Clinical significance, challenges and limitations in using artificial intelligence for electrocardiography-based diagnosis
Open accessAbstract
Cardiovascular diseases are one of the leading global causes of mortality. Currently, clinicians rely on their own analyses or automated analyses of the electrocardiogram (ECG) to obtain a diagnosis. However, both approaches can only include a finite number of predictors and are unable to execute complex analyses. Artificial intelligence (AI) has enabled the introduction of machine and deep learning algorithms to compensate for the existing limitations of current ECG analysis methods, with promising results. However, it should be prudent to recognize that these algorithms also associated with their own unique set of challenges and limitations, such as professional liability, systematic bias, surveillance, cybersecurity, as well as technical and logistical challenges. This review aims to increase familiarity with and awareness of AI algorithms used in ECG diagnosis, and to ultimately inform the interested stakeholders on their potential utility in addressing present clinical challenges.
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Bias in data presents a critical issue for ML-based PAS diagnosis, as ML algorithms trained on biased datasets may show disparities in performance across different population groups, leading to decreased diagnostic accuracy in specific demographic groups.
“Bias in data also presents a critical issue, as ML algorithms that have been trained on biased datasets may show disparities in performance across different population groups, leading to decreased diagnostic accuracy in specific demographic groups [40].”
From Machine Learning in Maternity: Early Diagnosis of Placenta Accreta Spectrum (2026) · cites this paper as [40]
The Systemic bias section says biased data can affect AI-ECG algorithms and reports lower diagnostic accuracy in populations with lower disease prevalence. It supports the general concern about unequal performance across groups, but discusses ECG-based detection of aortic stenosis and atrial fibrillation, not PAS diagnosis. (LLM, conf=0.86)
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References 63 · Cited by 79
Research works printed but not linked: 4
- [40]Au-Yeung WM, Sevakula RK, Sahani AK, Kassab M, Boyer R, Isselbacher EM, Real-time machine learning-based intensive care unit alarm classification without prior knowledge of the underlying rhythm. Eur Heart J Digit Health. 2021; 2(3): 437– 45. 34604758 PMC8482048
- [49]Bazoukis G, Hall J, Loscalzo J, The augmented intelligence in medicine: a framework for successful implementation. Cell Rep Med (in press). PMC8784713 35106506
- [51]Griffin F Artificial Intelligence and liability in health care. Health Matrix. 2021; 31: 65.
- [52]Barocas S, Selbst AD. Big data’s disparate impact. California Law Rev. 2016; 104: 671.
- Authors
- Chung, C. T., Lee, S., King, E., Liu, T., Armoundas, A. A., Bazoukis, G., & Tse, G.
- Affiliations
- Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, Hong Kong, China.
- Second Hospital of Tianjin Medical University (CN)
- Broad Institute (US)
- University of Nicosia (CY)
- Corresponding author
- Bazoukis, George
- Tse, Gary
- Venue
- International Journal of Arrhythmia
- Publisher
- XMLink
- Published
- 2022
- Volume
- 23
- Issue
- 1
- Pages
- 24-24
- ISSN
- 2466-0981 (ISSN-L)
- 2466-1171
- PMC
- PMC9525157
- OpenAlex
- W4298119164
- Cited by
- 79
- References
- 63
- Citations by year
- 2026: 24 · 2025: 22 · 2024: 21 · 2023: 12
- FWCI
- 2.80
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- International Journal of Arrhythmia (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Kent Academic Repository (University of Kent) (acceptedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- American Heart Association (17UNPG33840017)
- RICBAC Foundation
- National Institutes of Health (HL135335-01, 1 R21 HL137870-01, 1 R21EB026164-01, 3R21EB026164-02S1)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- artificial intelligence, electrocardiogram, electrocardiographic diagnosis, deep learning, machine learning, cardiovascular disease, algorithmic bias, professional liability, cybersecurity, clinical implementation barriers
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