Artificial intelligence for cardiology: from diagnosis to management

Abstract

Artificial intelligence (AI) and machine learning are rapidly transforming cardiac electrophysiology, offering new avenues for diagnosing, managing, and treating cardiac arrhythmias. These technologies leverage diverse data sources, including clinical records, imaging, and electrical waveforms, to support decision-making and optimize outcomes, particularly in procedures such as cardiac ablation. This scoping review explores the evolving role of AI in cardiology, emphasizing its applications in diagnostics, predictive analytics, and procedural innovations. It also examines the collaborative dynamics of interdisciplinary teams, highlighting how professionals, such as electrophysiologists, computer scientists, clinicians, nurses, perfusionists, and technologists, contribute to identifying and solving key challenges in the field. The integration of AI into cardiology is not only enhancing diagnostic precision and patient outcomes but also streamlining healthcare delivery. As technological capabilities expand, AI is poised to play an increasingly central role in preventive cardiology, enabling more accurate risk assessments, earlier interventions, and the promotion of healthier lifestyles. However, the successful implementation of AI requires thoughtful coordination across disciplines and a clear understanding of its limitations and ethical considerations. This review underscores the importance of fostering interdisciplinary collaboration and aligning AI innovations with clinical needs. It also identifies barriers to adoption and proposes strategies for integrating AI tools into routine practice. Ultimately, the findings aim to guide stakeholders, including researchers, clinicians, and policymakers, in advancing the development and application of AI systems in cardiology. By doing so, the healthcare community can move toward reducing the global burden of cardiovascular disease and improving population health. The insights presented here, after a review of 142 studies, offer a roadmap for future research and clinical integration, ensuring that AI continues to serve as a catalyst for innovation and excellence in cardiac care.

Plain language summary

Plain language summary

Artificial intelligence for cardiology: from diagnosis to management

This review paper explores the integration of artificial intelligence (AI) in cardiology, focusing on its applications in diagnosis, risk assessment, monitoring, and treatment. We discuss advancements in AI-driven diagnostic tools, predictive models for risk stratification, wearable devices for real-time monitoring, and personalized treatment plans. Ethical considerations, data privacy concerns, and technical challenges associated with AI implementation in clinical practice are also addressed.

Graphical abstract:

Artificial intelligence applications in cardiac care.

Introduction

Cardiovascular diseases (CVDs) remain the leading cause of death globally, accounting for an estimated 31% of all deaths worldwide. ¹ According to the World Health Organization, approximately 17.9 million people die from CVDs each year. Of these deaths, about 85% are due to heart attacks and strokes. The prevalence of CVDs is increasing due to various lifestyle risk factors such as unhealthy diet, physical inactivity, obesity, smoking, ² and excessive alcohol consumption. The global burden of CVDs is expected to rise in developing countries due to population growth and aging. Despite global advancements in prevention and treatment, the economic impact of CVDs is substantial, with global costs estimated to reach $1,044 billion by 2030. ³ While numerous expensive treatment options offer minimal additional benefits, healthcare funding constraints have diminished financial flexibility in healthcare systems.^4,5 These problems are aggravated by an overwhelming inflow of digital information, complexities of data analysis, and quantitative pattern recognition. Artificial intelligence (AI) presents a potential solution to many of these problems.

Globally, the last decade has seen numerous applications of AI in cardiology, and such techniques have been applied in cardiovascular medicine to explore novel treatment plans for existing diseases, improve the quality of patient care, enable cost-effectiveness, and reduce readmission and mortality rates. The emergence of AI in healthcare has revolutionized the early detection of CVDs by enabling faster, more precise, and cost-effective diagnosis,^6,7 and the integration of AI into preventive cardiology marks a significant advancement in how cardiovascular health is approached (Table 1). AI is a nontechnical, popular term that refers to machine learning (ML) of various types, but most often to deep neural networks. These deep neural networks provide intelligence with highly focused skills and are increasingly being integrated into various medical fields, including cardiology.

Table 1.

Summary of key AI tools and integration framework in cardiology.

