cirpublications.com-W6Cx-1790899046-h117919361 1790899046 Clinical Intelligence Research Press info@cirpublications.com Clinical Intelligence Research Press Journal of Health Informatics and Digital Systems J. Health Inform. Digit. Syst. 3149-8973 2022 2 1 Nguyen Thanh Huy Pham Quang Minh Le Thi Bich 2022 10.68159/h117919361 https://cirpublications.com/pub/journal/2/article/h117919361 Rivera SC, Liu X, Chan AW, Denniston AK, Calvert MJ, et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Nat Med. 2020;26(9):1351-63. 10.1038/s41591-020-1037-7 Liu R, Rizzo S, Whipple S, Pal N, Pineda AL, Lu M, et al. Evaluating eligibility criteria of oncology trials using real-world data and AI. Nature. 2021;592(7854):629-33. 10.1038/s41586-021-03430-5 de Hond AAH, Leeuwenberg AM, Hooft L, Kant IMJ, Nijman SWJ, van Os HJA, et al. Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review. NPJ Digit Med. 2022;5(1):2. 10.1038/s41746-021-00549-7 Vasey B, Nagendran M, Campbell B, Clifton DA, Collins GS, Denaxas S, 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):924-33. 10.1038/s41591-022-01772-9 Inan OT, Tenaerts P, Prindiville SA, Reynolds HR, Dizon DS, Cooper-Arnold K, et al. Digitizing clinical trials. NPJ Digit Med. 2020;3(1):101. 10.1038/s41746-020-0302-y Dobbins NJ, Spital CH, Black RA, Morrison JM, de Haan M, Latour CD, et al. The leaf clinical trials corpus: a new resource for query generation from clinical trial eligibility criteria. Sci Data. 2022;9(1):517. 10.1038/s41597-022-01521-0 Shah P, Kendall F, Khozin S, Goosen R, Hu J, Laramie J, et al. Artificial intelligence and machine learning in clinical development: a translational perspective. NPJ Digit Med. 2019;2(1):69. 10.1038/s41746-019-0148-3 Zhou Q, Chen ZH, Li SD, Xu JD, Li Y, Wang S, et al. Clinical impact and quality of randomized controlled trials involving interventions evaluating artificial intelligence prediction tools: a systematic review. NPJ Digit Med. 2021;4(1):154. 10.1038/s41746-021-00524-2 Parikh RB, Helmchen LA. Paying for artificial intelligence in medicine. NPJ Digit Med. 2022;5(1):63. 10.1038/s41746-022-00609-6 Feng J, Phillips RV, Malenica I, Bishara A, Hubbard AE, Celi LA, et al. Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare. NPJ Digit Med. 2022;5(1):66. 10.1038/s41746-022-00611-y Abazeed ME. Walking the tightrope of artificial intelligence guidelines in clinical practice. Lancet Digit Health. 2019;1(3):e100. 10.1016/S2589-7500(19)30063-9 Nephew LD. Accountability in clinical trial diversity: the buck stops where? EClinicalMedicine. 2021;36:100906. 10.1016/j.eclinm.2021.100906 Benjamens S, Dhunnoo P, Meskó B. The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. NPJ Digit Med. 2020;3(1):118. 10.1038/s41746-020-00324-0 Elkin ME, Zhu X. Predictive modeling of clinical trial terminations using feature engineering and embedding learning. Sci Rep. 2021;11(1):3446. 10.1038/s41598-021-82840-x Kostick-Quenet KM, Gerke S. AI in the hands of imperfect users. NPJ Digit Med. 2022;5(1):197. 10.1038/s41746-022-00737-z Liu X, Glocker B, McCradden MM, Ghassemi M, Denniston AK, Oakden-Rayner L. The medical algorithmic audit. Lancet Digit Health. 2022;4(5):e384-e397. 10.1016/S2589-7500(22)00003-6 Ciecierski-Holmes T, Singh R, Axt M, Brenner S, Barteit S. Artificial intelligence for strengthening healthcare systems in low- and middle-income countries: a systematic scoping review. NPJ Digit Med. 2022;5(1):162. 10.1038/s41746-022-00700-y Kotecha D, Asselbergs FW, Achenbach S, Anker SD, Atar D, Baigent C, et al. CODE-EHR best practice framework for the use of structured electronic healthcare records in clinical research. BMJ. 2022;378:e069048. 10.1136/bmj-2021-069048 Muehlematter UJ, Daniore P, Vokinger KN. Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015–20): a comparative analysis. Lancet Digit Health. 2021;3(3):e195-e203. 10.1016/S2589-7500(20)30292-2 Taeihagh A. Governance of artificial intelligence. Policy Soc. 2021;40(2):137-57. 10.1080/14494035.2021.1928377 von Itzstein MS, Hullings M, Mayo H, Reisch JG, Ma J, Gerber DE. Application of information technology to clinical trial evaluation and enrollment: a review. JAMA Oncol. 2021;7(10):1559-66. 10.1001/jamaoncol.2021.1165 Plana D, Shung DL, Grimshaw AA, Saraf A, Sung JJY, Kann BH. Randomized clinical trials of machine learning interventions in health care: a systematic review. JAMA Netw Open. 2022;5(9):e2233946. 10.1001/jamanetworkopen.2022.33946 Harrer S, Shah P, Antony B, Hu J. Artificial intelligence for clinical trial design. Trends Pharmacol Sci. 2019;40(8):577-91. 10.1016/j.tips.2019.05.005 Liu H, Chi Y, Butler A, Sun Y, Weng C. A knowledge base of clinical trial eligibility criteria. J Biomed Inform. 2021;117:103771. 10.1016/j.jbi.2021.103771 Liu X, Rivera SC, Moher D, Calvert MJ, Denniston AK, et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Lancet Digit Health. 2020;2(10):e537-e548. 10.1016/S2589-7500(20)30218-1 Ho CWL, Soon D, Caals K, Kapur J. Governance of automated image analysis and artificial intelligence analytics in healthcare. Clin Radiol. 2019;74(5):329-37. 10.1016/j.crad.2019.02.005 Zuiderwijk A, Chen YC, Salem F. Implications of the use of artificial intelligence in public governance: a systematic literature review and a research agenda. Gov Inf Q. 2021;38(3):101577. 10.1016/j.giq.2021.101577 Rogers JR, Liu C, Hripcsak G, Cheung YK, Zhou Y, Weng C. Using real-world data to evaluate the effects of relaxing add-on low-dose glucocorticoids on response to immune checkpoint inhibitors in advanced non-small cell lung cancer. J Biomed Inform. 2022;127:104010. 10.1016/j.jbi.2022.104010