In the twenty-first century, CVDs remain the leading cause of illness and death worldwide (J. Sun et al., 2023). Despite major strides in medical research and healthcare, cardiovascular diseases (CVDs) are on the rise, necessitating new strategies aimed at prevention, early detection, accurate diagnosis, personalized therapy, and enhanced management approaches (Çetin, 2022). Researchers identify artificial intelligence (AI) as a disruptive force in cardiology, with the potential to completely revolutionize the diagnosis and treatment of cardiovascular diseases (Udriște et al., 2024). Examples of artificial technologies or artificial intelligence (AI) include deep learning and other techniques like deep learning, natural language processing, and statistical analyses. These tools have significantly progressed to enhance human decision-making processes regarding important issues concerning cardiac health (Kapoor et al., 2022). Until now, conventional approaches have been used to identify and manage cardiovascular diseases.

Accuracies, late detection, and individualized therapy plans (Rao et al., 2024). However, AI technology has brought about a new era of predictive algorithms, data-driven insights, and patient-specific treatments (Olawade, Teke, et al., 2024).

This encompasses wearable devices for genetic profiling, continuous monitoring systems, imaging investigations, and electronic health records (EHRs), which contain vast amounts of data utilized by cardiology through artificial intelligence. Essentially, these are large, complex databases that form the basis of algorithms based on artificial intelligence that can learn from them, adapt to them, and make sense of them (Lopez-Jimenez et al., 2020). The goal is to integrate AI into cardiology practice in order to enhance clinical decision-making abilities, improve diagnostic accuracy, and ensure proper treatment decisions, thus leading to better patient outcomes (Van den Eynde et al., 2023). Therefore, solutions powered by AI could make doctors more competent, simplify processes, reduce diagnostic errors, and offer opportunities for preventive interventions, thereby enabling effective personalized care for different patients' progress and the integration of artificial intelligence (AI) innovations with cardiology have resulted in significant breakthroughs across multiple fields. Diagnostic algorithms now correctly classify cardiovascular diseases by assessing ECGs, cardiograms, and cardiac imaging, among other tests (Romiti et al., 2020).

Additionally, AI combines a variety of patient data, including images, genetic profiles, lifestyle variables, and treatment plans (Alghamdi & Alashban, 2023). The integration of AI with wearables and remote monitoring technology has enabled continuous real-time monitoring outside of clinical settings (Johnson et al., 2021). These technologies enable quick diagnosis of anomalies, early intervention, or targeted therapy for chronic cardiovascular conditions, thereby enhancing patient engagement and long-term outcomes.  As AI-driven technologies advance, this could radically change cardiac research and clinical practice (Kashyap).

Accuracy, effectiveness, and improved patient-centered care are the foundation of the new approach to cardiovascular healthcare brought about by artificial intelligence (AI). These include improvements in drug studies and development, risk assessment, machine learning—AI technology—and clinical decision support systems. However, integrating AI into cardiology requires overcoming several challenges. Consequently, it is necessary to So, we need to talk about some important issues related to data privacy, algorithmic biases, following the rules, and moral concerns if we want to successfully add AI innovations to the healthcare platforms we already have (Krishnan Ganapathy, 2021). This paper describes the various ways that artificial intelligence (AI) is changing the field of cardiology and emphasizes the uniqueness and critical importance of doing so.

The research employed an organized and systematic strategy to evaluate the present state of applications utilizing artificial intelligence in the field of cardiology. The approaches encompassed many key stages that guaranteed a comprehensive and meticulous analysis of literature, with a specific focus on the incorporation of artificial intelligence (AI) into various facets of cardiovascular care.

Literature Search Strategy

Several databases were utilized to get the essential scientific publications, research papers, and peer-reviewed journals. The databases searched encompassed PubMed/MEDLINE, ScienceDirect, IEEE Xplore, and Web of Science. To encompass a broad range of pertinent material, the search strategy for AI and Cardiology incorporated diverse keywords of AI in the field of cardiology. Examples of artificial intelligence (AI) include the fields of cardiology, cardiovascular diseases, AI in cardiology, diagnostics, precision medicine, remote patient monitoring, and therapeutic interventions. By combining and narrowing down the search terms using Bowe improved the search to be more specific by combining and narrowing down the search terms using Boolean operators like AND, OR. -published studies, specifically focusing on those conducted between 2000 and December 2023. By excluding non-peer-reviewed information like editorials, letters, conference papers, and letters, we were able to maintain high-quality criteria for the reviewed literature.