Framework element	Description	Application in cardiology	Clinical adoption	Strengths	Challenges
Natural Language Processing	Extracts insights from clinical text and patient records	Summarizing echocardiogram reports, identifying risk factors	Widely used in diagnostic reporting	Streamlines documentation; reveals hidden trends	May misinterpret nuanced cardiac terminology
Computer Vision	Processes visual data from medical imaging	Coronary angiography analysis, echocardiogram interpretation	Assists in visual interpretation	High precision in image-based diagnosis	Requires extensive labeled datasets
Predictive Analytics Models	Uses historical data to forecast patient outcomes	Predicting heart failure readmission, risk of myocardial infarction	Assists in clinical decision-making	Enables early interventions	Dependent on data quality and completeness
Clinical Decision Support	Offers diagnostic and therapeutic recommendations	Alerts for contraindicated cardiac medicines, care pathway optimization	Useful in diagnosis	Supports evidence-based cardiology care	Integration with existing EHR systems may be limited
Generative AI	Generates clinical content and patient-facing materials	Drafting discharge summaries, personalized lifestyle advice	Time-saving initiative	Enhances productivity and communication	Needs robust validation and clinician oversight
Federated Learning	Facilitates collaborative model training across institutions	Multi-center studies on arrhythmia or heart disease patterns	Supports training and education	Preserves patient privacy; broadens data diversity	Technically complex; standardization issues
Integration Framework	Combines protocols for governance, data flow, validation, and training	Hospital-wide AI adoption for cardiology services	Supports research	Enables systematic and scalable implementation	Requires leadership buy-in and resource allocation

AI, artificial intelligence; EHR, electronic health record.

Specifically, in the discipline of cardiac electrophysiology, several clinical, imaging, and electrical waveform data are considered in the diagnosis, prognosis, and management of cardiac arrhythmias and have often culminated in cardiac ablation procedures. The integration of AI and ML in cardiology entails close collaboration among members of the multidisciplinary team (MDT), such as electrophysiologists, computer scientists, clinicians, nurses, perfusionists, clinical technologists, and other users. Of importance is the fact that the integration of AI in the discipline of cardiac electrophysiology still allows for significant clinical, imaging, and electrical waveform data to be considered by these members of the MDT in the decision-making diagnosis, prognosis, and management of cardiac arrhythmias. Notably, AI practices in cardiology serve not only to enhance diagnostic accuracy and improve patient outcomes but also assist in streamlining healthcare processes. The efficacy of AI in cardiology depends on several critical elements, including high-quality data, sophisticated algorithms, clinical integration, and adherence to regulations. Decision-making and collaboration among MDTs, consisting of electrophysiologists, computer scientists, clinicians, and other healthcare experts, is ultimately crucial for patient management and interventional issues. Despite AI’s promising potential, obstacles such as data privacy concerns, bias, and the necessity for ongoing innovation and regulatory oversight must be addressed to guarantee the safe and effective implementation of AI-driven solutions. It is also important to note that, while AI may help with decision-making and risk stratification, clinical decision-making by the practitioner, healthcare provider, or clinician is dependent on the patient’s condition and is still considered superior. Suffice to say, the use of AI in healthcare must be combined with an expert guarantor from the healthcare provider. As technology continues to advance, AI’s role in cardiology is expected to grow, leading to even more innovations in patient care and management. ⁸

AI is ushering in a new era of preventive cardiology by providing tools that enhance risk assessment, facilitate early intervention, and promote healthier lifestyles. Similarly, technology continues to evolve, and AI integration in cardiology holds the promise of significantly reducing the burden of CVDs, leading to healthier populations and improved quality of life. The field of AI in modern medicine, while rapidly growing, is still a field that has significant hurdles in clinical validation and implementation that prevent universal application. Thus far, literature searches have remained limited on discussions related to risk factors like overreliance on AI and the potential to erode clinical judgment, and the potential for technical failures in AI-dependent systems.

Therefore, this review from a national and international perspective will discuss AI’s role in technological innovations related to cardiological procedures while addressing challenges in its implementation, and outline future directions for research and clinical practice.

