OkadaM. Big data and real-world data-based medicine in the management of hypertension. Hypertens Res. 2021;44:147-53.
2.
BehrendtCAMullerTVenermoM, et al.The VASCUNET manifesto on data privacy compliant real world evidence. Eur J Vasc Endovasc Surg. 2020;60:942-3.
3.
SutzkoDCManiKBehrendtCAWanhainenABeckAW. Big data in vascular surgery: registries, international collaboration and future directions. J Intern Med. 2020;288:51-61.
4.
BehrendtCAVenermoMCronenwettJL, et al.VASCUNET, VQI, and the international consortium of vascular registries - unique collaborations for quality improvement in vascular surgery. Eur J Vasc Endovasc Surg. 2019;58:792-3.
MirchevMMirchevaIKerekovskaA. The Academic Viewpoint on Patient Data Ownership in the Context of Big Data: Scoping Review. J Med Internet Res. 2020;22:e22214.
BeckmannMDittmerKJaschkeJ, et al.Electronic patient record and its effects on social aspects of interprofessional collaboration and clinical workflows in hospitals (eCoCo): a mixed methods study protocol. BMC Health Serv Res. 2021;21:377.
19.
OlivaAGrassiSVetrugnoG, et al.Management of medico-legal risks in digital health era: a scoping review. Front Med (Lausanne). 2021;8:821756.
20.
ReddySAllanSCoghlanSCooperP. A governance model for the application of AI in health care. J Am Med Inform Assoc. 2020;27:491-7.
21.
ShellerMJEdwardsBReinaGA, et al.Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Sci Rep. 2020;10:12598.
22.
MehmoodA IN, XiangYHuaGGuoS, Protection of Big Data Privacy. IEEE Access. 2016;4:1821-34, DOI: 10.1109/ACCESS.2016.2558446.
23.
LiDCLiuCWHuSC. A learning method for the class imbalance problem with medical data sets. Comput Biol Med. 2010;40:509-18.
24.
GaoLZhangLLiuCWuS. Handling imbalanced medical image data: A deep-learning-based one-class classification approach. Artif Intell Med. 2020;108:101935.
25.
LehneMSassJEssenwangerASchepersJThunS. Why digital medicine depends on interoperability. NPJ Digit Med. 2019;2:79.
26.
AsswadJGómezJM. Data Ownership: A Survey. Information. 2021; 12(11):465. DOI: 10.3390/info12110465.
27.
RaffortJAdamCCarrierMLareyreF. Fundamentals in artificial intelligence for vascular surgeons. Ann Vasc Surg. 2020;65:254-60.
28.
LareyreFAdamCCarrierMRaffortJ. Prediction of abdominal aortic aneurysm growth and risk of rupture in the era of machine learning. Angiology. 2020;71(8):767.
29.
ParaskevasKISabaLSuriJS. Applications of artificial intelligence in vascular diseases. Angiology. 2022.33197221087779.
30.
JavidanAPLiALeeMHForbesTLNajiF. A systematic review and bibliometric analysis of applications of artificial intelligence and machine learning in vascular surgery. Ann Vasc Surg. 2022;24:S0890‐5096(22)00148‐0.
31.
FischerUMShiremanPKLinJC. Current applications of artificial intelligence in vascular surgery. Semin Vasc Surg. 2021;34:268-71.
32.
HahnLDBaeumlerKHsiaoA. Artificial intelligence and machine learning in aortic disease. Curr Opin Cardiol. 2021;36:695-703.
33.
LareyreFLeCDBallaithA, et al.Applications of artificial intelligence in non-cardiac vascular diseases: a bibliographic analysis. Angiology. 2022;7:606-614.
34.
LareyreFAdamCCarrierMChakfeNRaffortJ. Artificial intelligence for education of vascular surgeons. Eur J Vasc Endovasc Surg. 2020;59:870-1.
35.
NystromMEKarltunJKellerCAndersson GareB. Collaborative and partnership research for improvement of health and social services: researcher's experiences from 20 projects. Health Res Policy Syst. 2018;16:46.
36.
DusdalJPowellJW. Benefits, motivations, and challenges of international collaborative research: a sociology of science case study. Sci Publ Pol, 48(2), 2021, 235-45.
37.
BjorckM. Collaborative research: population-based data and validation are necessary. Br J Surg. 2022;109(7):578-79.
38.
ExtanceA. How AI technology can tame the scientific literature. Nature. 2018;561:273-4.
39.
Reading TurchioeMVolodarskiyAPathakJWrightDNTchengJESlotwinerD. Systematic review of current natural language processing methods and applications in cardiology. Heart. 2022;108:909-16.
40.
SheikhalishahiSMiottoRDudleyJTLavelliARinaldiFOsmaniV. Natural language processing of clinical notes on chronic diseases: systematic review. JMIR Med Inform. 2019;7:e12239.
