Keywords
artificial intelligence - cardiovascular disease - ethics - ChatGPT
Introduction
Cardiovascular diseases (CVDs) are the leading cause of death worldwide, underscoring
the importance of prompt and precise diagnosis. Artificial intelligence (AI) and machine
learning (ML) have become crucial tools in the health care sector, offering substantial
potential for cardiovascular diagnosis and imaging advancements, revolutionizing patient
care, research, and clinical decision-making.[1]
Deep learning algorithms are currently employed to analyze computed tomography (CT)
and magnetic resonance imaging (MRI) studies, predict the results of exercise tests,
and forecast the outcomes of interventional procedures like transcatheter valve replacements.
AI-assisted electrocardiography (ECG) analysis enhances arrhythmia detection, heart
failure risk stratification, and myocardial infarction diagnosis. ML models optimize
cardiac catheterization reports, coronary angiograms, and telemedicine applications.
AI models can identify novel genetic associations with cardiac conditions, aiding
in personalized medicine.[2]
[3]
The AI algorithms can predict cardiovascular risk with greater accuracy than physicians,
aiding in the customization of management plans, and enhancing patient medication
adherence and health care engagement.
The swift progress of AI in cardiology offers promising opportunities but also presents
significant challenges that require thorough evaluation. Implementing AI in cardiovascular
medicine should be based on core bioethical principles: autonomy, beneficence, nonmaleficence,
and justice. The literature highlights several significant ethical issues, including
bias, privacy, trust, accountability, and responsibility.[4]
[5]
Clinical Applications
Recent advances focus on improving imaging techniques, disease detection, and risk
prediction using ML and deep learning models. These technologies offer insights that
are often beyond the reach of clinicians, particularly in fields such as imaging and
ECG analysis.[6]
ML can analyze images from echocardiography, cardiac MRI, and CT angiography. These
algorithms can identify subtle differences and uncover hidden signals that are not
visible to the human eye.[7]
AI has the potential to revolutionize traditional diagnostic tools by converting them
into advanced predictive instruments. These tools can identify nuance changes in ECGs
that may indicate future cardiovascular events. AI can predict new cases of atrial
fibrillation (AF), detect systolic heart failure, and diagnose heart attacks in the
emergency room. AI can improve decision-making in urgent situations, optimize resource
distribution, and tailor treatment plans.[8]
[9]
AI models outperform traditional ECG interpretation in detecting conditions such as
reduced ejection fraction, cardiomyopathies, and valvular heart disease. Deep learning
techniques significantly enhance arrhythmia classification and can predict AF risk
from normal sinus rhythm. AI-powered smartwatches and wearable devices enable early
detection of AF, heart failure decompensation, and abnormal rhythms.[3] AI-supported ECG screening has effectively detected hypertrophic cardiomyopathy
and cardiac amyloidosis early.[10]
[11]
The Mayo Clinic has examined over 650,000 ECGs from patients with various heart conditions.
This extensive analysis has enabled the creation of AI/ML algorithms that provide
valuable clinical insights. For instance, these algorithms can identify a patient's
gender, estimate their biological age, and detect signs of left ventricular dysfunction
using just a few heartbeats. The ECG algorithm may detect previous episodes of AF
in patients who are in sinus rhythm during the examination.[6]
ML algorithms can greatly enhance risk assessment and assist in treatment choices
for conditions such as long QT syndrome, Brugada syndrome, and hypertrophic cardiomyopathy.
They can predict the risk of arrhythmias with higher accuracy compared with traditional
risk scores.[12] In acute myocardial infarction, the latest generation of AI demonstrated superior
performance compared with traditional ST-elevation myocardial infarction criteria,
offering twice the sensitivity while maintaining the same level of specificity.[13]
An important area where AI can make notable progress is in diagnostic imaging. Several
studies have demonstrated that AI can significantly improve the precision, speed,
and accuracy of various imaging techniques such as echocardiography, CT, and MRI.