Methodology

Databases including PubMed, Web of Science, Scopus(Science direct), and IEEE Xplore were searched using keywords and concepts such as Artificial Intelligence in Cardiology OR AI in Cardiovascular Diagnosis OR Machine Learning in Cardiology OR AI in Cardiac Imaging OR AI in Heart Disease Management OR Predictive Analytics in Cardiology OR AI in Cardiac Risk Assessment OR AI in Cardiac Monitoring OR AI in Cardiovascular Treatment) related to studies published between 2015 and 2024 (Figures 1–3). Meta-analyses, RCTs, and systematic reviews were included in the results filter. Only English-language literature totaling 142 publications was utilized for this review. Two authors separately carried out the full-text screening, title/abstract screening, and database search. A third author was consulted to reconcile any differences.

Figure 1.

PRISMA flowchart illustrating the study selection process. The diagram outlines the number of records identified, screened, assessed for eligibility, and included in the final synthesis (n = 142), following database searches across PubMed, Web of Science, Scopus, and IEEE Xplore between 2015 and 2024.

Figure 2.

A visualization of literature from PubMed, Web of Science, Scopus, and IEEE Xplore.

Figure 3.

The number of published papers on artificial intelligence in cardiology.

AI in cardiac electrophysiology

Cardiovascular medicine has emerged as a key domain for AI applications, and considerable efforts have been made by clinicians to integrate these innovations into routine clinical practice. Within cardiac electrophysiology, AI applications, in recent years, especially in cardiac interventional procedures such as electrophysiology, have garnered substantial attention in existing literature. ⁹ Transformation in cardiac electrophysiology has been revolutionized by AI, because of its ability to improve diagnosis, treatment, and monitoring of heart rhythm disorders (Figure 4). Its ability to analyze large datasets and detect patterns beyond human capabilities makes it invaluable in the field. Although cardiac electrophysiology is rapidly evolving with advancements and applications in AI, it is important to understand the role of AIs and their application to electrophysiology in the context of detection, prediction, and management of cardiac arrhythmias and other heart rhythm disorders (Table 2).^8,10

Figure 4.

Potential applications of AI in cardiovascular medicine.

AI, artificial intelligence.

Table 2.

Major applications of artificial intelligence in electrophysiology.

Key applications of AI in electrophysiology	Explanation	References
Diagnosis and risk stratification in patients with a history of cardiac pathology	AI algorithms can analyze vast amounts of data from ECGs, cardiac imaging studies, and cardiac wearable devices, such as intracardiac defibrillators, to detect subtle patterns associated with arrhythmias, such as: • Atrial fibrillation, whereby silent or intermittent episodes or heart rhythm disorders are missed by standard diagnostics • Risk prediction, whereby AI can predict the probability of adverse cardiac events such as sudden cardiac death or ventricular arrhythmias by sourcing and integrating patient data from ECGs and other diagnostic tests	Kabra et al. 1
ECG interpretation	ECG analysis and interpretation is made readily available to clinicians so that identification and treatment of abnormalities such as PVCs; complex arrhythmias can assist in saving lives and reduce diagnostic errors	Riaz Gondal et al. 11
Personalized treatment planning	Management and treatment of cardiac interventions become personalized with AI as follows: Catheter ablation Algorithms analyze electro-anatomical mapping data to identify the origin of arrhythmias and predict the success of ablation procedures Device therapy AI helps identify patients who would benefit most from implantable devices such as pacemakers and implantable defibrillators Medication selection AI tools can predict how individual patients may respond to antiarrhythmic drugs, thereby minimizing trial-and-error approaches	Bahlke et al. 12
Intracardiac mapping and ablation guidance	In complex arrhythmia such as atrial or ventricular fibrillation, by analyzing high-density electro-anatomical maps to identify critical areas in the heart that have the tendency to produce abnormal cardiac rhythms and enhance precision during ablation procedures. This is not only time-saving but also improves long-term outcomes	Alhusseini et al. 13
Imaging in electrophysiology	AI enhances imaging techniques used in cardiac electrophysiology, such as MRI and CT scans to assess myocardial scar tissue and other structural abnormalities, helping electrophysiologists plan interventions. Procedures such as ultrasounds and radiology with AI-guided algorithms improve the accuracy of intracardiac echocardiography used during ablation	Wang et al. 14
Predictive modeling for outcomes	AI models use historical patient data to predict long-term outcomes, such as the recurrence of arrhythmias after ablation, device performance, and the risk of developing new cardiac conditions	Chou and Lin 15

AI, artificial intelligence; ECG, electrocardiogram; PVC, premature ventricular contraction.

