cirpublications.com-Ae1g-1794653715-i117109956 1794653715 Clinical Intelligence Research Press info@cirpublications.com Clinical Intelligence Research Press Journal of Artificial Intelligence for Healthcare Systems J. Artif. Intell. Healthc. Syst. 3149-8981 2024 3 2 Lucas Andrade Mariana Lopes Daniel Brooks 2024 10.68159/i117109956 https://cirpublications.com/pub/journal/1/article/i117109956 Ali O, Abdelbaki W, Shrestha A, Elbasi E, Alryalat MAA, Dwivedi YK. A systematic literature review of artificial intelligence in the healthcare sector: benefits, challenges, methodologies, and functionalities. J Innov Knowl. 2023;8(1):100333. 10.1016/j.jik.2022.100333 Kitsios F, Kamariotou M. Recent advances of artificial intelligence in healthcare: a systematic literature review. Appl Sci (Basel). 2023;13(13):7479. 10.3390/app13137479 Elragal R, Elragal A. Healthcare analytics—a literature review and proposed research agenda. Front Big Data. 2023;6:1277976. 10.3389/fdata.2023.1277976 Arora A, Arora A. The value of standards for health datasets in artificial intelligence-based applications. Nat Med. 2023;29(11):2929-38. 10.1038/s41591-023-02572-8 Ramezani M, Takian A, Bakhtiari A, Rabiee HR, Ghazanfari S. The application of artificial intelligence in health policy: a scoping review. BMC Health Serv Res. 2023;23(1):1416. 10.1186/s12913-023-10459-2 Sivarajah U, Kamal MM, Irani Z, Weerakkody V. Responsible artificial intelligence for digital health and medical analytics. Inf Syst Front. 2023;25(6):2125-7. 10.1007/s10796-023-10425-0 Samaras A, Lambrou G, Arvaniti N, Loutsidis A, Magou E, Chytas D, et al. Artificial intelligence-based mining of electronic health record data to accelerate the digital transformation of the national cardiovascular ecosystem: design protocol of the CardioMining study. BMJ Open. 2023;13(3):e068698. 10.1136/bmjopen-2022-068698 Liao F, Adelaine S, Afshar M, Patterson BW, Yao Z, Patterson B, et al. Governance of clinical AI applications to facilitate safe and equitable deployment in a large health system: key elements and early successes. Front Digit Health. 2022;4:931439. 10.3389/fdgth.2022.931439 Winter JS. AI in healthcare: data governance challenges. J Hosp Manag Health Policy. 2021;5:8 Morley J, Taddeo M, Floridi L. Governing data and artificial intelligence for health care: developing an international understanding. JMIR Form Res. 2022;6(1):e31623. 10.2196/31623 Ait Abdelouahid R, Audy D, Debache O. Literature review: clinical data interoperability models. Information (Basel). 2023;14(7):364. 10.3390/info14070364 Nan J, Xu L. Designing interoperable health care services based on fast healthcare interoperability resources: literature review. JMIR Med Inform. 2023;11:e44842. 10.2196/44842 Moazemi S, Vahdati S, Li J, Kalkhoff S, Castano LJ, Dewitz B, et al. Artificial intelligence for clinical decision support for monitoring patients in cardiovascular ICUs: a systematic review. Front Med (Lausanne). 2023;10:1109411. 10.3389/fmed.2023.1109411 Vijayakumar S, Barbieri R, Duong E, Khanna A, Ong K, Teng J, et al. Physicians’ perspectives on AI in clinical decision support systems: interview study of the CURATE.AI platform. JMIR Hum Factors. 2023;10:e48476. 10.2196/48476 Sanchez M, Cohen A, Jiang C, Zuckerman JE, Huang LC, Sarma KV, et al. AI-clinician collaboration via disagreement prediction: a decision pipeline and retrospective analysis of real-world radiologist-AI interactions. Cell Rep Med. 2023;4(10):101213. 10.1016/j.xcrm.2023.101213 Sandhu S, Georgiou A, Lin K, Westbrook JI. Integrating a machine learning system into clinical workflows: qualitative study. J Med Internet Res. 2020;22(11):e22421. 10.2196/22421 Chenais G, Lagadec E, Gil-Jardiné C, Foissaud V, Contrand B, Tellier E, et al. Deep learning transformer models for building a real-time trauma observatory: development and validation study. JMIR AI. 2023;2:e40843. 10.2196/40843 Lentzen M, Madan R, Larenz K, Schroth S, Knaup P. Critical assessment of transformer-based AI models for German clinical notes. JAMIA Open. 2022;5(4):ooac087. Khader F, Müller-Franzes G, Wang L, Han T, Haltmeier M, Khader A, et al. Medical transformer for multimodal survival prediction in intensive care. Sci Rep. 2023;13(1):10827. 10.1038/s41598-023-37928-7 Holmgren AJ, Apathy NC, Adler-Milstein J. Health information exchange: understanding the policy landscape and implications for data flow. J Gen Intern Med. 2023;38(Suppl 1):4-11. 10.1007/s11606-023-07974-4 van der Vegt AH, Scott IA, Dermawan K, Schnetler RJ, Kalkman CJ, Campbell JL. Implementation frameworks for end-to-end clinical AI: derivation of the SALIENT framework. J Am Med Inform Assoc. 2023;30(9):1503-15. Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380(14):1347-58. 10.1056/NEJMra1814259 Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56. 10.1038/s41591-018-0300-7 Beam AL, Kohane IS. Big data and machine learning in health care. JAMA. 2018;319(13):1317-8. 10.1001/jama.2017.18391 Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2(4):230-43. 10.1136/svn-2017-000101 Ching T, Himmelstein DS, Beaulieu-Jones BK, Kalinin AA, Do BT, Way GP, et al. Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface. 2018;15(141):20170387. 10.1098/rsif.2017.0387 Chen JH, Asch SM. Machine learning and prediction in medicine—beyond the peak of inflated expectations. N Engl J Med. 2017;376(26):2507-9. 10.1056/NEJMp1702071 Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115-8. 10.1038/nature21056 Beede E, Baylor E, Hersch F, Iurchenko A, Wilcox L, Ruamviboonsuk P, et al. A human-centered evaluation of a deep learning system deployed in clinics for the detection of diabetic retinopathy. Proc CHI Conf Hum Factors Comput Syst. 2020;2020:1-12. 10.1145/3313831.3376718 Liu Y, Kohlberger T, Norouzi M, Dahl GE, Smith JL, Mohtashamian A, et al. Artificial intelligence–based breast cancer nodal metastasis detection: insights into the black box for pathologists. Arch Pathol Lab Med. 2019;143(7):859-68. 10.5858/arpa.2018-0147-OA Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H, Duan T, et al. CheXNet: radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv. 2017;arXiv:1711.05225