In echocardiography, AI applications assist with tasks like ventricular segmentation,
measuring ejection fraction, and analyzing wall motion. Advances in deep learning
and large-scale computing have made it possible to quickly acquire parameters and
predict diseases and cardiovascular events, with or without quantitative data. An
automatic echocardiography report generation is anticipated.[3]
[14]
[15]
AI-enhanced coronary CT angiography has been utilized to detect and quantify atherosclerotic
plaques, offering a precise and efficient characterization of coronary artery disease
(CAD). Recent advancements in AI have led to the development of models that can automatically
detect cardiac anomalies, significantly enhancing diagnostic accuracy and reducing
operator variability.
Cardiac magnetic resonance (CMR) has also seen advancements due to AI innovations,
especially in functional MRI, which allows for more detailed tissue analysis. AI can
transform CMR imaging from acquisition to diagnosis. AI quantifies CMR images, assessing
strain, myocardial motion, and perfusion.[1]
[10]
AI enhances intracardiac signal analysis to guide electrophysiology procedures, such
as AF ablation. ML models assist in coronary artery stenosis detection and provide
real-time decision support during catheterization.[10]
AI improves preoperative risk assessment, intraoperative decisions, and postoperative
care by offering better risk stratification and more accurately predicting complications
like AF or acute kidney injury than traditional methods.[16] Robotically assisted percutaneous coronary intervention is expected to enhance angiography's
precision and lower the proceduralist's occupational hazards. AI refines genetic risk
stratification by identifying pathogenic variants associated with CVDs. AI models
predict future cardiovascular events by analyzing electronic health records (EHR),
laboratory results, and imaging data.[10] Integrating EHR data with AI enhances CVD risk prediction and management.[17]
We are currently witnessing the emergence of foundation models in medical AI, which
are large, adaptable systems trained on extensive and diverse data sets, offering
superior performance compared with traditional task-specific models. It is crucial
to thoroughly validate, carefully implement, and continuously evaluate AI tools used
in clinical practice.[4]
ChatGPT
The development of AI, particularly large language models such as ChatGPT, creates
new possibilities for preventing, diagnosing, managing, and researching CVD. ChatGPT
is revolutionizing health care by reducing administrative tasks, enhancing medical
education, and aiding research with literature reviews and hypothesis generation.
ChatGPT improves medical education by providing personalized learning, automated scoring,
and instant access to extensive medical knowledge.[18] ChatGPT-based models may have potential in ECG interpretation, but they currently
lack adequate reliability. Clinical use of ChatGPT requires more validation, regulatory
oversight, and continuous improvements in model accuracy and reliability.[19] By offering clear and understandable explanations of cardiovascular conditions,
procedures, and lifestyle changes, ChatGPT can assist patients in managing their health.[20]
ChatGPT has limitations in cardiology; it uses preexisting data sets, so it may lack
current medical recommendations. AI-generated responses can sometimes be error-prone
generating incorrect citations, a phenomenon referred to as “hallucination” or “stochastic
parroting.”[20]
[21]
Other chatbot systems capable of natural language conversations using advanced language
models include Google Bard, Microsoft Copilot, DeepSeek, Claude, and Perplexity.
Ethical Challenges
Equity
According to the World Health Organization, “equity” means eliminating avoidable,
unfair, or fixable differences among various social, economic, demographic, or geographic
groups. To reach the goal of health care equity, it is essential to use data sets
that represent the entire population when developing AI/ML algorithms. Additionally,
prioritizing equity should be explicitly mentioned as a goal during the creation of
these health care AI/ML tools. AI in health care should be made widely accessible
and should be developed to ensure its use across all ages, races, genders, incomes,
ethnicities, and locations.[22]
Bias
AI algorithms need extensive data for training and to develop an understanding of
a problem based on the data provided. If the data are limited or inaccurate, it will
lead to biased models. Nolin-Lapalme et al classified biases in AI models related
to cardiology into four types: data bias, algorithm bias, assessment bias, and user
bias.[23] Statistical bias arises from nonrepresentative samples in the training data, such
as undersampling or excluding certain populations. If a data set primarily consists
of certain demographics, such as middle-aged Caucasian males, the AI model may not
perform effectively for other groups. This lack of diversity can lead to biased outcomes
and reduced accuracy in diagnosing and treating these underrepresented races and ethnicities
that experience the worst health outcomes.[22] Low- and middle-income countries bear approximately 80% of the global CVD burden.
