Checklist for artificial intelligence in medical imaging (CLAIM) 2024 update: a comparison and interpretation_Chinese Journal of Evidence-Based Medicine

Authors：

ZHONG Jingyu ¹ , XING Yue ¹ , LU Junjie ² , YANG Jiarui ³ , CHU Jingshen ⁴ , SONG Yang ⁵ , HU Yangfan ¹ , DING Defang ¹ , LIU Xianwei ¹ , ZHANG Huan ⁶ ,  YAO Weiwu ¹

1. Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200336, P. R. China;
2. Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford 94305, USA;
3. Department of Biomedical Engineering, Boston University, Boston 02215, USA;
4. Editorial office of Journal of Diagnostics Concepts & Practice, Department of Science and Technology Development, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, P. R. China;
5. MR Research Collaboration, Siemens Healthineers Ltd., Shanghai 200126, P. R. China;
6. Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University of Medicine, Shanghai 200025, P. R. China;

Corresponding author：

YAO Weiwu, Email: yaoweiwuhuan@163.com

Keywords：

Artificial intelligence; Medical imaging; Report quality; Checklist for artificial intelligence in medical imaging (CLAIM)

DOI：

10.7507/1672-2531.202406123

Video：

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The rapid development of medical imaging methods based on artificial intelligence (AI) has led to the first release of the AI medical imaging research checklist (CLAIM) in 2020 to promote the completeness and consistency of AI medical imaging research reports. However, during the application process, it was found that some entries in CLAIM needed improvement. Therefore, the expert committee updated CLAIM and released the updated version of CLAIM 2024. This article introduces CLAIM 2024 for domestic scholars to follow up and refer to in a timely manner.