Selection Criteria

To guarantee the inclusion of papers that particularly focused on the use of AI in cardiology, a rigorous set of selection criteria was employed for this literature analysis. We established a number of specific criteria for inclusion to achieve this goal. Initially, we required the papers to focus on a specific topic; thus, we only considered publications that discussed the application of artificial intelligence (AI) in fields such as diagnostics, therapy, precision medicine, remote monitoring, drug development, and clinical decision support systems, specifically in cardiology. Furthermore, the article's category was another critical factor. This implies To ensure the inclusion of high-quality and reliable data, we only included peer-reviewed literature, which includes systematic reviews, meta-analyses, clinical trials, and original research. We decided to study articles from 2000 to December 2024 in this context. This will allow us to capture the most important advances up to the present moment. Furthermore, language had a significant impact on the decision to exclusively employ articles relevant to English in order to ensure uniformity throughout the evaluation process. We use inclusion criteria to include the strong and irrelevant, and exclusion criteria to exclude the weak or irrelevant. To ensure the analysis's accuracy and avoid potential issues like language barriers that could compromise the consistency of each variable, we decided to exclude resources in languages other than English. Keep in mind editorials and letters to the editor will not work here. Additionally, conference abstracts and research designs that do not pertain to AI applications in cardiology fall outside the scope of this paper. We utilized a rigorous selection process to ensure the inclusion of only the most pertinent and high-quality articles in this review, establishing a strong foundation for an in-depth exploration of artificial intelligence in cardiology.

Data Extraction and Synthesis

We conducted a comprehensive evaluation, scrutinizing the selected papers for crucial aspects that required consideration. During the data extraction process, we focused on comprehending the study's objectives, including its primary goals and research questions. We looked at the methods in excellent detail, going over machine learning models, deep learning architectures, and data analytics techniques in particular. Furthermore, we conducted a thorough examination of the application of artificial intelligence in the field of cardiology. This report covered a wide range of topics, including drug discovery, diagnosis, treatment planning, precision medicine, remote monitoring, and more. We provided a comprehensive description of each relevant domain and a comprehensive explanation of its implementation steps. This analysis considers the practical implications for enhancing cardiac healthcare in addition to presenting the most significant outcomes achieved thus far and the key findings from each experiment.

Critical Analysis and Evaluation

After extracting the data, we thoroughly reviewed the existing literature to discover any gaps, challenges, or opportunities in this field. These were responsible for determining which AI applications were the most useful and significant, assessing the quality of the study methods, and speculating as to how AI could enhance clinical outcomes in cardiology. Ethical concerns, algorithmic biases, and the difficulties of integrating into existing healthcare systems all received significant attention from us. We have methodically presented the findings, emphasizing the contributions of artificial intelligence to the field of cardiology, the advancements achieved, and the ongoing challenges that require attention. We compiled the results of these investigations into a narrative about how artificial intelligence could transform heart disease patients' healthcare.

To start my evaluation, I conducted an extensive search of databases. After conducting the initial scan, I identified a total of 270 publications as relevant to my topic. I successfully eliminated any duplicate articles using the Zotero program, resulting in a total of 90 distinct articles. Subsequently, I engaged two impartial evaluators to examine the titles and abstracts of each item, employing predetermined criteria to determine their relevance. We further narrowed the selection down to 69 papers that required extensive reading based on these criteria. In the end, we included 47 people in the final report who met all of our requirements. Initially, the reviewers had op ng viewpoints regarding the selection of papers; however, through discussion, they agreed. This collaborative procedure ensured the inclusion of all papers and maintained objectivity among the team members participating in the review process. The research made use of four databases: IEEE Xplore, PubMed/MEDLINE, ScienceDirect, and Web of Science. Starting with the identification phase and continuing through the screening and selection phases, I have meticulously documented the entire process. The actions taken, the methods used, the reasons behind them, the timing, the people involved, and the outcomes achieved are all detailed in Table 1.

