AI Resource Library

Artificial intelligence (AI) and digital technology advances have emerged as catalysts for changing how patients are diagnosed, managed and treated. The following collection of AI resources has been curated to support ACC members in understanding and applying AI in the digital transformation of care delivery.

Evaluating and Selecting AI for Clinical Practice

Frameworks and tools to assess AI solutions.

• Mastering Key Assessment Criteria
  How to confidently evaluate AI solutions for real-world clinical settings.
• Navigating the AI Landscape
  A guide to choosing the right tool for your specific medical practice
• Guiding the AI Revolution in Cardiovascular Medicine
  Consider these five guiding principles for responsible AI in the transformation of Cardiovascular Medicine.
• Analysis of FDA-Approved Artificial Intelligence and Machine Learning–Enabled Cardiovascular Devices | JACC
  Learn about the characteristics, approval pathways, clinical applications, and demographic reporting of FDA-approved cardiovascular (CV) devices that incorporate artificial intelligence (AI) and machine learning (ML).

Clinical Applications and Outcomes

Information focused on how AI is used in cardiology to improve patient care.

• AI in Cardiology: Improving Outcomes for All | JACC
  Innovations reshaping cardiovascular practice.
• Artificial Intelligence for Cardiovascular Care | JACC
  Part 1 (Advances) & Part 2 (Applications)
  An overview of recent advancements in AI that are enhancing diagnosis, treatment, and outcomes in cardiovascular care, with a focus on multimodal data, generative AI, and AI-augmented care.
• Prospective Human Validation of AI Interventions  | JACC
  Examine AI interventions in cardiology that have undergone prospective validation against expert standards, assessing their effectiveness and identifying opportunities for future research.
• Impact of AI-Enhanced Insertable Cardiac Monitors | JACC
  Learn how AI integration in insertable cardiac monitors can reduce nonactionable alerts, streamline clinic workflows and enhance resource efficiency.
• Automated Assessment of Right Atrial Pressure | JACC
  Evaluating whether machine learning models can accurately estimate RAP from echocardiogram data.
• AI in Cardiovascular Imaging
  Webinar Part 1 &  Webinar Part 2
  Essential knowledge on applications of AI in CV imaging.
• Artificial Intelligence Automation of Echocardiographic Measurements | JACC
  Explore how an open-source deep learning model can reduce clinician workload, improve measurement consistency, and enhance diagnostic efficiency in cardiovascular care.

Health Equity, Ethics and Assessment

Content addressing the safe, responsible, and ethical use of AI.

• Disparities in AI-Based Tools Among Diverse Minority Populations | JACC
  Explore how AI biases can deepen health disparities among minority population and why inclusive, representative data is essential for equitable health care innovation.
• Health Equity and AI in Health Systems | JACC
  Democratizing health care with AI while navigating regulatory challenges.

Training, Education and Research

Resources for clinicians, fellows, and researchers to stay ahead of the curve and integrate AI into their work.

• Embracing AI in Cardiovascular Disease Fellowship | JACC
  Integrating AI education into next-gen clinician training.
• AI in Cardiovascular Clinical Trials | JACC
  Streamlining recruitment and data interpretation by leveraging AI.
• Embracing Generative AI in Clinical Research | JACC
  Opportunities and evolving workflows in research.
• Tools, Not Ghosts: Artificial Intelligence, Writing, and Responsibility  | JACC
  Addresses the evolving role of AI tools in scientific writing, emphasizing that AI should be viewed as a tool, not a ghostwriter.
• Randomized Controlled Trials Evaluating Artificial Intelligence in Cardiovascular Care | JACC
  Review of RCTs evaluating the impact of AI in cardiovascular care, focusing on clinical outcomes and operational efficiency.
• Innovation in Action: Training AI Screening of Cardiac Amyloidosis
  Learn more about AI model training, and how to leverage AI for early screening of cardiac amyloidosis.
• ACC Anywhere
  Log in or activate your account to access ACC's on-demand video library. Search "Artificial Intelligence" for curated content from ACC's live meetings and more.†

Future Trends and Emerging Tech

High-level analyses into what's next, including Generative AI, LLMs, and new digital health tools.

• Moving Beyond the Model: Our Perspective on Meaningful AI Research in Cardiovascular Care | JACC
  Understand why AI research in cardiovascular care must go beyond retrospective model performance and demonstrate clinical relevance and implementation feasibility.
• AI: An Opportunity to Revolutionize Health Care?
  Investigating the broad potential of AI in cardiology.
• Novel Utilization of Large Language Models (LLMs) in Care Delivery
  Integrating AI language models into patient care.
• Impact of AI on Interventional Cardiology | JACC
  From decision-making aids to procedure assistance.

AI Regulations and Policies

Resources and frameworks to help navigate the evolving global regulatory landscape established by governments and medical societies to ensure AI is trustworthy, transparent, and safe.

United States National Frameworks

Content focusing on high-level federal strategy.

• America's AI Action Plan
  The overarching national framework for AI adoption, focused on innovation and global standards.
• ACC Comments on Federal AI Action Plan
  The American College of Cardiology's comments on maintaining clinician autonomy and unified governance around the adoption on AI.
• White House Executive Order on AI Leadership
  A directive aimed at streamlining regulations to accelerate AI development while maintaining oversight.
• NIST AI Risk Management Framework
  A voluntary tool to help organizations integrate "trustworthiness" (fairness, safety, and privacy) into AI products.
• NIST Zero Draft for AI Testing & Validation
  Proposed zero draft standard for AI testing, evaluation, verification, and validation, providing a high-level framework to guide how AI systems should be assessed across their lifecycle.
• ACC Comments on Federal AI Action Plan:
  The American College of Cardiology's comments on maintaining clinician autonomy and unified governance around the adoption on AI.

AI Standards

Direct mandates for clinical practice and health IT.

• ONC's HTI-1 Final Rule
  Established critical transparency requirements for AI in certified health IT (with enforcement discretion active through February 28, 2026).

European Union and International Guidelines

• E.U. AI Act
  Regulatory framework categorizing AI by risk.
• E.U. AI Act Compliance Checker
  A practical tool to help developers and users determine their obligations under the new law.
• WHO Ethics and Governance of AI for Health
  Outlines the ethical challenges, risks, and opportunities associated with using AI technologies in health care