Favoring data from Western European or North American sources over data from Asian
or African countries will inherently lead to biased algorithms. AI tools in cardiovascular
medicine frequently necessitate sophisticated infrastructure, advanced imaging technologies,
or wearable devices, which might be unavailable in resource-limited regions. Most
studies on these devices come from high-income countries, which raise ethical and
safety concerns.[24] AI-powered CAD risk calculators might not work in low-income areas without proper
infrastructure. Another important issue is that most AI developers are men, potentially
disadvantaging women. Despite women with AF having higher mortality risks, only 29%
of smartwatch users are women.[24]
Proposed strategies to tackle these challenges include meticulous data curation, development
of algorithm design techniques to reduce bias, and the promotion of transparency in
AI research.[23]
Privacy and Consent
Creating AI systems necessitates extensive access to detailed patient data for algorithm
training. The large volume of data makes sensitive information susceptible to cyber
threats. It is essential to have clear safeguards in place. In cardiology, AI frequently
utilizes patient health records, imaging data, and real-time information from wearable
devices such as smartwatches. Robust legal frameworks are essential to ensure that
identities are anonymized and safeguarded against breaches.
AI systems should not access patient data without obtaining informed and valid consent.
Patients should be informed about how their data are being used whether it is identified
or deidentified.[4]
[22] Rose and Shapiro recommended evaluating AI use cases based on specific criteria
to determine the proper category (no notification or no informed consent [IC], notification
only, and formal IC): (1) the level of AI model autonomy, (2) the extent to which
the AI deviates from standard practices, (3) whether the AI model interacts directly
with patients, (4) the clinical risks associated with the model, and (5) the administrative
burdens involved.[25]
While the default ethical stance requires IC for using personal health data, exceptions
exist under regulated conditions. Nonetheless, prioritizing transparency and obtaining
consent whenever possible remains the best practice to uphold ethical standards in
health data usage. Individuals should not encounter limitations on medical treatment
or essential services if they decide to withhold consent. Additionally, organizations
should not provide incentives to individuals to obtain consent, such as insurance
companies offering wearable technology to customers in exchange for access to their
health data.[26]
Liability
Evaluating the liability of AI/ML algorithms is essential to appropriately balance
their associated risks and advantages. AI systems can give incorrect or unsafe recommendations,
like misidentifying benign arrhythmias as critical. It is unclear who is responsible
for AI errors: developers, clinicians, or health care institutions. The integration
of AI into clinical practice raises pressing concerns, paralleling ethical and legal
debates seen with autonomous vehicles—particularly regarding accountability. In instances
where an AI system provides guidance that a clinician chooses to override, and adverse
outcomes occur, the question of responsibility becomes complex. Addressing such issues
is essential prior to the widespread adoption of AI in health care, with the answers
likely varying based on the type and application of the AI tool involved.[7]
In their scoping review, Bouhouita-Guermech and Haidar analyzed 136 articles. The
findings revealed an absence of a well-defined framework for assigning responsibility
in the use of AI within health care. They also highlighted the critical need for the
ethical and accountable development and deployment of AI technologies in this field.[27]
A Scientific Statement from the American Heart Association suggested that companies
should apply to the Food and Drug Administration (FDA) for approval to market an algorithm.
