Advances in Artificial Intelligence for Cardiovascular System: Present Applications and Future Directions

The cardiovascular system is also called as circulatory system of our body. Cardiovascular diseases (CVD) are the number one cause of death worldwide and resulted into approximately 18 million deaths per year¹. Nonetheless, that means the early diagnosis and individualized treatment pose big challenges for cardiology² despite great improvement was achieved in pharmacologic therapy and interventional procedures. Transmutation of cardiovascular information from images and electrocardiograms to genomics and electronic health records (EHRs) also requires more sophisticated analytical solutions for the discovery of knowledge and advancement in this field³.

AI will alter healthcare by offering us new tools that can help us uncover information, see patterns in business, and think about challenges that can come up in the future⁴. AI helps clinicians make better diagnoses and tailor therapies for people with heart disease by using large, diverse datasets like electronic health records (EHRs), imaging data, genomics data, and signals from wearable devices. This will help patients get better results⁵.

Each realm of the cardiovascular care continuum has integrated technologies based on artificial intelligence (AI) such as machine learning (ML), deep learning (DL) and natural language processing (NLP). Such systems offer automatic image interpretation, risk-stratifications, remote control of treatments by patients and parents, robotic telesurgery assisted treatment at distance as well as virtual simulations of the patient (virtual patient or so-called digital twin)^6,7.

The convergence of AI and cardiovascular medicine may bring care out of reaction mode – in other words, spotting disease early, treating it more directly and continuously staying vigilant for recovery progress. However, AI is accompanied by a number of ethical, regulatory and technical challenges that must be addressed in order to ensure its safe and fair deployment towards their specific patients⁸.

In the current manuscript, we attempt to present a comprehensive review of AI in CV medicine from trends to future directions. From imaging and predictive analytics to wearables, genomics, robotics and next generation computing - discussing the potential use cases and impact of AIs in high level patient use cases as well as for technical constraints/ethical considerations on using AI.

MATERIALS AND METHODS

Searching a massive number of articles and integrating the peer-reviewed literature, clinical trials, and bibliometric analyses on AI in cardiovascular medicine, this review article was written⁹. The search strategy used databases such as PubMed, Web of Science Scopus and Google Scholar¹⁰. The key words used when searching include " artificial intelligence and cardiology ", " machine learning heart disease ", " deep learning in heart scans ", " AI of heart failure" and "predictive analytics for cardiovascular diseases"¹¹. The included studies had to meet strict criteria for selection¹²:

• Published between January 2018 and October 2025.
• Focusing on AI application in cardiovascular diagnostics
• English publication
• Articles from peer-reviewed journals, meta-analyses, systematic reviews and original scientific research reports

The following studies were excluded¹²:

• Non-English publications
• Not cardiovascular-related studies
• Editorials, commentaries and other contents of this kind, which are not peer-reviewed work. Data extraction targeted the type of AI technology used, clinical application results measured and future implications¹³.

RESULTS AND DISCUSSION:

Current Trends and Future Prospects in AI for Cardiovascular Medicine

1. AI in Cardiac Imaging

Cardiac imaging has been transformed by using the Artificial Intelligence (AI), which is now able to handle image acquisition, segmentation and interpretation. AI is used across various imaging modalities, such as echocardiography (ECG), cardiac CT (CCT), cardiovascular magnetic resonance (CMR), nuclear imaging and cardiac MRI^14-15.

1.1 Echocardiography

The AI-based echocardiography leverages CNNs to automate:

• Chamber detection: Left and right ventricle and atria identification¹⁶.
• Estimation of the ejection fraction: Real-time measurement with low operator dependence¹⁶.
• Wall motion: Identification of regional abnormality in myocardial contraction.

Several tools such as EchoNet-Dynamic have achieved performance comparable to humans in left ventricular function evaluation which uses 2D echocardiograms¹⁶.