Table 1 Finding, Filtering, and Choosing Articles from Various Databases

AI-powered advances in cardiology diagnostics

The field of cardiology has greatly improved thanks to the development of artificial intelligence (AI), which has led to a higher degree of accuracy, efficiency, and early diagnosis of many different cardiovascular diseases (X. Sun et al., 2023). AI-powered diagnostic tools have transformed the analysis of electrocardiograms (ECGs), echocardiograms, cardiac imaging, and other diagnostic procedures. This has led to better clinical decision-making and therefore better patient outcomes in many cardiac pathologies (Forsythe et al., 2023). Algorithms based on artificial intelligence have revolutionized ECG analysis, allowing for rapid and precise reading of these vital tests (Bouchetara et al., 2024). AI systems can accurately identify arrhythmias (including atrial fibrillation) and conduction abnormalities, which lead to an earlier diagnosis and treatment (Goto et al., 2022). For example, AliveCor is a company that has created algorithms using artificial intelligence to detect atrial fibrillation from ECG recordings taken on smartphones. This technology allows immediate intervention for strokes and helps in early detection (García-Pérez, 2022).

AI has also allowed for the more precise analysis of cardiac structures and functions in echocardiographic imaging (Crea, 2023). For example, one of the algorithms developed by Ultromics Company reads echocardiographic data to predict coronary artery disease and assess left ventricular function (Kusunose et al., 2019). These tools allow doctors to make better-informed decisions about a diagnosis because they automate repetitive tasks and provide very accurate evaluations (Qayyum, 2023). Artificial neural networks have come a long way in enhancing the quality of cardiovascular images, especially in X-ray images and CT scans (Alsharqi et al., 2018). Artery's' machine learning software uses artificial intelligence to evaluate cardiac MRIs, automatically assessing heart function and locating abnormalities like myocardial infarctions or heart tumors (Kusunose, 2021). Furthermore, these methods could help in the early diagnosis of diseases such as pulmonary artery hypertension (PAH) (Stamate et al., 2024). They have also developed an algorithm that reads pictures of heart MRIs to look for PAH-related alterations (Siegersma et al., 2019). The algorithm can identify subtle changes in breathing patterns and heart function, thereby enabling early intervention for transplant consideration. Diagnosis and characterization of coronary artery disease (CAD) have shown significant promise thanks to AI (Dey et al., 2023). Machine learning algorithms are able to precisely locate and quantify the amount of atherosclerosis in coronary arteries by making use of enormous sets of cardiac CT angiography images (Badano et al., 2020).

There are companies, HeartFlow, for example, that have created non-invasive AI-based methodologies for assessing the functional significance of lesions in coronary arteries. This allows for better treatment planning in patients with CAD (Zhang et al., 2022). Artificial intelligence-based prediction algorithms can also predict heart failure exacerbations based on patient records and other types of data, such as imaging data (Muscogiuri et al., 2020). With the use of personalized management plans, physicians can identify patients at higher risk for the disease and augment their therapy with prophylactic strategies, resulting in better prognoses for these patients (Siswanto, 2022). Additionally, AI has the potential to have a significant impact on predicting readmission rates among heart failure patients, providing valuable information that can assist in preventing future events. The doctors can then use these predictions to find those patients that are in need of more aggressive therapy or close observation and, in turn, create patient-specific care plans to decrease the number of readmissions among this group of patients (Saini & Chandel, 2023).

Olawade, Aderinto, et al. (2024) report that artificial intelligence-based predictive modeling can accurately predict future congestive heart failure exacerbations and recognize those likely to be hospitalized due to this illness. For example, researchers have created algorithms such as the CART model that can accurately predict the probability of congestive heart failure before hospitalization simply by examining electronic medical records. These algorithms performed better than traditional risk prediction models before their development (Tangri & Ferguson, 2022). In addition, we identified clinical factors that might be able to predict readmission using machine learning methods. Clinicians are able to classify patients according to their risk levels and take the appropriate preventative measures to lessen the likelihood of relapses in the future as a result (Kavakiotis et al., 2017). Doctors can use AI's ability to analyze all sorts of patient data (clinical parameters, biomarkers, imaging results, etc.) to pinpoint heart failure patients who are more likely to be readmitted to the hospital after discharge. Due to this, they can do certain interventions for these types of patients and therefore reduce the amount of CHF (congestive heart failure) readmissions and eventually prevent them.