AI in early detection of CVDs

With AI utilization in healthcare, early detection of cardiac issues has become more precise and accessible. While early detection undoubtedly plays a critical role in the prevention of complications, thereby improving patient outcomes, AI-driven tools, such as ML algorithms, predictive analytics, and wearable devices, are all transforming how cardiovascular conditions are identified and managed by clinicians at an early onset. ¹⁶ The following segment details some of the ways in which AI has played a role in cardiovascular, medical, and surgical management.

ECG analysis

Electrocardiograms are central to the diagnosis of cardiac pathology, and often, manual interpretation can be open to human error. It has been noted that AI-powered algorithms analyze ECGs with remarkable accuracy, detecting irregularities such as atrial fibrillation, arrhythmias, and early signs of heart failure. ¹⁷ This kind of efficiency with AI reduces diagnosis time and enhances the proficiency of healthcare professionals.

Medical imaging

The last few decades have seen AI significantly improve the analysis of medical imaging techniques, such as echocardiograms, cardiac MRIs, and CT scans, and detecting early signs of coronary artery disease (CAD), structural heart abnormalities, and valve disorders with higher precision than traditional methods. These AI-powered imaging tools not only assist cardiologists in identifying disease patterns more effectively but also reduce false positives and negatives in diagnoses. This diagnostic accuracy can also ensure timely medical intervention. ¹⁸

Remote monitoring

The integration of AI with wearable technology has enabled continuous monitoring of cardiovascular health even remotely. Smartwatches and fitness trackers equipped with AI-driven algorithms can not only track heart rate variability but also detect arrhythmias and identify fluctuations in blood pressure. These remote AI-driven monitoring wearable devices benefit high-risk patients, reducing hospital admissions and visits, and allow for the provision of consistent health data for early diagnosis. ¹⁹

Predictive analytics and risk assessment

Predictive analytics play a vital role in assessing CVD risk factors by analyzing a patient’s medical history, lifestyle factors, genetic data, and biomarkers. Furthermore, AI can predict the likelihood of developing CVD. ²⁰ Analytics of this nature help healthcare professionals to design personalized treatment regimens, such as lifestyle modification plans, as well as early pharmacological interventions. ¹⁶

Factors contributing to the success of AI utilization in cardiology

The successful implementation of AI in cardiology depends on high-quality data, advanced algorithms, clinical integration, regulatory approval, user trust, and continuous innovation. ²¹ However, the success of AI utilization in medical or surgical cardiac intervention depends on several key factors. These factors can be characterized into technological, clinical, regulatory, and human-related factors as follows.

Technological factors

Here, factors such as data quality and availability play a vital role as AI models require vast amounts of high-quality, diverse, and representative datasets for accurate predictions. In addition, data standardization is necessary, whereby uniform data formats and well-labeled data can advance AI effectiveness. AI-powered algorithms have shown extraordinary developments in the analysis of ECGs, allowing for rapid and accurate interpretation of these vital diagnostic tests. Notably, AI has also significantly impacted echocardiographic imaging. This has enabled more precise and detailed analysis of cardiac anatomical structures and their functioning. While AI algorithms use echocardiographic data to assess conditions such as left ventricular function or to predict CAD, they provide immense value in aiding clinicians by assisting in time-consuming tasks to providing accurate assessments, and contribute to informed diagnostic decision-making.