1.2 Cardiac Computed Tomography (CCT)

AI makes CCT better by:

• Plaque Characterization: Differentiating calcified from non-calcified plaque¹⁷.
• Coronary artery segmentation: Automatic vessel tracking and stenosis quantification are performed¹⁷.
• Radiomics: to get high-dimensional features for sorting risk.

The AI-based quantitative CT shows a rough correlation with invasive methods like NIRS-IVUS^17,18.

1.3 Cardiovascular Magnetic Resonance (CMR)

AI is very important for a lot of CMR uses:

• One important area is characterizing tissue, which helps in finding the scar tissue, edema, and fibrosis¹⁹.
• Another part is about figuring out how much flow there is: It is possible to automate the process of phase-contrast analysis, which helps with valve assessment¹⁹.
• AI also helps with function analysis: volume measurements can be made in any of the heart's chambers¹⁹.
• Deep learning models have made post-processing time more than 70% shorter. But the accuracy of the diagnosis has not changed²⁰.

1.4 Nuclear Imaging AI and PET

AI helps perfusion mapping by increasing resolution and lowering noise levels. It also helps standardize the distribution of myocardial tracers by looking at how much they are taken up. When PET is used with CT or MRI, computers can do a full analysis MEMBER²¹.

TABLE 1: AI APPLICATIONS ACROSS CARDIAC IMAGING MODALITIES^14-21

2. AI in Predictive Analytics and Risk Stratification

One of the most important uses of AI is to use predictive analytics in heart medicine.  AI models are better than the Framingham Risk Score and the ASCVD calculator, which are both traditional ways to measure risk^22,23. They work well because they use a lot of different kinds of data, such as EHRs, genetic markers, and lifestyle factors²⁴.

Machine learning methods like XG Boost and random forests predict the myocardial infarction, stroke, and the heart failure with high sensitivity and specificity which helps in predicting analytics and risk stratification. 

These models allow healthcare providers to identify high-risk patients early.

They also support preventive measures, such as starting statins or making lifestyle changes, that are tailored to individual risk profiles²⁵.

2.1 Conventional Risk Models vs AI-Driven Formulations Traditional risk models

It includes the Framingham Risk Score, ASCVD calculator and SCORE system depends on a small set of variables (for example age, cholesterol and blood pressure)²². Such models may not accommodate variability in the profiles of individual patient, and therefore perform less accurately amongst different populations²⁴.

Specially DL-based, ensemble learning methods of AI models can:

• Integrate hundreds of variables simultaneously.
• Detect nonlinear relationships.
• Get smarter and better with new information.
• Personalize predictions for the individual, not just the population.
• Improves over the time as new datasets are included.
• Capable of early risk identification and preventive guidance.

TABLE 2: COMPARISON OF TRADITIONAL VS AI-BASED RISK MODELS

2.2 AI Algorithms for Cardiovascular Risk Prediction

A number of machine learning algorithms have been tried in cardiovascular risk prediction²⁵:

• Random Forests: For feature selection and classification.
• Gradient Boosting Machines (GBM): Good at handling unbalanced datasets.
• Neural Networks: Capture complex interactions between variables.
• Support Vector Machines (SVM): Useful for binary classification tasks.

The features of these models include:

• Whether or not to refer patients to appropriate diagnostic units.
• Stroke incidence
• Sudden cardiac death (SD)
• Hospitalization rates for acute heart failure patients who did not initially receive medications or who were resistant to treatment.
• Need for further coronary revascularization

2.3 Real-World Applications

We can now use ai technology, "big data," and many other tools to predict the five-year cardiac risk of a patient by means of its deep learning models trained on more than one million electronic health records (EHRs). Beyond risk prediction, AI algorithms are now widely used to analyze medical images such as ECG and CT scans, identifying abnormalities that can escape from human detection²⁴.

Once equipped with imaging, genomics as well as traditional clinical data, AI4Heart can distinguishes among many times before ill-health occurs a bad orchard from its companion (Lu, 48). LYNA, developed originally for use with cancer patients, has been adapted to assess cardiovascular risk²⁶.