Table 2 AI in Innovative Diagnostics

Wearable technology and remote monitoring

Wearable technology and remote monitoring are leading the way in cardiology today, revolutionizing the monitoring, management, and treatment of cardiovascular health (Alugubelli et al., 2022). Clinicians can now collect data continuously and in real time, even outside of traditional clinical settings, by utilizing these cutting-edge technologies and artificial intelligence (AI) methods for data analysis (Sequeira et al., 2020). People are able to continuously monitor parameters like heart rate, blood pressure, activity levels, and, in certain circumstances, electrocardiograms (ECGs) with wearable devices like smartwatches, fitness trackers, and sophisticated biosensors (Ganesananthan et al., 2022). These gadgets collect a lot of physiological data that, when combined, provide a complete picture of cardiovascular health (Belsare et al., 2023).

Artificial intelligence is essential in order to comprehend and interpret the constant flow of data generated by wearable devices, the utilization of artificial intelligence is essential. AI-driven algorithms analyze data to identify patterns, anomalies, and signs of an irregular heartbeat (Jindal & Bansal, 2020).For instance, even the most inconspicuous alterations in heart rhythm that go unnoticed by individuals or their physicians might indicate the presence of arrhythmia or another medical issue requiring treatment. Artificial intelligence can detect such early warning signs (Awuah et al., 2023). Using AI-enabled remote telemonitoring, doctors can access streaming wearable device data from anywhere with an internet connection (Mushtaq et al., 2024). This technology enables doctors to monitor their patient's health from the comfort of their own homes, enabling them to identify potential problems early on by identifying patterns in their vital signs (Perumal et al., 2021).

The implications are significant in cardiology. Wearables, when combined with remote monitoring equipment, provide essential support for individuals with chronic cardiac conditions such as arrhythmias or congestive heart failure (Gatla). By consistently monitoring vital signs, we can identify deteriorating situations early and take timely actions to prevent hospitalizations, ultimately saving lives. Remote monitoring, combined with wearable technology, also helps to promote patient-centeredness. Giving individuals control over their health increases their engagement in the process. Additionally, they develop confidence and a sense of responsibility for adhering to treatment programs when they understand the impact of their actions on their own results. The transition to patient-centric care increases compliance with recommended regimens, resulting in improved results and long-term adoption of healthy behaviors. Continued progress in wearable technology, along with more intelligent AI programming, has the potential to greatly enhance remote monitoring in the fields of preventive cardiology and chronic illness management (Bautista et al., 2024). The integration of these two technologies will revolutionize our approach to heart health by enabling us to be more proactive in detecting, predicting, and preventing heart-related issues. This will ultimately lead to improved patient outcomes in all cases (Lavanya et al., 2024).

However, remote monitoring and wearable technologies in cardiology are currently facing specific challenges. The placement of these devices on the body, signal noise, and other factors can affect data accuracy and consistency (Donnelly). Multiple studies have demonstrated disparities between data collected from wearables and data received by very accurate measurements, which brings into doubt the reliability of such information for clinical decision-making (Burma et al., 2024). Furthermore, there is a lack of consensus about the methods of data collection and sharing across various systems. This provides a challenge when attempting to integrate and analyze data from disparate platforms that do not communicate using compatible protocols. Equally significant are concerns about the privacy implications of patient information confidentiality, as well as the safeguarding of personal data under global legislative frameworks. Furthermore, many individuals are unable to obtain these devices either because of their expensive price or lack of proficiency in digital technology, therefore making it more challenging for them to make use of potentially life-saving solutions (Hughes et al., 2023).

Customization for treatment and precise medicine

In cardiology, precision medicine is defined as the use of unique patient characteristics, genetic profiles, and disease-specific information (Antman & Loscalzo, 2016). A crucial part of this new approach is artificial intelligence (AI). AI considers a patient's medical history or can predict how the person will react to certain medications by examining large and complex databases and tailoring treatment plans for the individual (Dainis & Ashley, 2018). Precision medicine has used AI to treat various cardiovascular diseases (Coorey et al., 2021). Companies like Verily and Duke University have already used AI algorithms in their research. There are AI-based risk prediction models that use everything from imaging data to medical histories to genetic profiles to accurately determine the probability that someone will develop CAD or suffer some other form of cardiovascular event (Perez-Cerrolaza et al., 2024). Therefore, these models can serve as predictive tools, identifying individuals who are at a higher risk of developing the disease. As a result, doctors can come up with measures to prevent it from getting out of hand. Siemens Healthcare has created these predictive models using AI systems that can process a wide range of data, from clinical history to biomarkers. Siemens Healthcare uses these models to estimate the likelihood of untreated heart failure exacerbations. In addition to helping to optimize treatment strategies for patients with heart failure, these models also assist healthcare professionals in developing individualized care plans. Myogenes and Corvidia Therapeutics used AI algorithms to use genetic data to determine how different people would react to antiarrhythmic drugs used to treat atrial fibrillation (AF) (Kranz & Abele, 2024).

