Background The integration of Artificial Intelligence (AI) in electrocardiographic (ECG) devices has become a pivotal area of research, particularly during the COVID-19 pandemic. These technologies are essential for enhancing cardiac diagnosis and monitoring. Methods This study assesses current trends, key contributors, and collaborative networks in the field of AI-enhanced ECG devices. We utilized a comprehensive analysis, using the Biblioshiny library from Bibliometrix for data exploration of data extracted from the Scopus database and VOSViewer for creating and visualizing maps. These tools were played an important role in conducting an in-depth analysis of the relationships and developments within the field. Results The analysis shows a significant increase in publications related to AI-enhanced ECG devices, with a marked surge during the COVID-19 pandemic. Despite the growing interest and technological advancements, the study exposes a notable disparity in the geographical distribution of research contributions, highlighting substantial under-representation of African researchers. This gap is attributed to infrastructural, financial constraints, and limited collaborative networks within the continent. Conclusion The rapid evolution and increasing importance of AI in ECG devices underscore the need for more inclusive research practices. There is a critical need to integrate and promote contributions from under-represented regions, particularly Africa, to ensure a globally diverse perspective in tackling health challenges. This study calls for enhanced participation and support for African researchers to bridge the existing research gap and foster global health equity.

The integration of Artificial Intelligence (AI) in electrocardiographic (ECG) devices has become a pivotal area of research, particularly during the COVID-19 pandemic. These technologies are essential for enhancing cardiac diagnosis and monitoring. This study assesses current trends, key contributors, and collaborative networks in the field of AI-enhanced ECG devices. We utilized a comprehensive analysis, using the Biblioshiny library from Bibliometrix for data exploration of data extracted from the Scopus database and VOSViewer for creating and visualizing maps. These tools were played an important role in conducting an in-depth analysis of the relationships and developments within the field. The analysis shows a significant increase in publications related to AI-enhanced ECG devices, with a marked surge during the COVID-19 pandemic. Despite the growing interest and technological advancements, the study exposes a notable disparity in the geographical distribution of research contributions, highlighting substantial under-representation of African researchers. This gap is attributed to infrastructural, financial constraints, and limited collaborative networks within the continent. The rapid evolution and increasing importance of AI in ECG devices underscore the need for more inclusive research practices. There is a critical need to integrate and promote contributions from under-represented regions, particularly Africa, to ensure a globally diverse perspective in tackling health challenges. This study calls for enhanced participation and support for African researchers to bridge the existing research gap and foster global health equity.

Background The integration of Artificial Intelligence (AI) in electrocardiographic (ECG) devices has become a pivotal area of research, particularly during the COVID-19 pandemic. These technologies are essential for enhancing cardiac diagnosis and monitoring. Methods This study assesses current trends, key contributors, and collaborative networks in the field of AI-enhanced ECG devices. We utilized a comprehensive analysis, using the Biblioshiny library from Bibliometrix for data exploration of data extracted from the Scopus database and VOSViewer for creating and visualizing maps. These tools were played an important role in conducting an in-depth analysis of the relationships and developments within the field. Results The analysis shows a significant increase in publications related to AI-enhanced ECG devices, with a marked surge during the COVID-19 pandemic. Despite the growing interest and technological advancements, the study exposes a notable disparity in the geographical distribution of research contributions, highlighting substantial under-representation of African researchers. This gap is attributed to infrastructural, financial constraints, and limited collaborative networks within the continent. Conclusion The rapid evolution and increasing importance of AI in ECG devices underscore the need for more inclusive research practices. There is a critical need to integrate and promote contributions from under-represented regions, particularly Africa, to ensure a globally diverse perspective in tackling health challenges. This study calls for enhanced participation and support for African researchers to bridge the existing research gap and foster global health equity.

Cardiovascular diseases remain a major global health challenge, highlighting the need for accessible, intelligent, and real-time cardiac monitoring solutions. This paper presents Moroccan VitaTrack GeoMed, a patented Made-in-Morocco 12-lead ECG monitoring prototype integrating embedded signal acquisition, artificial intelligence, real-time visualization, and screening support. The proposed framework combines ECG preprocessing, a new hybrid CNN–Transformer architecture for local morphological feature extraction and long-range temporal dependency modeling, and an ensemble-based meta-classification module designed to improve prediction robustness under practical operating conditions. The proposed classification framework was retrospectively evaluated using 7536 12-lead ECG recordings from a publicly available dataset, covering nine ECG categories. Across ten experimental runs, the framework achieved a micro-F1 of 91.76 ± 0.23%, a macro-F1 of 87.35 ± 0.19%, and a micro-AUPRC of 96.46 ± 0.13%. The highest F1-scores were obtained for SR (95.40 ± 0.22%) and SB (95.14 ± 0.23%). AFL, ST, AF, and TWC achieved F1-scores between 86.72 ± 0.39% and 90.80 ± 0.42%, while MI, SA, and MCA showed more moderate performance at 79.42 ± 0.46%, 80.37 ± 0.48%, and 79.94 ± 0.67%, respectively, possibly due to overlapping ECG patterns and class heterogeneity. Separately from this retrospective algorithmic evaluation, the framework was integrated into the Moroccan VitaTrack GeoMed prototype and assessed through a one-month preliminary technical feasibility study involving a single healthy volunteer (the principal author). A total of 42 repeated ECG recording sessions were conducted under six monitoring conditions, of which 38 produced analyzable recordings, corresponding to a technical session consistency rate of 90.5%. Comparison with a conventional 12-lead ECG system demonstrated relative measurement differences below 3% and a signal correlation coefficient of 0.93. These findings support the preliminary engineering feasibility of the integrated platform for real-time ECG acquisition, processing, visualization, and screening-support functionality. Importantly, the reported classification metrics were obtained from retrospective database recordings and do not represent diagnostic performance on pathological ECG signals acquired directly using the prototype. Further prospective clinical studies involving independent participants and patients with documented cardiac abnormalities are required to establish diagnostic validity, clinical utility, and generalizability before routine clinical use.