2.4 Integration into Clinical Algorithms AI-based risk scores

These are being integrated into electronic health records to²⁴:

• Alert clinicians about high-risk patients.
• Recommend diagnostic tests or referrals.
• Guide preventive interventions (e.g., statin initiation, lifestyle counseling).
• Real time analyzing patient data.
• Helps in medication optimization.
• Make your guesses more accurate.
• Helps keep the health of a group of people in check.

Fig. No. 2: ROC CURVE COMPARISON—AI VS. FRAMINGHAM SCORE (SHOWS THAT AI MODELS HAVE A HIGHER AUC FOR PREDICTING HEART EVENTS)

2.5 Limitations and Challenges

• How good the data is A model can work worse if it has missing or wrong data.
• Generalization: Models that work well for one group of people may not work well for another.
• Understandability: Clients trying to figure out the black-box models may not work, and doctors lose faith in them²⁶.

3. Wearable devices and remote health monitoring devices that use AI

These have opened up a whole new world of cardiovascular monitoring when used with AI. They let you collect and analyze data in real time. Wearable devices can help patients keep track of their heart rate, blood oxygen levels, blood pressure, and physical activity²⁷. They make it easier for patients and healthcare providers to embrace rapid cardiovascular disease identification, personalize treatment plans, and lower the number of hospitalizations. Devices that use AI and don't need to be watched by a doctor can also keep an eye on your heart all the time, day and night. Devices like the Apple Watch and KardiaMobile use AI algorithms to read single-lead ECGs. With a sensitivity of over 95%, they can find atrial fibrillation²⁸.

The Zio Patch and BioBeat are two new kinds of patches that can check for heart failure by keeping an eye on thoracic impedance, heart rate variability, and breathing patterns²⁹. These technologies also let patients and doctors keep an eye on each other, which can lead to faster actions, shorter hospital stays, and a better quality of life³⁰.

3.1 Presentation of Wearable Technologies

Smartwatches, chest patches, biosensors, and implantable devices with sensors and AI algorithms that help people all over the world are all examples of wearables today. They keep track of things like how fast and how often the heart beats²⁷.

• Blood pressure
• How much you sleep and how much you do
• The amount of oxygen in your blood

• The speed at which you breathe
• These devices send information to cloud-based platforms, where AI algorithms look for patterns, find problems, and send alerts.

3.2 Wearables AI Algorithm

AI enhances the functionality of wearables by:

• Signal processing: Removing noise and artifacts from raw data.
• Pattern recognition: Spotting arrhythmias, ischemic changes and heart failure decompensation.
• Predictive modeling: Forecasting adverse events based on historical trends.

Common machine learning models such as support vector machines (SVM), decision trees, and recurrent neural networks (RNNs) are embraced by wearables³¹.

3.3 Purposes in Clinical Detection

Atrial Fibrillation Detection

AI in the apple watch and KardiaMobile for wearable devices is used to analyze single-lead ECGs. The performance of the stethoscope is more than 95% sensitive in discerning atrial fibrillation. Early detection AF can reduce the risk of stroke and help guide anticoagulant therapy²⁸. 

Monitoring Heart Failure

Patches with AI on board (for instance, the Zio Patch and BioBeat) use thoracic impedance, HRV measurements and respiratory recording to predict when forms of heart failure might worsen. Pre-emptive alerts tie medicine adjustments made at home or in an ambulance may prevent hospital admission²⁹.

High Blood Pressure Management

Wearable devices with a digital cuff that can measure blood pressure use AI to change the readings based on the time it takes for the pulse to move and photoplethysmography (PPG). These tools help people change their medications and their way of life when they need to. It helps find hidden hypertension and white coat hypertension, predicts when high blood pressure will happen, and allows for personalized treatment, among other things³⁰.

3.4 Patient Participation and Compliance

Devices that use AI and work with wearable sensors can give you personalized reminders and feedback. It turns health goals into a game, which makes people 50% less likely to skip their medicine and more likely to eat better and work out more³⁰.