Equally important is access to real-time data, such as integration with electronic health records (EHRs), imaging systems, and wearable devices undoubtedly enhances real-time decision-making. Notably, real-time AI applications in wearable devices and bedside monitoring systems require efficient computing resources as they aid in analyzing clinical notes, medical literature, and guidelines for better diagnosis and treatment plans. ²²

Clinical integration factors

AI in the clinical setting assists clinicians without disrupting workflow, providing decision-making support, and reducing the workload in report generation, triaging, and monitoring critically ill patients. Although AI-driven decisions increase patient confidence in automated diagnoses and treatment plans, AI should assist rather than dictate decisions, allowing physicians and patients to collaborate on treatment choices. ²³

Regulatory factors

While AI applications in cardiology must meet regulatory requirements for safety, efficacy, and clinical validation, AI models should provide interpretable results to ensure clinicians trust and understand their recommendations. ²⁴ It becomes mandatory that cardiologists themselves be trained in AI-based applications and tools to help integrate them effectively into practice. Applications related to AI must be tested in diverse populations to ensure reliability and effectiveness, and all AI systems should incorporate feedback from clinicians on an ongoing basis to refine performance and improve recommendations. ²⁵

Cost-effectiveness and accessibility factors

Though AI can reduce diagnostic errors and prevent unnecessary hospitalizations, it can be easily accessible, especially in resource-burdened areas through initiatives such as telemedicine and mobile health. Continuous monitoring for early symptom detection with the use of wearable AI devices eliminates hospitalization and prevents disease complications. ²⁶

Challenges and limitations

It is globally accepted that AI is revolutionizing modern medicine, particularly in the field of cardiology. However, despite its potential, AI applications and operations in cardiology face several challenges (Figure 5). These challenges range from data limitations and ethical and regulatory concerns to clinical integration. Addressing the following challenges is crucial to ensuring AI-driven solutions are safe, effective, and widely accepted.

Figure 5.

Future directions and challenges for AI-driven applications in cardiac care.

AI, artificial intelligence.

Notwithstanding the emergence of AI as a transformative tool in cardiology, which has enabled machines to analyze large datasets and assist in cardiology decision-making, cardiologists must remain vigilant and cross-check all AI outputs with cardiological expertise.

It is therefore imperative that cardiologists balance AI tools with rigorous clinical expertise to ensure optimal patient care and prioritize the avoidance and possibility of human oversight, particularly in critical decision-making.

Data-related challenges

Cardiology data often suffers from inconsistencies such as missing values and variations in data collection methods. Therefore, the lack of standardized data across hospitals and healthcare systems makes it difficult to develop AI models that perform consistently, making it imperative that AI models have huge amounts of high-quality, well-labeled data for training and validation. ²⁷

Bias and generalizability

AI models trained on datasets that are not diverse may introduce biases, leading to inaccurate predictions for certain populations. An AI model with data input from a country with different demographics will not be diversely representative in different demographic or socioeconomic groups. ²⁸

Data privacy and security

Patient data privacy is a significant factor in AI-driven cardiology, and all regulations and protocols should be upheld to reduce the risk of data breaches and cyberattacks. ²⁹

Technological challenges

Technological concerns often surface when there is a lack of explanations regarding AI algorithms, as most cardiologists have limited training in AI. This becomes essential when clinicians need a rationale for their decision-making, especially as it concerns human lives. ¹⁴ The diverse applications of AI in cardiology have showcased the transformative potential of this technology. Globally, the convergence of AI with remote monitoring technologies and wearable devices has expanded the horizons of cardiac healthcare. This has not only enabled real-time monitoring outside clinical settings but also offered the potential for timely detection of abnormalities, diagnosis, early intervention, and individualized management of life-threatening cardiovascular conditions, thereby improving long-term outcomes of the patient.

Integration with current trends in healthcare systems

The adoption of AI can become challenging if hospital management systems are outdated or incompatible. This can cause a disruption in existing workflows, requiring training and development. Additionally, the utilization of AI in cardiology requires significant resources, including high-performance server’s real-time applications. Healthcare facilities, especially in low-resource settings, lack the infrastructure and resources to support these requirements, which further limits the adoption or implementation of AI. ³⁰