This method allows doctors to determine the best treatment for each patient according to their genetic makeup. Examples of this include Cardiogram and Omron Healthcare's HeartGuide, which utilize artificial intelligence (AI) to interpret information from an ambulatory blood pressure monitor, allowing us to better understand this kind of data (Ivanova, 2024). These systems offer individualized guidance for managing hypertension and encourage lifestyle changes based on an individual's preferences and needs. Zebra Medical Vision is one of the rare few that uses artificial intelligence to analyze metabolic profiles and imaging data. This analysis identifies cardiometabolic risk factors quickly, enabling the provision of patient-specific strategies to mitigate these risks (Ashraf et al., 2022). AI-based tools such as Genoox and Fabric Genomics, doctors can analyze genetic mutations that are associated with familial cardiomyopathies (Marques et al., 2024). This is very useful information because knowledge of these genetic mutations allows for individualized medical treatment and genetic counseling for family planning. Genuity Science, a pharmacogenomics company, uses AI algorithms to analyze pharmacokinetic data derived from patient samples. The goal of this research is to predict the person's reaction to different heart problem drugs (Haque & Islam, 2024). It makes it a lot easier to decide what kind of cardiovascular medications will work best for a patient. However, it is important to realize that these predictions are not exact. These illustrate the great promise that AI-based precision medicine techniques hold for revolutionizing cardiac care by incorporating all kinds of patient data. This way, doctors can adjust therapeutic methods to individual patient differences (Chhabra et al., 2023).

Therefore, employing a tailored strategy results in improved outcomes as it allows for the selection of more efficient therapies while reducing the occurrence of adverse responses (Mohsen et al., 2023). As technology advances, the field of cardiology is expected to greatly benefit from the precise treatment choices provided by AI systems. These systems enable increased degrees of precision in dealing with various types of cardiac problems. Table 3 provides comprehensive data on the particular applications of artificial intelligence (AI) in precision medicine for various cardiovascular disorders, along with successful instances of its implementation.

Table 3 Applications of AI in cardiovascular disease precision medicine

Improved visual aids and comprehension

Our method of analyzing and interpreting images in cardiology has been transformed by the incorporation of artificial intelligence (AI) into medicine. Consequently, significant advancements have been made in the diagnosis, classification, and monitoring of cardiac disorders (Lin et al., 2023). Utilizing cutting-edge imaging techniques and AI-driven tools, cardiologists have improved the precision and efficacy of cardiovascular treatment (Xu et al., 2020). In the field of cardiology, where new apps based on artificial intelligence aid clinicians in more accurately interpreting medical images, these advancements have demonstrated to be particularly useful. This results in precise diagnoses that, in the end, save lives (Bojsen et al., 2024). One such company, Heart Flow, uses AI algorithms to precisely analyze CT angiography images in order to identify coronary artery blockages (Gauriau et al., 2021). The diagnosis of CAD, the evaluation of the functional significance of lesions, and the recommendation of the most effective treatment options all depend on this technology (Antoniades & Oikonomou, 2024). Artery's and Siemens Healthcare have also successfully established the widespread use of AI-assisted analysis in cardiology, specifically for interpreting cardiac MRI or CT images (Dey et al., 2019). The conclusion of myocardial localized necrosis, innate coronary illness, and different circumstances is altogether worked on by these strategies, which empower programmed and quick understanding of cardiovascular life structures, capability, and tissue portrayal (Li et al., 2022). Utilizing deep learning models, researchers have specifically developed software assistance to enhance the accuracy of LV measurement rates in echocardiography (Grzyb et al., 2024). These tools play a crucial role in identifying anomalies and heart failure. These noninvasive techniques enable one to assess valvar disease in such a way that one can precisely measure and define the heart's workings (Kilic, 2020).