TABLE 3: AI-ENABLED WEARABLES IN CARDIOVASCULAR CARE

3.5 Limitations and Challenges

• Accuracy of data: Moving your wrist or having different skin tones can change how the sensor reads.
• Battery life: Watching things all the time drains the battery.
• Privacy concerns: All of a patient's health information must be kept and sent safely, like by encrypting it.
• Clinical validation: Most technologies haven't been tested enough in real life to prove that they work as they say they do³¹.

3.6 Future Directions

• Multi-modal sensing: ECG, PPG, accelerometry, and quick temperature monitoring all work together to keep an eye on patients' health.
• Edge computing: AI runs on the computer itself, which speeds things up and makes them less reliant on the cloud.
• Integration with EHRs: Clinical systems can easily get information from both preventive and restorative care.
• AI coaching: Based on real-time, hard physiological signs.
• Customized care: AI-powered platforms that give you personalized medicine doses, types of therapy, and more^27,30.

4. AI-assisted stethoscopes

AI-enhanced stethoscopes combine regular sound sensors with ECG capabilities, making it easier to find heart problems, especially arrhythmias and valvular disorders³².   The Eko DUO and StethoMe are two examples of devices that work well for both kids and adults³³.   These advanced stethoscopes use machine learning to look at heart sounds³⁴.

4.1 Evolution of the Digital Stethoscope

Traditional stethoscopes can make mistakes because they depend on the doctor's ability to listen carefully. Digital stethoscopes turn sounds from the heart into electronic signals that can be seen, stored, and studied. This makes the results more accurate and exact³².

AI improves this by: 

• Filtering out background noise 
• Classifying heart sounds (S1, S2, murmurs, gallops) 
• Spotting abnormal rhythms and problems with heart valves^34,35 

Examples include:

• Eko DUO: This device merges ECG and digital listening with AI to detect murmurs³². 
• StethoMe: This system uses AI to find respiratory and heart issues in young patients³³.
• Butterfly iQ+: While mainly an ultrasound tool, it uses AI for heart assessments³⁶.

4.2 Clinical Applications

Valvular Heart Disease: AI algorithms, which have been trained on thousands of annotated heart sound recordings, can identify:

• Aortic stenosis
• Mitral regurgitation
• Tricuspid insufficiency There are reports that AI-assisted stethoscopes can achieve sensitivity and specificity >85% for common valvular lesions³⁴.

Arrhythmia Detection ECG equipped dual-mode stethoscopes may detect:

• Atrial fibrillation
• Bradycardia
• Tachyarrhythmias Live alerts facilitate timely measure and lower the risk of stroke³².

Paediatric Cardiology AI improves auscultation in children, who frequently have faint and hard to discern heart sounds. It can help to identify congenital heart defects and innocent murmurs and more diseases.

1. 3. Integration with Telemedicine 

Telemedicine platforms are increasingly used with AI-supported stethoscopes.

Remote clinicians can:

• Obtain good heart speech recordings
• View synchronized ECG traces
• Decide without being there This is particularly crucial in rural and underserved regions³⁷.

4.4 Limitations and Challenges 

• False positives: AI could mistakenly say that mild murmurs are something serious, which would result in unnecessary referrals³⁵.
• Bias in training datasets: Models that were trained on adults won't work as well on kids³³.
• Cost and access: Small clinics might not be able to buy expensive equipment.
• Issues with rules: Clinical fulfillment has to follow the rules set by the FDA and CE³⁷.

4.5 Future Directions

• Data fusion uses sounds from a stethoscope, information from an ECG, and pictures to quickly and completely figure out what's wrong³⁶.
• Cloud-based learning gets better over time when you share and save data online.
• Voice-guided feedback: This helps new doctors figure out how the heart is beating right now.
• Electronic health records make it easy for hospitals to keep track of patient information and follow up with them³⁷.