Ethical and regulatory challenges

While AI-driven decision-making in cardiology advances ethical questions regarding the accountability, liability, and responsibility of the clinician, ethical considerations and challenges must be addressed to ensure AI remains a reliable and equitable tool in CVD detection. Ethical guidelines and legal frameworks require extensive clinical validation, and it is crucial to address these concerns, such as healthcare providers ensuring transparency and obtaining informed consent when incorporating AI into patient care. ³⁰ It is also vital that AI models be regularly updated and re-evaluated to ensure continued compliance with evolving healthcare regulations. ML-based models are often constrained by ethical concerns. ³¹ The participation of minority groups in clinical databases has been hindered by systemic biases. Developing ML algorithms using biased datasets not only reduces the effectiveness of ML as a diagnostic and treatment tool due to limited generalizability but also risks perpetuating societal inequalities.

It is crucial to ensure that the advantages are fairly distributed across individuals of various genders, races, ethnicities, cultures, and socioeconomic backgrounds. ³² As supervised ML models require labeled datasets to function, substantial human effort is needed for data labeling, which increases the likelihood of introducing bias in predictions. Additionally, selection, confirmation, and sampling biases can affect AI algorithm predictions in clinical applications. To address these biases, careful consideration is necessary at every stage of AI development to ensure fairness and minimize disparate impacts. Addressing these challenges requires a multidisciplinary approach involving collaboration between cardiologists, AI researchers, policymakers, and regulatory bodies.

Clinical and human-related challenges

AI could lead to a decline in clinical reasoning and decision-making skills among cardiologists; therefore, AI should be positioned as a supportive tool rather than a replacement for human clinicians. This will reduce overreliance and reduce clinical judgment on AI. Overall, human judgment remains central to patient care. ³³ Although advancements have been made, the application of AI remains constrained by ongoing challenges. In heart failure, one issue is the multifaceted nature of variables. An abundance of variables can negatively affect model efficacy. ³⁴ To address this, researchers consolidate features into broader categories, such as organizing diagnostic codes into hierarchical groups or classifying drugs into parent categories. While this approach enhances the likelihood of features being predictive without compromising the model, it may influence performance and interpretation. Conversely, if predictive features are scarce, ML algorithms risk overfitting, limiting their applicability.^35,36 External validation is crucial before implementing ML algorithms, as prediction models may underperform on external datasets. It is worth noting that most AI studies lack external validation. These shortcomings can potentially be mitigated by conducting validation across multiple independent datasets or diverse data modalities (e.g., datasets from various regions and hospitals) for feature selection and predictive analytics. Prediction models may erroneously correspond or be tailored to peculiarities in the development dataset. As we progress toward implementing predictive modeling in clinical practice to personalize care, existing methods or proposed approaches must undergo systematic evaluation using datasets that encompass diverse populations from real-world clinical environments. A further constraint is the accessibility of patient or population variables used in AI algorithms. ³⁷ Some developed models incorporate numerous variables or those rarely gathered in clinical settings, making their practical application uncertain. The implementation of automated EHR systems in heart failure management can boost efficiency as they continuously update with new data. Additional research is needed to improve existing data processing methods, imputation techniques, and develop new data preparation approaches to effectively utilize ML models.

Future perspectives of AI use in cardiology

The integration of AI and language models into cardiology has the potential to transform medical diagnosis and decision-making processes. ³⁸ By leveraging these technologies to analyze extensive medical datasets, healthcare professionals can receive valuable assistance in making more precise diagnoses and treatment choices. One significant application of AI and language models in cardiology involves the examination of medical imaging, such as echocardiograms, angiograms, and ECGs. AI algorithms can be trained to detect patterns within these images that might be difficult for human clinicians to discern. For example, AI systems can be employed to detect irregularities in ECGs that could indicate conditions like heart failure, ischemic heart disease, or arrhythmia. ³⁹ Beyond diagnostic applications, AI and language models can contribute to decision-making processes in cardiology by examining medical information and suggesting tailored treatment plans.^3,40 AI systems can evaluate patient records to determine the most suitable therapeutic approaches for specific cardiovascular issues, considering variables such as the individual’s age, health background, and genetic makeup.