Moreover, some research teams have created AI algorithms that can process cardiac MRI's to detect and predict the early stages of pulmonary arterial hypertension (PAH) before the patient feels any symptoms. As a result, doctors and other medical professionals can create better management plans. One must remember that without AI, humans could not accurately diagnose and predict these diseases, especially while they are curable. Another significant development is the use of artificial intelligence (AI) to detect cardiac tumors in MRI scans. This type of technology is extremely useful in treatment planning because it allows us to better differentiate between benign and malignant tumors (Eck et al., 2021). Using the ECG detection algorithms in the Alive or Cardia Mobile device, doctors can diagnose AF patients more easily than ever before (Rashid et al., 2023). The algorithm's ability to monitor continuously allows for quick detection of arrhythmias, which could be life-threatening if not immediately treated. Heart Flow and Canon Medical provide novel CT and MRI technology for the quantitative assessment of myocardial perfusion. These techniques aid in the selection of the most effective treatments as well as the identification of ischemic hearts (Cruz et al., 2019). Overall, the use of AI imaging translation devices in cardiology has made for much better analytical precision. This technology allows doctors to better detect subtle changes and have a more accurate prognosis of the disease. This simplifies the process of developing improved management strategies, particularly for various heart disease types. Doctors may be able to identify a greater number of patients earlier, potentially leading to a cure. Furthermore, the worldwide availability of treatments for patients with various heart diseases has improved their prognoses. Time machines and artificial intelligence, as they grow in complexity, open up to a whole new spectrum of possibilities (Eck et al., 2023).

Analytical prediction and risk evaluation

Cardiologists are now using AI to create more accurate predictive analytics that directly correlate with risk assessment. This collaboration has ushered in the age of prescription medicine. AI breakthroughs are revolutionizing cardiovascular disease prevention, management, and treatment by utilizing extensive patient data sets that include clinical records, genetic information, and imaging results, among other factors (A. D. Jamthikar et al., 2020). Predictive analytics employs advanced AI technologies to forecast the development, advancement, and potential consequences of different cardiovascular disorders (Johri et al., 2021).  Verily and Duke University have made notable progress in building AI-based risk prediction models for coronary artery disease (CAD), a prevalent cardiovascular illness. These models use several factors of patients' health, such as genetic markers or imaging, to identify individuals who have a greater likelihood of developing CAD and experiencing subsequent cardiac events (A. Jamthikar et al., 2020). By enabling the implementation of therapies before they manifest in those already identified as high risk, predictive models can serve as powerful tools in combating these disorders. This proactive approach enhances healthcare delivery (Al-Maini et al., 2023).

In the field of heart failure (HF), predictive analytics is highly beneficial and considered indispensable (Guo, 2022). Siemens Healthiness has created an AI-powered prediction algorithm that analyzes biomarkers, imaging data, and patient histories to forecast HF exacerbations (Suri et al., 2022). Healthcare professionals can use this strategy to create individualized care plans, improve treatment methods, and reduce adverse events, all of which can improve patients' outcomes and improve their overall quality of life (Truslow et al., 2022). Artificial intelligence's predictive capabilities are also evident in the context of atrial fibrillation (AF), a prevalent form of irregular heartbeat, particularly through genetic analysis (Johri et al., 2022). Several anti-arrhythmic therapies have successfully controlled atrial fibrillation. However, it is sad that not all individuals respond well to these medications, and some may even develop unpleasant responses. AI algorithms may examine genetic information to assess an individual's likelihood of responding positively to medications used in treating AF (Alghubayshi et al., 2022).

In addition, pharmacogenomics and the field of cardiology have made significant progress in thoroughly assessing the dangers of cardiometabolic diseases. Zebra Medical Vision, genomic health, and genomic science have developed artificial intelligence technology to make these advancements possible. These algorithms can work with images, genetic markers, etc. 105. This allows for early detection, personal risk reduction, and drug selection for multiple cardiac pathologies. Table 4 provides a summary of the main results focusing on the integration of artificial intelligence (AI) into predictive analytics and risk assessment for different cardiovascular diseases. This table gives a complete picture of what kinds of AI applications, tools, and technologies they used, what types of data they included, and what they discovered. It also demonstrates that AI may have a significant impact on patient care, such as personalized treatment options for heart failure (HF), atrial fibrillation (AF), and coronary artery disease (CAD) prevention. The following table highlights the importance of AI in early detection, risk reduction, and personalized treatment of cardiometabolic wellness and hypertension control. That demonstrates how AI can transform cardiology today.