1.	周俊林, 萧毅, 张雪君, 等. 中国医学影像人工智能应用现状调研报告. 中华放射学杂志, 2022, 56(11): 1248-1253.
2.	刘士远. 医学影像人工智能发展趋势与挑战. 中华放射学杂志, 2021, 55(7): 700-702.
3.	陈冲, 夏黎明. 积极稳妥地推进人工智能在医学影像的应用. 中华放射学杂志, 2022, 56(1): 5-8.
4.	Boverhof BJ, Redekop WK, Bos D, et al. Radiology AI deployment and assessment rubric (RADAR) to bring value-based AI into radiological practice. Insights Imaging, 2024, 15(1): 34.
5.	Langlotz CP. The Future of AI and informatics in radiology: 10 predictions. Radiology, 2023, 309(1): e231114.
6.	Yang L, Ene IC, Arabi Belaghi R, et al. Stakeholders' perspectives on the future of artificial intelligence in radiology: a scoping review. Eur Radiol, 2022, 32(3): 1477-1495.
7.	Rivera SC, Liu X, Chan AW, et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI Extension. BMJ, 2020, 370: m3210.
8.	Liu X, Rivera SC, Moher D, et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension. BMJ, 2020, 370: m3164.
9.	Vasey B, Nagendran M, Campbell B, et al. Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. Nat Med, 2022, 28(5): 924-933.
10.	Collins GS, Moons KGM, Dhiman P, et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ, 2024, 385: e078378.
11.	Sounderajah V, Ashrafian H, Rose S, et al. A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI. Nat Med, 2021, 27(10): 1663-1665.
12.	Sounderajah V, Ashrafian H, Golub RM, et al. Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol. BMJ Open, 2021, 11(6): e047709.
13.	Collins GS, Dhiman P, Andaur Navarro CL, et al. Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open, 2021, 11(7): e048008.
14.	Mongan J, Moy L, Kahn CE. Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiol Artif Intell, 2020, 2(2): e200029.
15.	O'Shea RJ, Sharkey AR, Cook GJR, et al. Systematic review of research design and reporting of imaging studies applying convolutional neural networks for radiological cancer diagnosis. Eur Radiol, 2021, 31(10): 7969-7983.
16.	Si L, Zhong J, Huo J, et al. Deep learning in knee imaging: a systematic review utilizing a Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Eur Radiol, 2022, 32(2): 1353-1361.
17.	Belue MJ, Harmon SA, Lay NS, et al. The low rate of adherence to checklist for artificial intelligence in medical imaging criteria among published prostate MRI artificial intelligence algorithms. J Am Coll Radiol, 2023, 20(2): 134-145.
18.	Zhong J, Hu Y, Zhang G, et al. An updated systematic review of radiomics in osteosarcoma: utilizing CLAIM to adapt the increasing trend of deep learning application in radiomics. Insights Imaging, 2022, 13(1): 138.
19.	Sivanesan U, Wu K, McInnes MDF, et al. Checklist for artificial intelligence in medical imaging reporting adherence in peer-reviewed and preprint manuscripts with the highest altmetric attention scores: a meta-research study. Can Assoc Radiol J, 2023, 74(2): 334-342.
20.	Kocak B, Keles A, Akinci D'Antonoli T. Self-reporting with checklists in artificial intelligence research on medical imaging: a systematic review based on citations of CLAIM. Eur Radiol, 2024, 34(4): 2805-2815.
21.	诸宇佳, 韩慧, 卫建华, 等. 人工智能医学影像研究报告规范: CLAIM检查清单解读. 中国循证医学杂志, 2023, 23(12): 1478-1484.
22.	王亚辉, 徐虹, 李大金. 人工智能医学影像研究报告规范(CLAIM)的解读及应用建议. 出版与印刷, 2023, (6): 89-95.
23.	Tejani AS, Klontzas ME, Gatti AA, et al. Checklist for artificial intelligence in medical imaging (CLAIM): 2024 update. Radiol Artif Intell, 2024, 6(4): e240300.
24.	Bossuyt PM, Reitsma JB, Bruns DE, et al. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. BMJ, 2015, 351: h5527.
25.	Luo W, Phung D, Tran T, et al. Guidelines for developing and reporting machine learning predictive models in biomedical research: a multidisciplinary view. J Med Internet Res, 2016, 18(12): e323.
26.	Handelman GS, Kok HK, Chandra RV, et al. Peering into the black box of artificial intelligence: evaluation metrics of machine learning methods. AJR Am J Roentgenol, 2019, 212(1): 38-43.
27.	Park SH, Han K. Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction. Radiology, 2018, 286(3): 800-809.
28.	Bluemke DA, Moy L, Bredella MA, et al. Assessing radiology research on artificial intelligence: a brief guide for authors, reviewers, and readers-from the radiology editorial board. Radiology, 2020, 294(3): 487-489.
29.	Provenzale JM, Stanley RJ. A systematic guide to reviewing a manuscript. AJR Am J Roentgenol, 2005, 185(4): 848-854.
30.	Budovec JJ, Kahn CE. Evidence-based radiology: a primer in reading scientific articles. AJR Am J Roentgenol, 2010, 195(1): W1-W4.
31.	Kocak B, Baessler B, Bakas S, et al. Checklist for evaluation of radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII. Insights Imaging, 2023, 14(1): 75.
32.	Kocak B, Borgheresi A, Ponsiglione A, et al. Explanation and elaboration with examples for CLEAR (CLEAR-E3): an EuSoMII radiomics auditing group initiative. Eur Radiol Exp, 2024, 8(1): 72.
33.	Kocak B, Akinci D'Antonoli T, Mercaldo N, et al. METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII. Insights Imaging, 2024, 15(1): 8.
34.	Zhong J, Xing Y, Lu J, et al. The endorsement of general and artificial intelligence reporting guidelines in radiological journals: a meta-research study. BMC Med Res Methodol, 2023, 23(1): 292.
35.	Koçak B, Keleş A, Köse F. Meta-research on reporting guidelines for artificial intelligence: are authors and reviewers encouraged enough in radiology, nuclear medicine, and medical imaging journals. Diagn Interv Radiol, 2024, 30(5): 291-298.