TABLE 4: CAPABILITIES OF AI-SUPPORTED STETHOSCOPES

5. Clinical Decision Support Systems (CDSS) Powered by AI

CDSS are computer applications designed to help the health care professionals in making evidence-based decisions. Enriched with AI, these systems become state-of-the-art and build on extensive databases identifying the patterns, offering personalized recommendations with hwlp of AI by identifying the best practice in the cardiovascular care³⁸.

AI-driven (CDSS) support clinicians in the diagnosis, planning of treatment, and prognosis modeling³⁸. They use the patient data and clinical guidelines to offer the optimal therapies, highlight alerts on possible side effects of any drug used by the particular patient, and perform record-keeping automatically and report if any side effect or adverse effect of a drug detected on patients³⁹.

Notable examples include HeartFlow Analysis for non-invasive coronary FFR estimation and CardioSmart Advisor for customized care planning²?. AI-driven clinical decision support systems improve diagnostic accuracy, reduce errors, and increase clinical workflow efficiency⁴⁰.

5.1 Overview of AI-Driven CDSS

Traditional CDSS use logic that is based on rules that have already been set and clinical guidelines that are set in stone. AI-driven CDSS, on the other hand, use advanced technologies like machine learning, deep learning, and natural language processing to:

• Look at both organized and unstructured data, like EHRs, images, and lab reports
• Guess what will happen in the patient's treatment
• Recommend the best tests and treatments for the diagnosis
• Tell healthcare workers about possible bad outcomes

AI-powered CDSS also learn from new data and clinical experience, which makes them more accurate over time. This is different from older systems^38,41.

5.2 Components of CDS Systems 

Level of Data Integration: You can effortlessly dive into a treasure trove of data from all sorts of sources, like genomic databases, those fancy wearable gadgets, imaging systems, and the ever-popular electronic health records (EHRs). It's like a data buffet, and you're invited to feast on it⁴⁰.

Getting AI to do your bidding!

Dive into the wacky world of complex algorithms that whip up spot-on classifications, predictions, and suggestions tailored just for you—like a personal assistant who knows your coffee order better than you do.

Ambitions for the user interface: Handing healthcare professionals a treasure map of clear, useful information that's as easy to read as a comic book and totally on point for what they need⁴¹.

5.3 Application in Cardiology

Diagnosis Assistance

AI-powered CDSS may act like a doctor by looking at a patient's test results, medical history, and symptoms and coming up with a list of possible diagnoses, like a game of medical bingo!    To give you an idea:  HFrEF and HFpEF are like the odd couple of heart failure. They each have their own unique traits that set them apart from the rest⁴².

Treatment Optimization

AI-CDSS gives therapeutic recommendations that are based on clinical criteria and the specific needs of each patient is by choosing the optimum antithrombotic medication for atrial fibrillation³⁹.
Based on the lipid profile and hereditary risk factors, it is recommended to start statins,
deciding who can get device-based treatments like CRT and ICD⁴².

IBM Watson and Google DeepMind are two systems that can guess: -

• • The chance of going back to the hospital within 30 days
  • People with acute coronary syndrome are more likely to die
  • The likelihood of stroke in persons with atrial fibrillation⁴³

Workflow Automation

AI-CDSS can do simple tasks on its own, such as: 

• • Writing and making discharge summaries plans for follow-up instructions for smooth patient recovery.
  • Sending alerts when lab results are unusual⁴⁰

5.4 Benefits

• More accurate: It lowers the number of diagnostic errors and encourages people to follow healthcare guidelines more closely.
• Efficiency: Saves time by automating data synthesis and documentation.
• Personalization: Tailors recommendations to individual patient profiles.
• Scalability: Can be deployed across hospitals and clinics with minimal infrastructure, making it easy to implement widely.

5.5 Future Directions

• Explainable AI: This makes things clearer, which helps clinicians trust and understand system recommendations even more.
• Voice-enabled interaction: It lets CDSS work as a hands-free assistant during clinical operations.
• Federated learning: This is when model training happens at more than one institution without exchanging sensitive raw data.
• Digital twin integration: This lets you simulate the outcomes of different patients to help you choose the best treatment.

