This review compares three primary AI technologies for AFib diagnostics: neural networks, wearable-integrated tools, and machine learning (ML) models, focusing on studies from 2020 to 2024, and includes head-to-head performance comparisons between the models and clinicians.

Wearable AI technologies, while slightly lower in diagnostic precision than NN, offered advantages in real-time ambulatory monitoring, with sensitivities above 94% [12]. Devices like the SanketLife wireless ECG biosensor and smartwatches demonstrated high usability and timely arrhythmia detection [11, 25, 26]. Yet, their reliance on patient compliance, single-lead ECGs, and testing in narrow populations limits broader clinical reliability. These models may reduce the time between arrhythmia onset and diagnosis, supporting more timely interventions in outpatient cardiology [13, 27]. Head-to-head comparisons with clinicians and validation across diverse settings are still needed [28].

Neural networks, especially convolutional and deep neural networks, demonstrated the highest diagnostic accuracy, often exceeding clinician performance in retrospective ECG analyses (80% vs. 75%) [14]. For instance, DNNs have shown promise in detecting paroxysmal AFib from sinus rhythm ECGs, which is helpful for post-visit screening or population-level risk assessment [16]. However, these models were trained on limited, homogeneous datasets and lacked prospective validation, raising concerns about generalizability [14, 15]. However, limitations, such as retrospective design, lack of multicenter data, and dataset imbalance, highlight the need for broader clinical validation and testing in real-world settings.

ML technologies showed relatively strong accuracy (AUROC 0.74–0.89) in identifying high-risk individuals in developing AFib, incorporating genetic and social factors from EMR [20]. However, most of these models were developed using retrospective data and lacked external validation, limiting their immediate clinical applicability. Overall, machine learning (ML) tools, when trained on large datasets, can aid clinicians in efficiently identifying arrhythmias while maintaining care quality [29]. ML strength lies in risk prediction and stratification, but like NNs, they were primarily retrospective and lacked external validation.

Together, these modalities offer complementary strengths: neural networks for high-accuracy ECG analysis, wearables for accessible real-time detection, and ML models for risk stratification, each with distinct limitations that require further validation and refinement.

As artificial intelligence becomes increasingly integrated into cardiovascular diagnostics, understanding its practical applications is essential for effective clinical translation. Clinicians can consider integrating AI tools into AFib care, depending on the setting and subtype. Wearable AI devices are beneficial for detecting paroxysmal or asymptomatic AFib in ambulatory or postoperative monitoring [12, 16]. Neural networks can support ECG analysis in high-volume clinics, while machine learning models integrated into EHR systems may enhance preventative care by identifying high-risk individuals using genetic and social data [30]. Physicians should prioritize tools with external validation, transparent algorithms, and clear demographic reporting. Clinical use should be tailored to the societal context, as some models are better suited for detecting latent AFib via routine ECGs, while others leverage EMR data to identify incident AFib in older populations [31]. As evidence continues to evolve, cardiologists must remain informed about the strengths and limitations of each modality to ensure the responsible, equitable, and impactful implementation of patient care.

Although AI models demonstrated high diagnostic performance in controlled settings, this review is limited by the predominance of single-center, small-sample studies with minimal population diversity, often underrepresenting racial minorities and low-resource settings. For instance, Zhu et al. and Cai et al. relied on Chinese hospital data with limited geographic diversity, while Gruwez et al. used Belgian ECG datasets with unclear socioeconomic variation [14,15,16]. Most studies employed retrospective designs and had an unclear risk of bias in patient selection and follow-up timing, as indicated by the QUADAS-2 assessment, which limits their external validity and generalizability. This reduces external validation and can limit reliability and generalizability. Furthermore, performance metrics, such as AUROC, sensitivity, and specificity, were inconsistently reported, making cross-study comparisons challenging. No formal meta-analysis was performed due to heterogeneity in study design, AI model types, and reported outcome metrics. As a result, findings are presented descriptively without pooled estimates. Finally, the potential for publication bias remains, as studies with negative or null results may be underrepresented in the included literature. Additionally, rare or complex arrhythmias, such as asymptomatic paroxysmal AFib, may have reduced diagnostic accuracy across models. These models also rely on patient adherence, especially the wearable devices, for accurate detection of arrhythmias. None of the included studies assessed cost-effectiveness, which is critical for equitable implementation, particularly in under-resourced communities. This review also excluded studies written in non-English languages, which may have introduced language bias and limited the inclusion of relevant international research.

Ethical concerns remain unresolved, including patient data ownership, privacy, and liability—whether it falls on the clinician, AI developer, or healthcare institution. Regulatory ambiguity further complicates adoption: while the US Food and Drug Administration (FDA) has approved some AI-based cardiac tools under the Software as a Medical Device (SaMD) framework, ongoing model updates, reimbursement, and accountability remain underregulated [32]. The responsible integration of AI in AFib care will require not only technical performance but also transparency, inclusivity, and alignment with evolving legal and ethical standards.

Future research should focus on improving the reliability, scalability, and equity of AI models in AFib management. Advancing these tools can enhance diagnostic accuracy and timeliness while also improving healthcare system efficiency and patient outcomes. To ensure clinical applicability, studies should prioritize the detection of underrepresented and complex arrhythmias, aiming to strengthen model sensitivity and specificity across diverse populations. The development of interpretable AI models—using techniques such as SHAP values, saliency maps, or attention mechanisms—can provide clinicians with greater insight into algorithmic decision-making, fostering trust and adoption. Moreover, standardized reporting using AI-specific checklists, such as CONSORT-AI and SPIRIT-AI, is essential to ensure consistency in metrics, dataset transparency, and model disclosure [33]. Equity-focused algorithm design should include publicly available datasets annotated with race, sex, and socioeconomic metadata to support reproducible and inclusive model training. Extensive, multicenter, longitudinal studies involving diverse populations are also needed to evaluate long-term clinical outcomes and generalizability. Lastly, future research should assess how AI outputs are integrated into electronic health records (EHRs), influence clinician decision-making, and engage patients in their care workflows.