Review Articles

The Impact of Technology and Digital Health on Cardiology: A Review of the Present to Reach the Future

Main Article Content

Sofia Couto da Rocha
Alberto Ramos
Keywords:
Artificial Intelligence, Biomedical Technology, Cardiology, Therapy, Computer-Assisted, Wearable Electronic Devices

Abstract

The integration of digital health technologies is revolutionizing the field of cardiology, particularly in the diagnosis, treatment, and management of cardiovascular diseases (CVDs). The rapid advancements in wearable devices, artificial intelligence (AI), and telemedicine have enabled more precise, predictable, and personalized care strategies, transforming the landscape of cardiovascular health. Wearable technologies, such as smartwatches with some electrocardiogram (ECG) capabilities, have improved early detection of arrhythmias, particularly atrial fibrillation (AF), enhancing patient outcomes by enabling timely interventions. Similarly, AI-driven diagnostic tools and machine learning (ML) models have demonstrated superior accuracy in interpreting ECGs and identifying complex arrhythmias, often outperforming traditional methods.


Telehealth has also gained traction, particularly during the COVID-19 pandemic, by facilitating remote monitoring of chronic CVDs. Remote monitoring devices, including implantable pacemakers and defibrillators, have further reduced mortality rates by providing real-time data to healthcare providers, allowing for early interventions. AI language models, such as ChatGPT, are being utilized to accelerate research, aid in clinical decision-making, and enhance patient engagement through personalized education and real-time assistance.


In addition to these advancements, digital therapeutics, and mobile health (mHealth) platforms are providing real-time feedback to patients and improving adherence to medication regimens, which is crucial for managing chronic conditions like hypertension and heart failure. Genomic and metabolomic medicine, with its focus on precision cardiology, allows for more personalized treatment plans based on an individual's genetic profile, further enhancing outcomes for those at risk for inherited cardiovascular diseases.


Despite the promising developments, challenges remain, including the need for better integration with healthcare systems, data privacy concerns, and ensuring equitable access to these technologies. Nevertheless, the future of cardiology is expected to be shaped by advancements in AI, wearable technologies, and precision medicine, paving the way for real proactive and personalized care.

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References

Lubitz SA, Faranesh AZ, Selvaggi C, Atlas SJ, McManus DD, Singer D, et al. Detection of Atrial Fibrillation in a Large Population Using Wearable Devices: The Fitbit Heart Study. Circulation. 2022;146:1415–24. doi: 10.1161/CIRCULATIONAHA.122.060291

Kuan PX, Chan WK, Fern Ying DK, Rahman MAA, Peariasamy K, et al. Efficacy of telemedicine for the management of cardiovascular disease: a systematic review and meta-analysis. The Lancet. Digital Health. 2022;4:e676–91. doi:10.1016/S2589-7500(22)00124-8

Chaikijurajai T, Laffin LJ, Tang WHW. Artificial Intelligence and Hypertension: Recent Advances and Future Outlook. Am J Hypertens. 2020;33:967–74. doi:10.1093/ajh/hpaa102

Pekmezaris R, Tortez L, Williams M. Home telemonitoring in heart failure: a systematic review and meta-analysis. Health Aff. 2018;37:1983-9. doi:10.1377/hlthaff.2018.05087

Muzammil M, Javid S, Afridi A, Siddineni A, Shahabi M, Haseeb M, et al. Artificial intelligence-enhanced electrocardiography for accurate diagnosis and management of cardiovascular diseases. J Electrocardiol. 2024;83:30-40. doi:10.1016/j.jelectrocard.2024.01.006

Khera R, Oikonomou E, Nadkarni G. Transforming Cardiovascular Care With Artificial Intelligence: From Discovery to Practice: JACC State-of-the-Art Review. JACC. 2024;84:97–114. doi:10.1016/j.jacc.2024.05.003

Siddiqui S, Ahmad A, Fatima N. IoT-based disease prediction using machine learning. Comput Electr Eng. 2023;108:108675. doi:10.1016/j.compeleceng.2023.108675

Ferguson C, Wynne R, Inglis SC. Wearable cardiac monitoring pusing smart-devices for the detection of atrial fibrillation in adults. Cochrane Database Syst Rev. 2021;2021:CD014629. doi:10.1002/14651858. CD014629