TABLE 5: AI-CDSS CAPABILITIES IN CARDIOVASCULAR CARE

6. AI in Genomics and Precision Medicine

Using genomics and artificial intelligence together makes it possible to use precision medicine in new ways to treat heart problems. By combining genetic data with clinical and lifestyle information, AI can find new biomarkers, figure out the best treatments for a person, and even figure out who is more likely to get sick. AI is making genomics in heart care happen faster by helping to figure out complicated genetic data for predicting risk and tailoring treatment to each patient⁴⁴.Machine learning algorithms can identify relevant single nucleotide polymorphisms (SNPs) from genome-wide association studies and build polygenic risk scores for disorders such as coronary heart disease and atrial fibrillation⁴⁵. AI systems such as MyGeneRank and CardioDx turn genomic profiles into actionable decisions through AI guidance on questions like whether to wait and see if you're at high risk of an existing condition to undergo drug therapy, lifestyle change early detection⁴⁶. Such strategies allow medicines to meet specific needs by taking into consideration an individual's genetic makeup⁴⁷.

6.1 Cardiovascular Disease in the Era of Precision Medicine 

 This section describes how precision medicine has begun to benefit... In cardiology, prevention, diagnosis and treatment today must be: Precisely honed Genetic risk scoring Pharmacogenomics Molecular phenotyping Targeted therapies AI has become indispensable. Charles Scriver Credit: © Charles B. Scriver It processes high-dimensional genomic data sets and uncovers complex interactions between genes and with the environment^44,47. 

6.2 AI Techniques Used in Genomic Analysis 

AI algorithms in genomics includes:

• Unsupervised learning: Clustering gene expression profiles to identify disease subtypes.
• Supervised learning: Predicting disease risk based on known genetic variants.
• Natural language processing (NLP): Mining literature for gene-disease associations.
• Deep learning: Modeling nonlinear relationships in multi-omics data.
• These techniques make it possible to: Find single nucleotide polymorphisms (SNPs) linked to CVD Speculate on drug metabolism and adverse reactions Integrate transcriptomic, proteomic and epigenomic data^45.

6.3 Pharmacogenomics

AI mode’s forecast—how an individual will respond to cardiovascular drugs for example:

• Clopidogrel: CYP2C19 Variants affects antiplatelet effect
• Warfarin: VKORC1 and CYP2C9 P450 variants influence required doses
• Statins: SLCO1B1 Variants linked to myopathy risk⁴⁷.

Among the many clinical pathways supported by patents this year was AI-CDSS, integrating both genetic and clinical data to recommend a personalized drug regimen; small adverse events can thus be greatly reduced and beneficial outcomes are more likely.

6.4 Biomarker Discovery

AI can be used to aid in biomarker study by performing the following functions^44,48:

• Analysing data from gene expression
• Discovering fresh targets for heart failure arrhythmia and cardiomyopathy
• Validating biomarkers by using cross-platform data Examples of the above include:
• miRNA signatures for myocardial infarction
• Identities of the exosomes in heart failure
• Genomic predictors of sudden cardiac death 

6.5 Clinical Implementation

• MyGeneRank: an AI-driven app that predicts a PRS for coronary artery disease.
• CardioDx: Testing for gene profile associated with obstructive CAD risk.
• All of Us Research Program: An NIH initiative uses AI to examine, from diverse populations, the genetic data on which this research is based⁴⁶.

6.6 Challenges and Limitations

• Data heterogeneity: Variability in sequencing platforms and annotation standards.
• Ethical concerns: Genetic privacy and consent for data use.
• Population bias: Inadequate or no representation of non-European ancestries in training datasets.
• Clinical translation: Limited integration of genomic insights into routine practice.
• Model interpretability: Advanced AI models often act as black box making it difficult for clinicians to understand their predictions, which can reduce trust and use.
• Regulatory hurdles: Gaining approval for AI medical tools is often slow and costly, delaying their move from research to clinical use⁴⁷.

