Análisis de la calidad de una aplicación móvil de inteligencia artificial para la interpretación del electrocardiograma

Rodrigo Chavez-Ecos; Kiara Camacho-Caballero; Marcelo S. Chavez-Ecos; Miguel A. Chavez-Gutarra; Oscar Aguirre-Zurita; Fabian A. Chavez-Ecos

doi:10.47487/apcyccv.v5i2.363

Authors

Rodrigo Chavez-Ecos CHANGE Research Working Group, Facultad de Ciencias de la Salud, Carrera de Medicina Humana, Universidad Científica del Sur, Lima, Perú. https://orcid.org/0009-0009-5285-0393
Kiara Camacho-Caballero CHANGE Research Working Group, Facultad de Ciencias de la Salud, Carrera de Medicina Humana, Universidad Científica del Sur, Lima, Perú. https://orcid.org/0000-0002-3609-2424
Marcelo S. Chavez-Ecos CHANGE Research Working Group, Facultad de Ciencias de la Salud, Carrera de Medicina Humana, Universidad Científica del Sur, Lima, Perú. https://orcid.org/0009-0002-9833-7543
Miguel A. Chavez-Gutarra Facultad de Medicina Humana, Universidad Nacional San Luis Gonzaga, Ica, Perú. https://orcid.org/0009-0000-7022-4315
Oscar Aguirre-Zurita Instituto Nacional Cardiovascular «Carlos Alberto Peschiera Carrillo», Departamento de Cardiología, INCOR, Lima, Perú. https://orcid.org/0000-0003-3517-0395
Fabian A. Chavez-Ecos CHANGE Research Working Group, Facultad de Ciencias de la Salud, Carrera de Medicina Humana, Universidad Científica del Sur, Lima, Perú; Facultad de Medicina Humana, Universidad Nacional San Luis Gonzaga, Ica, Perú.

DOI:

https://doi.org/10.47487/apcyccv.v5i2.363

Keywords:

Artificial Intelligence, Electrocardiography

Downloads

Download data is not yet available.

References

CookDA,OhSY,PusicMV.AccuracyofPhysicians’Electrocardiogram Interpretations: A Systematic Review and Meta-analysis. JAMA Intern Med. 2020;180(11):1461-71. doi: 10.1001/jamainternmed.2020.3989.

Semigran HL, Levine DM, Nundy S, Mehrotra A. Comparison of Physician and Computer Diagnostic Accuracy. JAMA Intern Med. 2016;176(12):1860-1861. doi: 10.1001/jamainternmed.2016.6001.

Attia ZI, Harmon DM, Behr ER, Friedman PA. Application of artificial intelligence to the electrocardiogram. Eur Heart J. 2021;42(46):4717- 30. doi: 10.1093/eurheartj/ehab649.

Shiferaw KB, Wali P, Waltemath D, Zeleke AA. Navigating the AI frontiers in cardiovascular research: a bibliometric exploration and topic modeling. Front Cardiovasc Med. 2024;10:1308668. doi: 10.3389/fcvm.2023.1308668.

Stoyanov SR, Hides L, Kavanagh DJ, Wilson H. Development and Validation of the User Version of the Mobile Application Rating Scale (uMARS). JMIR Mhealth Uhealth. 2016;4(2):e72. doi: 10.2196/ mhealth.5849

Veazie S, Winchell K, Gilbert J, Paynter R, Ivlev I, Eden K, Nussbaum K, Weiskopf N, Guise JM, Helfand M. Mobile Applications for Self- Management of Diabetes. Rockville (MD): Agency for Healthcare Research and Quality (US); 2018 May. Report No.: 18-EHC010-EF.

Steubl LS, Reimann J, Simon L, Terhorst Y, Stach M, Baumeister H, et al. A systematic quality rating of available mobile health apps for borderline personality disorder. Borderline Personal Disord Emot Dysregul. 2022;9(1):1-10. doi: 10.1186/s40479-022-00186-w.

Mathews SC, McShea MJ, Hanley CL, Ravitz A, Labrique AB, Cohen AB. Digital health: a path to validation. NPJ Digit Med. 2019;2(1):38. doi: 10.1038/s41746-019-0111-3.