Furthermore, AI technology can be employed to supervise patients with cardiac implants like pacemakers or defibrillators, notifying healthcare professionals of potential complications or device malfunctions.^41,42 Tsiachristas et al. ⁴³ conducted a recent investigation into the use of coronary computed tomography angiography (CCTA) as an initial diagnostic tool for chest pain in patients suspected of having obstructive CAD. However, it is worth noting that many acute cardiac events occur without the presence of obstructive CAD. This study assessed the long-term cost-effectiveness of incorporating a cutting-edge AI-enhanced image analysis algorithm (AI risk) into routine CCTA interpretation. This algorithm measures the risk of cardiac events by measuring coronary inflammation, along with the extent of coronary artery plaque and clinical risk factors, through analysis of CCTA images. The findings suggest that adding AI risk assessment to standard CCTA interpretation is economically viable, as it enhances risk-guided medical management.

The advent of stethoscopes with ECG capabilities (Figure 6), which can record single-lead ECGs during standard auscultation, presents an opportunity to utilize AI-ECG for screening at the point of care. This approach depends not only on the algorithm’s accuracy when using single-lead ECG data, but also on the ease and consistency of recording high-quality inputs suitable for AI-ECG interpretation. Research has shown that AI-ECG can detect patients with a left ventricular ejection fraction of 40% or below using single-lead ECG inputs. This is achieved through an AI algorithm integrated into an ECG-enabled stethoscope, a familiar clinical instrument that fits seamlessly into standard and universal clinical workflows. Considering the numerous clinical encounters of undiagnosed patients prior to their initial hospital admission for heart failure, the stethoscope examination could serve as a point-of-care screening opportunity. Furthermore, with additional AI algorithms, it has the potential to evolve into a comprehensive tool for detecting CVDs. ⁴²

Figure 6.

Illustration of ECG-enabled stethoscope and AI-integrated ECG.

Source: Adapted with permission from Bachtiger et al. ⁴² Copyright (2022). Elsevier.

AI, artificial intelligence; ECG, electrocardiogram.

One potential issue with language models is the risk of patients self-diagnosing and treating their own symptoms without consulting a cardiologist. However, it is noteworthy that AI-powered ChatGPT-4, when asked, did not propose any medications or treatments but instead advised seeking professional medical help. ³⁸ Language models can assist healthcare professionals in formulating appropriate questions for patient consultations and identifying high-risk individuals. By analyzing symptoms, medical histories, and other pertinent data, these models can aid healthcare providers in narrowing down possible diagnoses and recommending suitable treatment strategies.

While these advantages are significant, several important factors must be considered when employing language models. The accuracy of their predictions and recommendations is constrained by the quality and quantity of their training data. In addition, since language models rely solely on provided information, they may exhibit bias and fail to consider all relevant factors influencing a patient’s diagnosis or treatment plan. Furthermore, these models might struggle to account for the intricacies and complexities of individual cases, particularly rare conditions or atypical presentations, potentially leading to inaccurate diagnosis.^44,45

Conclusion

The incorporation of AI into cardiology, especially in cardiac electrophysiology, has transformed the field by enhancing diagnostic precision, improving patient outcomes, and optimizing healthcare processes. AI’s capacity to examine extensive clinical, imaging, and electrical waveform data has proven crucial in diagnosing, prognosticating, and managing cardiac arrhythmias, often resulting in more accurate and efficient cardiac ablation procedures. This technological progress not only enables early detection and intervention but also fosters individualized treatment strategies, thus diminishing the impact of CVDs and enhancing patients’ quality of life.

As technology advances, AI’s role in cardiology is anticipated to grow, leading to further breakthroughs in patient care and management. The ongoing development and application of state-of-the-art AI systems in cardiology will demand a coordinated effort from all stakeholders, including researchers, clinicians, policymakers, and regulatory authorities. By embracing these advancements, the medical community can significantly reduce the burden of CVDs, ultimately resulting in healthier populations and improved patient outcomes. While AI is set to play a transformative role in the future of cardiology, offering new possibilities for early detection, personalized treatment, and enhanced patient care, the continued exploration and refinement of AI applications in cardiology will pave the way for a new era of preventive and precision medicine, benefiting both patients and healthcare providers likewise.