6.7 Future Directions

• Multi-omics integration: Marrying genomics, proteomics, metabolomics, microbiomics and even geography data as well.
• Federated learning: Model training through many institutions but not at University itself.
• AI-guided gene editing: Genetic interventions on virtual patient models that can be simulated with known outcomes.
• Digital twin genomics: Simulating genetic interventions in virtual patient models⁴⁸.

TABLE 6:AI APPLICATIONS IN GENOMICS FOR CARDIOVASCULAR MEDICINE

7. Robotics and AI in Cardiac Surgery

The combination of AI with robotics has marked a new era in precision, safety, efficacy and outcome⁴⁹. AI algorithm-guided robotic procedures provide superior visualization, dexterity and decision support, turning historical surgical methods into minimally invasive interventions that use data for improved outcomes. Systems like the da Vinci Surgical System and CorPath GRX employ AI to help them target optimal instrument trajectories, deliver real-time anatomical guidance and spare a surgeon from having to spend long hours on telesurgery⁵⁰. Positive impacts of AI adoption have been observed in a variety of procedures such as mitral valve repair, coronary artery bypass grafting (CABG), and atrial septal defect closure⁵¹. According to reports in the literature, decreased blood loss, a short hospital stay and sooner recovery are seen when compared with conventional techniques⁵².

7.1 Overview of Robotic Cardiac Surgery

Robotic cardiac surgery employs computer-assisted systems such as the:

• da Vinci Surgical System
• CorPath GRX (Corindus)
• MAKO SmartRobotics These systems allow surgeons to perform complicated procedures through small incisions using robot arms controlled from a distance.

They are now being enhanced with AI to:

• Optimize instrument trajectories
• Predict surgical complications
• Provide real-time anatomical guidance^49,51

7.2 Applications of AI in Cardiac Surgery 

Preoperative Planning

AI analyse imaging data (CT, MRI, echocardiography) using algorithms to:

• Reconstruct 3D models of cardiac anatomy
• Simulate surgical approaches
• Identify optimal incision sites and graft paths⁵³ 

Intraoperative Guidance

• Finding tissue in real time
• Automatically stitching and anastomosing
• Hemodynamic alerting and monitoring with computer vision and deep learning models make it easier to tell the difference between healthy and diseased tissue, which lowers the number of mistakes made during surgery^50,53.

Postoperative Monitoring

• AI keeps track of things like heart rate variability and oxygen saturation that affect recovery.
• Scores for the pain and movement

You can use these factors to find possible problems, such as atrial fibrillation, infections after surgery, or bleeding, and get treatment early⁵⁴.

7.3 Routine Robotic Cardiac Procedures

• Repairing or replacing the mitral valve
• Using a graft to bypass the coronary arteries (CABG)
• Closing the atrial septal defect
• Putting in epicardial leads for pacemakers
  Studies have shown that robotic CABG results in:
• Less blood loss
• Shorter hospital stays
• A quick recovery and return to daily activities⁵²

7.4 Benefits of AI-Robotic Integration

• Accuracy: Clinicians can make precise cuts or do surgeries exactly where they are needed with the help of computer-controlled tools which helps in doing precise surgeries and hence patient compliance.
• Imagery: Surgeons can see tiny details inside the body more clearly now that 3D images and high magnification have gotten better their vision on every single detail inside the human body.
• Using technology and real-time alerts may help lower the number of surgical mistakes, infections, and other problems.
• Doctors also say they are less tired.
• Individualization: AI helps doctors make personalized treatment plans for each patient, which leads to the best results.

• Precision: With the help of AI, we can reach to the precision in the cardiovascular treatment of a patient and increase the chances of success rate and precision rate in treating cardiovascular diseases.
• Eliminate errors: Reduces the chances of errors in the cardiovascular treatment when compared to humans^49,52.

