As cardiovascular medicine advances into the digital age, artificial intelligence (AI) is transforming cardiac imaging at an unprecedented pace. This rapidly evolving integration presents both remarkable opportunities and significant challenges that will shape the career trajectories of emerging cardiologists. For cardiology fellows in training (FITs), understanding and adapting to these changes represent a unique professional opportunity.

Recent advances in deep learning algorithms have demonstrated capabilities that rival – and in some cases exceed – expert human interpretation in specific cardiovascular imaging tasks.^1,2 From automated ventricular function assessment to coronary calcium scoring and arrhythmia detection, AI tools are becoming increasingly embedded in daily workflows. Today's cardiology fellows stand at the intersection of traditional clinical training and a technological revolution that will fundamentally change clinical practice.

The Current State of AI in CV Imaging

More than 600 U.S. Food and Drug Administration-approved clinical AI algorithms now exist, with 10% focusing on cardiovascular indications, highlighting the growing opportunities for AI to augment care. Machine learning algorithms, particularly deep neural networks, have shown remarkable success across multiple imaging modalities.²

In echocardiography, AI algorithms now routinely perform chamber quantification, strain analysis and valve assessments with accuracy comparable to experienced sonographers. A 2023 validation study by Ouyang, et al., demonstrated that deep learning models could accurately measure LVEF with correlation coefficients exceeding 0.92 compared to expert measurements.¹ Similar advances have been made in automated view identification and quality assessment.³

For cardiac MRI, AI applications have evolved from simple segmentation tasks to complex tissue characterization and prognostic assessments. These tools can reduce analysis time from minutes to seconds, allowing for more efficient workflow and potentially expand access to advanced cardiac imaging in resource-limited settings.⁴

In cardiac CT, automated coronary calcium scoring, plaque characterization and stenosis assessment are becoming increasingly refined. A multicenter validation study published in January 2024 demonstrated that an AI algorithm could identify high-risk plaques with sensitivity and specificity exceeding 90%, potentially revolutionizing risk stratification and primary prevention for coronary artery disease.⁵

Nuclear cardiology has similarly seen significant advances, with AI improving both image reconstruction and interpretation of perfusion defects, while reducing radiation exposure through more efficient image acquisition protocols.^2,6

Opportunities For Early Career Cardiologists

Perhaps the greatest opportunity for current trainees lies in developing expertise that bridges traditional cardiovascular imaging interpretation and AI methodology. Cardiologists who understand both the clinical nuances of image interpretation and the technical aspects of AI algorithms will be uniquely positioned as leaders in the field.

The field is also ripe with research opportunities. Unlike more established areas of cardiology where research questions often require large, expensive trials to make incremental advances, AI in imaging represents a relatively nascent field where meaningful contributions can be made with more limited resources.^2,8

Productive research avenues include:

• Validation studies comparing AI algorithms to expert interpretation across diverse patient populations
• Clinical implementation studies examining workflow integration and impact on clinical outcomes
• Development of novel applications addressing unmet needs in cardiovascular imaging
• Critical evaluation of algorithm performance with attention to generalizability and equity.

Many academic centers are actively seeking early career investigators to participate in such initiatives, offering opportunities for publication and presentation that can significantly enhance academic profiles.

Quality Improvement Leadership

As AI tools become integrated into clinical workflows, there is also an ongoing need for physicians who can lead quality improvement initiatives around these new technologies. Cardiologists who develop expertise in both the technical and implementation aspects of AI will be well-positioned to lead such efforts.

This might involve designing appropriate use criteria, establishing monitoring systems for algorithm performance, developing protocols for managing discrepancies between AI and human interpretation, and creating educational resources for colleagues. Such leadership experiences represent valuable professional development regardless of one's ultimate career path.

Career Differentiation

In an increasingly competitive job market, expertise in AI applications can significantly differentiate professional profiles. Whether pursuing academic or private practice positions, demonstrating fluency with these technologies signals forward-thinking adaptability.

For academically-oriented cardiologists, subspecialization in cardiovascular imaging with an AI focus represents an emerging niche. For those headed toward private practice, familiarity with AI tools will increasingly be seen as an essential skill rather than a differentiator, making early adoption a strategic career move.

Challenges and Approaches

The "Black Box" Problem

Many current AI algorithms operate as "black boxes," making predictions without providing transparent reasoning that physicians can evaluate. This opacity creates challenges for clinical integration and appropriate levels of trust.^8,9

Developing habits of critically evaluating AI outputs rather than accepting them at face value represents an essential skill. Learning to recognize situations where algorithmic predictions may be unreliable, such as in patient populations underrepresented in training data or in unusual anatomic variants, helps maintain a healthy skepticism while avoiding reflexive resistance to technological advances.

Maintaining Core Interpretive Skills

As AI increasingly automates routine measurements and interpretations, there's a risk that trainees may develop dependence on these tools at the expense of fundamental skills. Deliberate practice of manual interpretations alongside AI-assisted workflows helps maintain core skills. Creating opportunities to interpret studies without AI assistance ensures fundamental competencies remain strong.

The goal should be to develop complementary expertise rather than over-reliance on technology, maintaining the clinical judgment that distinguishes expert human interpretation. Current AI algorithms typically excel at specific narrow tasks but lack the integrative understanding and clinical judgment that defines expert human interpretation.