Table 4 Implementation of artificial intelligence (AI) into cardiovascular disease risk assessment and predictive analytics

Drug development and discovery

Cardiology is a field that is currently undergoing a big transition in the search for new forms of treatment and prevention of cardiovascular disease. This has led to the emergence of artificial intelligence in the drug discovery and development process. This drastic shift fundamentally alters how scientists discover new drugs, enhance their development processes, and pinpoint specific diseases with unprecedented precision (Visan & Negut, 2024). The latest trend in medical research is to use artificial intelligence to precisely identify molecular targets related to cardiac diseases. Companies and other organizations also use AI-enhanced algorithms to analyze large genetic and molecular datasets for research purposes. This study has made it easier to identify specific pathways or proteins, enabling the treatment of atherosclerosis and heart failure with targeted medicines that directly affect the molecular level (Kumar et al., 2023). Also, artificial intelligence's amazing ability to process large amounts of patient data (genetic profiles, clinical symptoms, etc.) could help accelerate the growth of precision medicine in cardiology (Parvathaneni et al., 2023). Using these platforms' data patterns, one can easily distinguish between two groups. These discoveries have enabled doctors to tailor treatment plans specifically for each patient, minimizing side effects and ensuring the medication they take is most effective for their body. A significant advancement in patient-centered cardiac care. Artificial intelligence (AI) has significantly driven the emerging field of medication repurposing, an increasingly popular approach to finding new treatments for cardiac disease (Gadade et al., 2024). AI systems utilize predictive models and extensive databases to identify approved drugs for other illnesses that could potentially treat specific cardiovascular diseases (Pareek et al., 2023). This method allows for faster drug development by skipping many of the tedious, but often necessary, steps that traditional methods entail. This could potentially accelerate the availability of treatments for cardiovascular disease. Table 5 presents several cardiac conditions and their corresponding treatment areas. Table 4 also demonstrates how AI-driven drug development addresses these conditions, providing samples of the tools and technologies used in each domain. AI-driven projects aim to uncover new pharmacological targets, develop individualized treatments, and enable drug repurposing. This holds the potential to revolutionize cardiovascular care by providing focused therapeutics and precision medicine (Alghubayshi et al., 2022)

Table 5 AI-driven drug development and discoveries in cardiology.

Technologies for clinical decision assistance

Clinical decision support systems (CDSS) in cardiology have seen significant advancements since the introduction of artificial intelligence (AI). These are decision-support tools that process large amounts of patient data in conjunction with medical literature and clinical guidelines (Demandt et al., 2022). However, integrating and utilizing these strategies in cardiovascular medicine presents numerous challenges. Healthcare is one field that is already using AI algorithms to sort through large amounts of data and provide recommendations (Alotaibi et al., 2021). One major problem that the CDSS attempts to deal with is the ability to forecast and classify the risk of many different types of cardiovascular disease (Aamir et al., 2023). These platforms, among others, can determine whether a person is likely to have a stroke, heart attack, or arrhythmia by looking at many parameters, such as genetic profiles, imaging data, and clinical records (Thiruganasambandamoorthy et al., 2024). Individual risk assessments enable the scheduling of therapies for patients at higher risk. As a result of this tailored approach, their outcomes improve even further (Statz et al., 2023). CDSS allows for better diagnosis and treatment of coronary artery disease (CAD). The AI platform for IBM Watson Health analyzes clinical records and imaging data based on hundreds of rules. It allows experts to interpret complicated test results, simplify treatment plans, and select appropriate medications for computer-aided design patients. Diagnostic precision is the hope of all these personalized treatment plans based on each patient's unique situation. But some obstacles may prevent cardiologists from deriving the full benefits of the CDSS. The biggest hurdle is interoperability, or the ability of these systems to connect to different EHR platforms and medical devices. Ensuring the soundness and precision of the AI algorithms used in CDSS is another challenging aspect. Constant updates and checks are necessary to ensure their functionality over time. However, when integrating Clinical Decision Support Systems (CDSs) into workflow, it's crucial to strategically design their implementation to avoid disrupting the provider's workflow and instead enhance its efficiency. To ensure successful utilization of these systems, health care workers should receive training on how to interpret their recommendations (Sutton et al., 2020).

