Literature DB >> 35481865

Artificial Intelligence for Education, Proctoring, and Credentialing in Cardiovascular Medicine.

Zvonimir Krajcer1,2.   

Abstract

Artificial intelligence and machine learning are rapidly gaining popularity in every aspect of cardiovascular medicine. This review discusses the past, present, and future of artificial intelligence in education, remote proctoring, credentialing, research, and publication as they pertain to cardiovascular procedures. This review describes the benefits and limitations of artificial intelligence and machine learning and the exciting potential of integrating advanced simulation, holography, virtual reality, and extended reality into disease diagnosis and patient care, as well as their roles in cardiovascular research and education. Nonetheless, much of the available data resides in electronic medical records or within industry-sponsored proprietary programs that are not compatible or standardized for current clinical application. Many areas in cardiovascular medicine would benefit from the introduction or increased use of artificial intelligence. Web-based artificial intelligence applications could be used to address unmet needs for education, on-demand procedural proctoring, credentialing, and recredentialing for interventionists and physicians in remote locations. Further progress in artificial intelligence will require further collaboration among computer scientists and researchers in order to identify and correct the most relevant problems and to implement the best data-based approach to achieving this goal. The future success of artificial intelligence in cardiovascular medicine will depend on the degree of collaboration between all pertinent experts in this field. This will undoubtedly be a prolonged, stepwise process.
© 2022 by the Texas Heart® Institute, Houston.

Entities:  

Keywords:  Artificial intelligence; cardiovascular surgical procedures; credentialing; diagnostic techniques, cardiovascular/methods/trends; education; imaging, three-dimensional; investigative techniques; machine learning

Mesh:

Year:  2022        PMID: 35481865      PMCID: PMC9053661          DOI: 10.14503/THIJ-21-7572

Source DB:  PubMed          Journal:  Tex Heart Inst J        ISSN: 0730-2347


  17 in total

1.  A new microscopic principle.

Authors:  D GABOR
Journal:  Nature       Date:  1948-05-15       Impact factor: 49.962

2.  A comparison of models for predicting early hospital readmissions.

Authors:  Joseph Futoma; Jonathan Morris; Joseph Lucas
Journal:  J Biomed Inform       Date:  2015-06-01       Impact factor: 6.317

3.  Recent advances in digital holography [invited].

Authors:  Wolfgang Osten; Ahmad Faridian; Peng Gao; Klaus Körner; Dinesh Naik; Giancarlo Pedrini; Alok Kumar Singh; Mitsuo Takeda; Marc Wilke
Journal:  Appl Opt       Date:  2014-09-20       Impact factor: 1.980

4.  Digital holography super-resolution for accurate three-dimensional reconstruction of particle holograms.

Authors:  Nicolas Verrier; Corinne Fournier
Journal:  Opt Lett       Date:  2015-01-15       Impact factor: 3.776

Review 5.  Machine learning in cardiovascular medicine: are we there yet?

Authors:  Khader Shameer; Kipp W Johnson; Benjamin S Glicksberg; Joel T Dudley; Partho P Sengupta
Journal:  Heart       Date:  2018-01-19       Impact factor: 5.994

6.  Clinical and economic outcomes of ProGlide compared with surgical repair of large bore arterial access.

Authors:  Darren B Schneider; Zvonimir Krajcer; Machaon Bonafede; Elizabeth Thoma; James Hasegawa; Prajakta Bhounsule; Ellen Thiel
Journal:  J Comp Eff Res       Date:  2019-10-31       Impact factor: 1.744

Review 7.  Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges.

Authors:  Benjamin A Goldstein; Ann Marie Navar; Rickey E Carter
Journal:  Eur Heart J       Date:  2017-06-14       Impact factor: 29.983

8.  A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data.

Authors:  Sara Bersche Golas; Takuma Shibahara; Stephen Agboola; Hiroko Otaki; Jumpei Sato; Tatsuya Nakae; Toru Hisamitsu; Go Kojima; Jennifer Felsted; Sujay Kakarmath; Joseph Kvedar; Kamal Jethwani
Journal:  BMC Med Inform Decis Mak       Date:  2018-06-22       Impact factor: 2.796

Review 9.  Emerging Applications of Virtual Reality in Cardiovascular Medicine.

Authors:  Jennifer N A Silva; Michael Southworth; Constantine Raptis; Jonathan Silva
Journal:  JACC Basic Transl Sci       Date:  2018-06-25

10.  Leveraging the Electronic Health Record to Create an Automated Real-Time Prognostic Tool for Peripheral Arterial Disease.

Authors:  Adelaide M Arruda-Olson; Naveed Afzal; Vishnu Priya Mallipeddi; Ahmad Said; Homam Moussa Pacha; Sungrim Moon; Alisha P Chaudhry; Christopher G Scott; Kent R Bailey; Thom W Rooke; Paul W Wennberg; Vinod C Kaggal; Gustavo S Oderich; Iftikhar J Kullo; Rick A Nishimura; Rajeev Chaudhry; Hongfang Liu
Journal:  J Am Heart Assoc       Date:  2018-12-04       Impact factor: 5.501

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