7.5 Constraints and Hurdles

• • Cost: The initial investment needed for capital and the ongoing costs will be high.
  • Training: Surgeons and staff will have to endure an extremely steep learning curve.
  • Data dependency: The efficacy of AI is based on obtaining high-quality inputs.
  • Approval from the government: It has to be shown to be safe and work before it can be used⁵⁴.

7.6 Future Directions

• - Autonomous surgical systems: Robotic surgeries that use AI and don't need much help from     people.
  • Haptic feedback integration: The remote surgeon is made to feel like they are touching something.
  • Simulation with AI: The release of virtual reality training modules for people of all skill levels.
  • Digital twin surgery: a way to practice surgery on a patient before doing it for real.
  • Use of AI: AI and wearable biosensors work together to keep an eye on the heart all the time, which makes it possible to find and treat arrhythmias, heart failure, and other heart problems in the right away in greater precision rates than humans.
  • Better learning models: These let AI systems learn from data from a lot of different places while still keeping patient privacy and data security confidential at high security level so that not any information of patients gets leaked^53,54.

TABLE 7: AI AND ROBOTICS IN CARDIAC SURGERY

8. Explainable AI and Digital Twins in Cardiology

Explainable AI (XAI) is an important tool for putting AI into practice in the real world because it makes the decision-making process of a model clear. SHAP (SHapley Additive exPlanations)⁵⁶, LIME (Local Interpretable Model-Agnostic Explanations)⁵⁷, and attention maps are some of these methods⁵⁵. These let you know how important each attribute is and how the model works. Not only does this openness help build trust in AI systems, but it also helps doctors trust them and get permission from regulators and patients.

Digital twins—AI-powered computer models that represent the patient’s cardiovascular system—are capable of simulating the progression of the disease along with the treatment response⁵⁸. These models combine anatomical, physiological, behavioural, and genomic data to not only make the care more personalized but also to facilitate the safe testing of interventions⁵⁹.

8.1 Explainable AI (XAI)

It refers to the methods that make the decisions of complex AI models transparent and understandable to humans. In cardiology, XAI will help clinicians to:

• Visualize the importance of various features, such as identifying which factors influenced a risk prediction.
• Understand model behaviour across different patient populations.
• Identification and mitigation of biases in training data.

Uses in Cardiology:

• Making it easier to understand how likely heart attacks are.
• Explaining why treatment recommendations, like starting statin therapy are made.
• Pointing out the parts of the ECG that cause arrhythmia alarms.

TABLE 8: EXPLAINABLE AI TECHNIQUES IN CARDIOVASCULAR APPLICATIONS

8.2 Digital Twins in Cardiology

These days, when technology is so personal, the lines between patients and doctors are quickly fading. This means that medical data like X-rays need to be shared and understood better right away. Digital twins meet this need by giving doctors virtual models of the patient's cardiovascular system, which are essential for accurate treatment⁵⁸.

These digital twins always get real-time data from wearables, imaging equipment, genetic profiles, and clinical records. This lets them run simulations that show how diseases progress and how treatments work⁵⁵. This is a problem that a lot of healthcare systems have: data comes from a lot of different places, which makes it hard to put it all together. But these digital twins might help put all of that data into one system. This is a good step forward that will probably lead to progress in the near future.

The main parts of a digital twin for cardiology are:

• 3D Model: A very detailed computer model of the heart and blood vessels^.
• Anatomical Model: This shows how the heart and blood vessels work together.
• Physiological Model: This model shows how blood flows, how pressure changes, and how electricity works in the heart and blood vessels.
• Behavioral Model: Looks at how people live their lives and how well they stick to their drug plans.
• Predictive Engine: Uses AI to make guesses about what will happen in the clinic for a lot of different treatment options in the cardiovascular treatment.

Uses

• Changing valves or putting in stents to see what happens.
• Predicting how heart failure will get worse with different treatments.
• Testing how different medications work together and what side effects they might have in a virtual setting.
• Running virtual clinical trials.
• Keeping track of patients' health over time^58,59.

Fig. No. 4: ARCHITECTURE OF A CARDIAC DIGITAL TWIN