Strengths and Limitations of the study

Although this study is very informative about the use of AI in cardiology, one must recognize its limitations before truly understanding and applying its results to other aspects. Another limitation is the wide range of databases and approaches utilized in the examined research. Since it is hard to compare results because of different trial designs, patient populations, and AI algorithms, researchers should have some standardization protocol when they publish their findings in different places. Another significant limitation of this analysis was a lack of diversity among the patient populations.The findings are only applicable to regions where income levels differ from those worldwide because the majority of studies focus on people from wealthy nations. Future research should include patients from a variety of socioeconomic backgrounds and geographical locations, rather than solely focusing on high-income nations, as was the case in most previous studies. This will ensure the inclusion of various demographic groups in the development of artificial intelligence (AI) solutions for hospitals worldwide, especially in developing regions. In addition, it is essential to consider the age distribution of the study participants. The majority of participants were adults, but it's important to keep in mind that there were also children and elderly people who needed medical attention. Even if these investigations take longer than anticipated, we should not tolerate ageism. As a result, the inclusion of all demographic groups throughout a person's life should be a top priority for researchers when designing experiments. For research into cardiological AI, we require significantly improved datasets. In a similar vein, issues with access, privacy, security, and interoperability persist in this setting. The sharing of datasets between organizations may be hindered by regulatory compliance regarding ethical rules if prompt, clear guidelines are not established. As a result, everyone will be aware of the specific guidelines they must follow when handling health-related data.

Implications and recommendations

To comprehend and implement the findings of this study more broadly, we must identify its limitations, even though it provides valuable insights into the application of AI in cardiology. The difficulty lies in the wide variety of methods used by researchers, as well as their data sources; this poses quite a challenge for anyone looking at such aTrial designs vary from one another, which can lead to biased comparisons between findings, even when patient populations remain constant. This is because there are discrepancies between the artificial intelligence algorithms used in these experiments, which can also be influenced by prejudice. Therefore, authors must establish uniform procedures for sharing their work among various sites while taking into account potential biases arising due to dissimilarities among trial designs, patient populations involved, or artificial intelligence algorithms used. This review identified a significant flaw in the homogeneity of patient groups, as they were all too similar. This means that any conclusions drawn cannot be applied universally worldwide, except in regions where income levels differ significantly from global averages. This is because the majority of studies focus on individuals living in wealthy countries, excluding those living elsewhere. It would thus be prudent if researchers enrolled subjects representative of diverse socioeconomic backgrounds geographically dispersed rather than limiting enrolment only to high-income nations like what has been done before most studies switched focus instead towards including people from different parts of world so that all races may benefit equally from development process involving machine learning applicable in hospitals situated anywhere on earth particularly those found within underprivileged areas where access remains limited even though age distribution among participants should also considered mainly adults were involved nevertheless younger individuals together with elderly persons requiring medical care were present during such surveys hence no room left for ageism even though some might take longer than anticipated still scientists need ensure every stage life covered when designing experiments related cardiac ai datasets have shown poor quality improvement is long overdue regarding access privacy security interoperability within given context sharing datasets across organizations could meet regulatory compliance requirements concerning ethical rules unless prompt clear guidelines are issued everybody will know specific rules they need abide by while handling health related information.

It is concluded that to revolutionize cardiovascular care, cardiology needs to marry AI in a big way. This article examines many uses of AI in cardiology. Intelligent machines drive these applications, which have the potential to revolutionize current heart disease treatment methods. AI algorithms can enhance the precision of electrocardiograms and other diagnostic tools, leading to more accurate and efficient interpretation of various heart problems. In modern cardiology, precision medicine allows physicians to tailor medical interventions to the unique features of an individual patient, such as the patient's genetic makeup or disease-specific features. Remote monitoring-based technology, such as wireless wearable devices, has utilized this artificial intelligence in real-time continuous monitoring outside of hospitals. This new turn of events increases patient participation and turns reactive home health care into proactive home health care. Significant advancements in imaging technology have resulted in increased precision, thereby enhancing the efficiency and effectiveness of healthcare delivery. This has enabled doctors to diagnose patients more precisely, thereby saving lives. Artificial intelligence (AI) can do a lot to help clinicians make accurate "risk" predictions for certain medical conditions and thus alter the face of cardiovascular disease prevention. The use of computers and feeding those historical data has allowed drug discovery to find new molecular targets in pharmacology. This is due to the ability of these computers to first analyze all this information and then propose potential treatments, thereby reducing the time required to develop treatments for heart disease alone. In light of this, the field has undergone a significant transformation over the years, moving away from paradigms that solely focused on treatment approaches. Despite these significant achievements, numerous challenges remain; we must improve the accuracy algorithms, ensure interoperability, and consider the ethical implications of entering patients without their consent or knowledge.

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