After approval, postmarket safety monitoring should be conducted similarly to phase
IV drug evaluations. During this phase, if the algorithm's use results in adverse
events or system failures, the developers are responsible for reporting and investigating
these outcomes. Consequently, a physician's liability in the event of an incorrect
decision and potential harm, as with any medical product, is limited to ensuring the
algorithm is used as “labeled,” thereby minimizing liability concerns.[22] While clinicians are expected to use AI/ML algorithms in accordance with their intended
use, it is still unclear whether they will be held legally accountable for any harm
resulting from these tools. Additionally, it remains uncertain whether the integration
of such technologies will redefine the standard of care in clinical practice.
Nonmedical staff can be trained quickly to conduct basic cardiac procedures such as
echocardiography and pacing device interrogation. However, the responsibility for
the accurate execution and reporting of these cardiac investigations should remain
with the qualified clinician.[2]
Regulation and Oversight
The advancement of AI in cardiology is progressing quickly, sometimes faster than
regulatory bodies can evaluate and approve these tools. Insufficient oversight could
result in the early deployment of technologies that have not been fully tested, potentially
increasing patient risk. It is important for AI tools to undergo thorough validation
on diverse, real-world data sets. Just as computer-assisted ECG analysis still requires
cardiologist oversight, expert clinicians must remain involved to prevent potential
AI-related harm.[3]
The use of AI in clinical decision-making must align with privacy laws and ethical
guidelines. AI deployment requires adherence to FDA guidelines, patient data protection
laws (Health Insurance Portability and Accountability Act, General Data Protection
Regulation), and ethical AI governance.
Transparency and Explainability
Transparency and Explainability
In health care, transparency means being able to access information about how AI and
ML algorithms work and make decisions. Explainability is about ensuring stakeholders
can understand and interpret the results from these algorithms in a clear way.[28]
A frequent critique of utilizing AI in health care is sophisticated technology's “black
box” characteristic. This term describes the perceived lack of understanding among
health care providers about AI systems' development, reliability, testing, and potential
issues. If an AI system predicts a high risk of arrhythmia, for example, the clinician
may not understand which features contributed to the prediction.
Creating explainable AI is a key focus for policymakers to foster trust in new systems.
Offering a detailed explanation of how complex algorithms work is challenging for
intricate ML models. A certain degree of comprehension of these algorithms may assist
patients in giving IC for using AI in their medical treatment.
Outputs produced by AI systems should be clearly labeled as such and, when possible,
supplemented with explanatory information.[3] Ensuring transparency and providing understandable reasoning behind AI and ML recommendations
are essential to fostering trust among health care professionals and patients, thereby
supporting informed clinical decision-making.[2]
[28] Patients may be reluctant to trust AI-derived recommendations if they view the system
as impersonal or prone to errors. It is crucial to ensure that patients comprehend
how AI assists in their care, while also highlighting its limitations. Understanding
AI system accountability, fairness, transparency, and explainability is paramount
for user trust.
Conclusion
AI is transforming cardiovascular care by enhancing diagnostics, treatment strategies,
and patient outcomes. Recent advances focus on improving imaging techniques, disease
detection, and risk prediction using ML and deep learning models. AI-driven solutions
now extend across ECG, echocardiography, CMR, nuclear cardiology, coronary angiography,
and others.
AI has surpassed traditional methods in many areas but remains limited by the need
for extensive labeled data sets, potential model overfitting, and transparency and
bias issues. While AI has the potential to enhance human intelligence, it is unlikely
to serve as a complete substitute. Human qualities such as creativity, adaptability,
and critical thinking remain indispensable, particularly in complex domains like scientific
inquiry and various aspects of daily life.
Several ethical concerns regarding the use of AI in cardiology need to be addressed.
A multidisciplinary approach is essential, involving: Transparent AI development processes,
robust regulatory frameworks, ongoing education for clinicians and patients, ensuring
data sets represent all demographic groups, and establishing clear guidelines for
developing, validating, and deploying AI tools. Organizations such as the World Health
Organization, the U.S. FDA, and the American Medical Informatics Association have
emphasized the need to incorporate ethical principles into the development and use
of emerging technologies. By addressing these ethical issues through policies, collaboration,
and research, AI can be integrated into cardiology to improve care while adhering
to ethical standards.