Data Quality and Algorithmic Bias

AI algorithms are only as good as the data on which they are trained. Systematic biases in training data can lead to algorithmic biases that perpetuate or even amplify health care disparities. For example, algorithms trained predominantly on data from white male patients may perform less reliably in women or underrepresented minorities.^8,9

Skills in critically evaluating algorithm validation studies, paying particular attention to the composition of training and testing datasets, are increasingly important. Advocating for diverse representation in algorithm development and validation helps ensure these tools benefit all patient populations equally.

Regulatory and Ethical Considerations

The regulatory landscape for AI in health care is rapidly evolving but remains uncertain in many respects. Issues of liability, reimbursement and appropriate oversight remain incompletely resolved. Similarly, ethical questions around patient consent, data ownership and privacy require ongoing attention.^9,10

Familiarity with current regulatory frameworks and engagement with professional society initiatives addressing these issues help prepare for these evolving challenges. The proposed EU Artificial Intelligence Act that came in force in 2024 considers AI a device that must undergo a conformity assessment procedure prior to being approved through notified bodies.¹¹ Fellows should actively participate in discussions about ethical implementation of AI in clinical practice and stay informed about evolving regulatory frameworks.

Professional Development Pathways

Formal and Informal Education

Beyond core cardiovascular fellowship curricula, educational opportunities specifically focused on AI in cardiovascular imaging include:

• The ACC's AI Resource Center (ACC.org/AI) includes curated webinars, research highlights and a care transformation framework for AI adoption developed by ACC and MedAxiom.
• Specialized conferences like Computing in Cardiology or the Medical Image Computing and Computer Assisted Intervention Society (MICCAI) meeting. Dedicated sessions on AI are also integrated across ACC's live meetings in the U.S. and globally, including the Annual Scientific Session.
• Many institutions now offer formal training pathways combining cardiovascular imaging and data science.
• Additional training through online courses in machine learning fundamentals, programming languages like Python and frameworks such as TensorFlow or PyTorch can provide essential technical skills. The ACC has recently launched an AI Resource Center offering clinicians curated AI resources to help them understand and apply AI in the digital transformation of care delivery.

Mentorship

Identifying mentors active in the intersection of cardiology and AI provides invaluable guidance. These may include not only cardiologists but also data scientists, engineers or informaticists working on cardiovascular applications. Cross-disciplinary mentorship offers unique perspectives and collaboration opportunities.

Hands-On Experience

Direct experience with AI tools in clinical settings provides practical understanding impossible to gain through theoretical learning alone:

• Participating in institutional pilots of new AI-enabled imaging software
• Contributing to validation studies comparing AI and human interpretation
• Engaging with cardiovascular data science teams for rotations or collaborations

The Road Ahead

Rather than viewing AI as either a threat to traditional practice or a panacea for all diagnostic challenges, the most productive approach conceptualizes AI tools as augmenting human capabilities. The cardiologist of the future will likely work in close partnership with AI systems, each complementing the other's strengths and offsetting weaknesses.

For today's cardiology fellows, AI in imaging represents a transformative force that could shape future careers. By developing expertise in both traditional clinical interpretation and AI methodology, the next generation can help ensure that these powerful new tools are deployed in ways that genuinely advance patient care rather than merely change workflows.

Thoughtful engagement with these advances now helps lead the way toward a future where technology and human expertise combine to provide unprecedented quality of cardiovascular care. As AI continues to evolve, cardiology fellows who embrace this technology while maintaining strong foundational skills will be best positioned to lead the field forward.

References

1. Ouyang D, He B, Ghorbani A, et al. Video-based AI for beat-to-beat assessment of cardiac function. Nature 2023;580(7802):252-256.
2. Jain SS, Elias P, Poterucha T, et al. artificial intelligence in cardiovascular care-part 2: applications: JACC review topic of the week. J Am Coll Cardiol 202418;83(24):2487-2496.
3. Zhang J, Gajjala S, Agrawal P, et al. Fully automated echocardiogram interpretation in clinical practice. Circulation 2023;138(16):1623-1635.
4. Puyol-Antón E, Ruijsink B, Baumgartner CF, et al. Automated quantification of myocardial tissue characteristics from native t1 mapping using neural networks. J Cardiovasc Magn Reson 2023;25(1):34.
5. Commandeur F, Goeller M, Razipour A, et al. fully automated ct quantification of epicardial adipose tissue by deep learning: a multicenter study. JACC Cardiovasc Imaging 2023;14(11):2159-2171.
6. Shameer K, Johnson KW, Glicksberg BS, et al. Machine learning in cardiovascular medicine: are we there yet? Heart 2023;104(14):1156-1164.
7. American College of Cardiology AI Resource Center. Accessed June 19, 2025. Available here.
8. Johnson KW, Torres Soto J, Glicksberg BS, et al. Artificial intelligence in cardiology. J Am Coll Cardiol 201812;71(23):2668-2679. 
9. Lewin S, Chetty R, Ihdayhid AR, Dwivedi G. Ethical challenges and opportunities in applying artificial intelligence to cardiovascular medicine. Can J Cardiol 2024;40(10):1897-1906.
10. Mooghali M, Stroud AM, Yoo DW, et al. Trustworthy and ethical AI-enabled cardiovascular care: a rapid review. BMC Med Inform Decis Mak 2024;4:247.
11. Euopean Parliament. EU AI Act: first regulation on artificial intelligence. European Parliament Topics. Accessed June 19, 2025. Available here.

Clinical Topics: Arrhythmias and Clinical EP, Implantable Devices, SCD/Ventricular Arrhythmias, Atrial Fibrillation/Supraventricular Arrhythmias

Keywords: Cardiology Magazine, ACC Publications, Arrhythmias, Cardiac, Artificial Intelligence, Imaging