1. 周俊林, 萧毅, 张雪君, 等. 中国医学影像人工智能应用现状调研报告. 中华放射学杂志, 2022, 56(11): 1248-1253.
2. 刘士远. 医学影像人工智能发展趋势与挑战. 中华放射学杂志, 2021, 55(7): 700-702.
3. 陈冲, 夏黎明. 积极稳妥地推进人工智能在医学影像的应用. 中华放射学杂志, 2022, 56(1): 5-8.
4. Boverhof BJ, Redekop WK, Bos D, et al. Radiology AI deployment and assessment rubric (RADAR) to bring value-based AI into radiological practice. Insights Imaging, 2024, 15(1): 34.
5. Langlotz CP. The Future of AI and informatics in radiology: 10 predictions. Radiology, 2023, 309(1): e231114.
6. Yang L, Ene IC, Arabi Belaghi R, et al. Stakeholders' perspectives on the future of artificial intelligence in radiology: a scoping review. Eur Radiol, 2022, 32(3): 1477-1495.
7. Rivera SC, Liu X, Chan AW, et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI Extension. BMJ, 2020, 370: m3210.
8. Liu X, Rivera SC, Moher D, et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension. BMJ, 2020, 370: m3164.
9. Vasey B, Nagendran M, Campbell B, et al. Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. Nat Med, 2022, 28(5): 924-933.
10. Collins GS, Moons KGM, Dhiman P, et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ, 2024, 385: e078378.
11. Sounderajah V, Ashrafian H, Rose S, et al. A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI. Nat Med, 2021, 27(10): 1663-1665.
12. Sounderajah V, Ashrafian H, Golub RM, et al. Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol. BMJ Open, 2021, 11(6): e047709.
13. Collins GS, Dhiman P, Andaur Navarro CL, et al. Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open, 2021, 11(7): e048008.
14. Mongan J, Moy L, Kahn CE. Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiol Artif Intell, 2020, 2(2): e200029.
15. O'Shea RJ, Sharkey AR, Cook GJR, et al. Systematic review of research design and reporting of imaging studies applying convolutional neural networks for radiological cancer diagnosis. Eur Radiol, 2021, 31(10): 7969-7983.
16. Si L, Zhong J, Huo J, et al. Deep learning in knee imaging: a systematic review utilizing a Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Eur Radiol, 2022, 32(2): 1353-1361.
17. Belue MJ, Harmon SA, Lay NS, et al. The low rate of adherence to checklist for artificial intelligence in medical imaging criteria among published prostate MRI artificial intelligence algorithms. J Am Coll Radiol, 2023, 20(2): 134-145.
18. Zhong J, Hu Y, Zhang G, et al. An updated systematic review of radiomics in osteosarcoma: utilizing CLAIM to adapt the increasing trend of deep learning application in radiomics. Insights Imaging, 2022, 13(1): 138.
19. Sivanesan U, Wu K, McInnes MDF, et al. Checklist for artificial intelligence in medical imaging reporting adherence in peer-reviewed and preprint manuscripts with the highest altmetric attention scores: a meta-research study. Can Assoc Radiol J, 2023, 74(2): 334-342.
20. Kocak B, Keles A, Akinci D'Antonoli T. Self-reporting with checklists in artificial intelligence research on medical imaging: a systematic review based on citations of CLAIM. Eur Radiol, 2024, 34(4): 2805-2815.
21. 诸宇佳, 韩慧, 卫建华, 等. 人工智能医学影像研究报告规范: CLAIM检查清单解读. 中国循证医学杂志, 2023, 23(12): 1478-1484.
22. 王亚辉, 徐虹, 李大金. 人工智能医学影像研究报告规范(CLAIM)的解读及应用建议. 出版与印刷, 2023, (6): 89-95.
23. Tejani AS, Klontzas ME, Gatti AA, et al. Checklist for artificial intelligence in medical imaging (CLAIM): 2024 update. Radiol Artif Intell, 2024, 6(4): e240300.
24. Bossuyt PM, Reitsma JB, Bruns DE, et al. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. BMJ, 2015, 351: h5527.
25. Luo W, Phung D, Tran T, et al. Guidelines for developing and reporting machine learning predictive models in biomedical research: a multidisciplinary view. J Med Internet Res, 2016, 18(12): e323.
26. Handelman GS, Kok HK, Chandra RV, et al. Peering into the black box of artificial intelligence: evaluation metrics of machine learning methods. AJR Am J Roentgenol, 2019, 212(1): 38-43.
27. Park SH, Han K. Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction. Radiology, 2018, 286(3): 800-809.
28. Bluemke DA, Moy L, Bredella MA, et al. Assessing radiology research on artificial intelligence: a brief guide for authors, reviewers, and readers-from the radiology editorial board. Radiology, 2020, 294(3): 487-489.
29. Provenzale JM, Stanley RJ. A systematic guide to reviewing a manuscript. AJR Am J Roentgenol, 2005, 185(4): 848-854.
30. Budovec JJ, Kahn CE. Evidence-based radiology: a primer in reading scientific articles. AJR Am J Roentgenol, 2010, 195(1): W1-W4.
31. Kocak B, Baessler B, Bakas S, et al. Checklist for evaluation of radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII. Insights Imaging, 2023, 14(1): 75.
32. Kocak B, Borgheresi A, Ponsiglione A, et al. Explanation and elaboration with examples for CLEAR (CLEAR-E3): an EuSoMII radiomics auditing group initiative. Eur Radiol Exp, 2024, 8(1): 72.
33. Kocak B, Akinci D'Antonoli T, Mercaldo N, et al. METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII. Insights Imaging, 2024, 15(1): 8.
34. Zhong J, Xing Y, Lu J, et al. The endorsement of general and artificial intelligence reporting guidelines in radiological journals: a meta-research study. BMC Med Res Methodol, 2023, 23(1): 292.
35. Koçak B, Keleş A, Köse F. Meta-research on reporting guidelines for artificial intelligence: are authors and reviewers encouraged enough in radiology, nuclear medicine, and medical imaging journals. Diagn Interv Radiol, 2024, 30(5): 291-298.

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Latest ArticlesChecklist for artificial intelligence in medical imaging (CLAIM) 2024 update: a comparison and interpretation

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