Kalasin S, Surareungchai W. Challenges of emerging wearable sensors for remote monitoring toward telemedicine healthcare. Anal Chem. 2023;95:1773-84. doi: 10.1021/acs.analchem.2c02642

Bashar S, Ding E, Whitcomb C, McManus D, Chon K. Smartwatch Based Atrial Fibrillation Detection from Photoplethysmography Signals. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2019. 4306-9. doi: 10.1109/EMBC.2019.8856928

Al-Mallah MH. Introduction to the Special Issue: Myocardial Imaging in Heart Failure. Heart Fail Rev. 2017;22:381–3. doi:10.1007/s10741-017-9633-4

Heidenreich PA, Bozkurt B, Aguilar D, Allen LA, Byun JJ, Colvin MM, et al. 2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation. 2022;145:e895–e1032. doi:10.1161/CIR.0000000000001063

Pegoraro V, Bidoli C, Dal Mas F, Bert F, Cobianchi L, Zantedeschi M, et al. Cardiology in a Digital Age: Opportunities and Challenges for e-Health: A Literature Review. J Clin Med. 2023;12:4278. doi:10.3390/jcm12134278

Bhardwaj A, Bandyopadhyay R, Kumar R, Neelapu B, Pal K, Sivaraman J. Artificial intelligence techniques for diagnosis of atrial fibrillation. In: Advances in Artificial Intelligence. London: Academic Press; 2024. p. 203-235.

Giudicessi J, Kullo I, Ackerman M. Precision Cardiovascular Medicine: State of Genetic Testing. Mayo Clin Proc. 2017;92:64262. doi:10.1016/j.mayocp.2017.01.015

Gray M, Fatkin D, Ingles J, Robertson E, Figtree G. Genetic testing in cardiovascular disease. Med J Aust. 2024;220:428-34. doi:10.5694/mja2.52278

Williams G, Al-Baraikan A, Ciravegna F, N van de Vosse F, Lawrie D, Rothman A, et al. Wearable technology and the cardiovascular system: the future of patient assessment. Lancet Digit Health. 2023;5:e467-e476. doi: 10.1016/S2589-7500(23)00087-0

Karatzia L, Aung N, Aksentijevic D. Artificial Intelligence in Cardiology: Hope for the future and power for the present. Front Cardiovasc Med. 2022;9. doi:10.3389/fcvm.2022.945726

Cai Y, Cai YQ, Tang LY, Wang YH, Gong M, Jing TC, et al. Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review. BMC Med. 2024;22:56. doi:10.1186/s12916-024-03273-7

Gupta MD, Kunal S, Girish MP, Gupta A, Yadav R. Artificial intelligence in cardiology: The past, present and future. Indian Heart J. 2022;74:265–69. doi:10.1016/j.ihj.2022.07.004

Turchioe M, Slotwiner D. Screening for atrial fibrillation using digital health: moving from promises to reality. JACC Adv. 2023;2:100621. doi: 10.1016/j.jacadv.2023.100621

Vandenberk B, Chew DS, Prasana D, Gupta S, Exner DV. Successes and challenges of artificial intelligence in cardiology. Front Digit Health. 2023;5:1201392. doi:10.3389/fdgth.2023.1201392

Hannun AY, Rajpurkar P, Haghpanahi M, Tison G, Bourn C, Turakhia M, et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med. 2019;25:65–9. doi:10.1038/s41591-018-0268-3

Attia ZI, Noseworthy PA, Lopez-Jimenez F, Asirvatham SJ, Deshmukh AJ, Gersh BJ, et al. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet. 2019;394:861-7. doi: 10.1016/S0140-6736(19)31721-0

Finet P, Jeannès R, Dameron O, Gibaud B. Review of current telemedicine applications for chronic diseases. IRBM. 2015;36:133-57. doi: 10.1016/j.irbm.2015.01.009

Sharma S, Rawal R, Shah D. Addressing the challenges of AI-based telemedicine: Best practices and lessons learned. J Educ Health Promot. 2023;1:338. doi: 10.4103/jehp.jehp_402_23

Chao TF, Potpara TS, Lip GYH. Atrial fibrillation: stroke prevention. Lancet Reg Health Eur. 2024;37:100797. doi: 10.1016/j.lanepe.2023.100797

Kilic A. Artificial Intelligence and Machine Learning in Cardiovascular Health Care. Ann Thorac Surg. 2020;109:1323-9. doi: 10.1016/j.athoracsur.2019.09.042