Aquila M, Ciaccio A, Fiori M, et al. Digital pathology world tour. Digit. Health. 2023;9:20552076231194550.
  26. 26..
    Ahn JS, Ebrahimian S, McDermott S, Bui AT, Cao M, Durack JC, et al. Association of artificial intelligence-aided chest radiograph interpre-tation with reader performance and efficiency. JAMA Netw Open. 2022;5(8): e2229289.
  27. 27..
    Bond RR, Novotny T, Andrsova I, Koc L, Sisakova M, Finlay DD, et al. Automation bias in medicine: the influence of automated diagnoses on interpreter accuracy and uncertainty when reading electrocardiograms. J Electrocardiol. 2018;51(6 Suppl): S6–11.
  28. 28..
    Dembrower KE, Crippa A, Eklund M, Strand F. Human–AI interaction in the ScreenTrustCAD trial: recall proportion and positive predictive value related to screening mammograms flagged by AI CAD versus a human reader. Radiology. 2025;314(3): e242566.
  29. 29..
    Plesner LL, Müller FC, Brejnebøl MW, Krag CH, Laustrup LC, Rasmussen F,et al. Using AI to identify unremarkable chest radiographs for automatic reporting. Radiology. 2024;312(2): e240272.
  30. 30..
    Poursabzi-Sangdeh F, Goldstein DG, Hofman JM, Vaughan JW, Wal-lach H. Manipulating and measuring model interpretability. In: CHI’21: proceedings of the 2021 CHI conference on human factors in computing systems. 2021:1–52. .
  31. 31..
    Cabitza F, Rasoini R, Gensini GF. Unintended consequences of machine learning in medicine. JAMA. 2017;318(6):517–8.
  32. 32..
    Groh M. Physician-machine partnerships boost diagnostic accuracy, but bias persists. Nat Med. 2024;30(2):356–7.
  33. 33..
    Olson KD, Meeker D, Troup M, Barker A, Nguyen K, Manders C, et al. Use of ambient AI scribes to reduce administrative burden and professional burnout. JAMA Netw Open. 2025;8(10): e2534976.
  34. 34..
    Han SS, Moon IJ, Kim SH, Myeong N, Choi DH, Kim W, et al. Assessment of deep neural networks for the diagnosis of benign and malignant skin neoplasms in comparison with dermatologists: a retrospective validation study. PLoS Med. 2020;17(11): e1003381.
  35. 35..
    Jabbour S, Fouhey D, Shepard S, Valley TS, Kazerooni EA, Banovic N, et al. Measuring the impact of AI in the diagnosis of hospitalized patients: a randomized clinical vignette survey study. JAMA. 2023;330(23):2275–84.
  36. 36..
    Parasuraman R, Riley V. Humans and automation: use, misuse, disuse, and abuse. Hum Factors. 1997;39(2):230–53.
  37. 37..
    Ghassemi M, Oakden-Rayner L, Beam AL. The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit Health. 2021;3(11): e745–50.
  38. 38..
    Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366(6464):447–53.
  39. 39..
    U.S. Department of Health and Human Services. Standards for privacy of individually identifiable health information; final rule. Fed Regist. 2000;65:82462–829.
  40. 40..
    European Parliament & Council of the European Union. Regulation (EU)2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation). Off J Eur Union. 2016;L119:1–88.
  41. 41..
    Rajkomar A, Hardt M, Howell MD, Corrado G, Chin MH. Ensuring fair-ness in machine learning to advance health equity. Ann Intern Med. 2018;169(12):866–72.
  42. 42..
    Tabassi E. Artificial intelligence risk management framework (AI RMF 1.0). NIST trustworthy and responsible AI. Gaithersburg: National Institute of Standards and Technology; 2023. .
  43. 43..
    The U. S. Food and Drug Administration (FDA), Health Canada, the United Kingdom’s Medicines and Healthcare products Regulatory Agency(MHRA). Good machine learning practice for medical device develop-ment: guiding principles. 2021. .
  44. 44..
    Liu X, Cruz Rivera S, Moher D, Calvert MJ, Denniston AK. CONSORT-AI extension. Nat Med. 2020;26(9):1364–74.
  45. 45..
    Collins GS, Moons KGM, Dhiman P, Ma J, Hooft L, Riley RD, et al. TRIPOD +AI statement. BMJ. 2024;385: e078378.
  46. 46..
    Rivera SC, Liu X, Chan AW, Denniston AK, Calvert MJ. SPIRIT-AI extension. BMJ. 2020;370: m3210.
  47. 47..
    International Organization for Standardization. ISO 14971:2019 medical devices—application of risk management to medical devices. 2019..
  48. 48..
    International Electrotechnical Commission. IEC 62304:2006 medical device software—software life cycle processes. 2006. .
  49. 49..
    Molina CA, Theodore N, Ahmed AK, Westbroek EM, Park CW, Chang JH,et al. Augmented reality-assisted pedicle screw insertion: a cadaveric proof-of-concept study. J Neurosurg Spine. 2019;31(1):139–46.
  50. 50..
    Willett FR, Kunz EM, Fan C, Wright J, Avansino DT, Bandyopad-hyay S, et al. A high-performance speech neuroprosthesis. Nature. 2023;620(7976):1031–6.
  51. 51..
    Metzger SL, Littlejohn KT, Silva AB, Moses DA, Seaton MP, Wang R, et al. A high-performance neuroprosthesis for speech decoding and avatar control. Nature. 2023;620(7976):1037–46.

قراءة النص الكامل

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website