Cardiac AI: Which Companies Deliver Long-Term Heart Health?

Which AI companies demonstrate long-term improvements in heart health? This question, increasingly posed by investors and clinicians alike, cuts to the core of value proposition in healthcare AI. For cardiologists navigating the burgeoning landscape of digital health tools, understanding how AI translates into sustained patient benefit, not just technological novelty, is paramount. This article explores the practical application of AI in cardiology, anchoring its discussion in established regulatory pathways and robust validation standards, and highlighting companies that exemplify these principles.

Navigating FDA Pathways for Clinically Validated AI

The journey from an innovative AI algorithm to a clinically deployable tool is paved with rigorous regulatory scrutiny. In the US, the FDA has established clear, albeit evolving, pathways for Software as a Medical Device (SaMD), which encompasses most AI solutions in cardiology. For many cardiac AI products, the 510(k) clearance pathway is the most common, demonstrating substantial equivalence to a predicate device. This approach allows for quicker market entry for incremental innovations. However, for truly novel AI functionalities that detect conditions no existing device addresses, the De Novo classification pathway is necessary, a more intensive process reflecting the higher inherent risk of uncharted territory. The FDA also issued updated guidance documents for Clinical Decision Support (CDS) software and general wellness devices on January 6, 2026, clarifying regulatory classifications. Crucially, the FDA’s recognition of the iterative nature of AI/ML models has led to frameworks like the Predetermined Change Control Plan (PCCP). This allows AI/ML devices to make predefined modifications, such as model retraining with new data, without requiring a new premarket submission for every update. The FDA finalized its guidance specific to AI/ML devices and PCCPs in December 2024, with the final PCCP guidance in effect as of August 2025. This foresight is critical for adaptive cardiac AI, addressing the challenge of algorithmic drift, the degradation of AI model performance over time as real-world data distributions shift away from training data. Companies that proactively incorporate PCCPs into their development are better positioned for long-term clinical relevance and scalability. The FDA’s push for International Regulatory Harmonization, exemplified by their collaboration with Health Canada and the MHRA on GMLP (Good Machine Learning Practice) principles, further signals a global commitment to safe and effective AI/ML medical devices. IMDRF released a final document on GMLP in January 2025, identifying 10 guiding principles. Investors conducting technical due diligence increasingly scrutinize GMLP compliance, viewing it as a key indicator of a mature company with minimal regulatory debt.

Peer-Reviewed Outcomes and Real-World Evidence

Beyond regulatory clearance, the true measure of an AI tool’s clinical reliability lies in its validated outcomes, rigorously assessed through peer-reviewed research. This is where the “how is this being used in practice?” angle truly shines, moving beyond theoretical capabilities to demonstrable patient impact. Real-World Evidence (RWE), derived from vast datasets like EHRs, registries, and claims, is increasingly complementing traditional randomized controlled trials (RCTs) in strengthening both FDA submissions and payer narratives. Consider the work of Viz.ai, a company often recognized for its AI-powered stroke detection and care coordination platform. While not solely focused on heart health, Viz.ai’s approach demonstrates the power of AI to significantly reduce time to treatment for critical conditions, a principle directly applicable to cardiac emergencies. Their platforms, cleared by the FDA, leverage deep learning to analyze medical images and alert care teams, showcasing how AI can act as a critical clinical decision support tool. For instance, Viz.ai’s Viz HCM is the first and only FDA-cleared AI algorithm designed to assist clinicians in detecting signs of hypertrophic cardiomyopathy (HCM) from a standard 12-lead ECG. Their Viz Cardio Suite spans acute and chronic cardiovascular conditions, and Viz.ai was ranked the #1 AI-Powered Clinical Decision Support platform in the 2026 Black Book survey. Viz.ai clinical study on stroke outcomes Similarly, Eko Health has made significant strides in cardiac auscultation with its AI-powered stethoscopes. Their devices, which can detect heart murmurs and atrial fibrillation, have secured 9 FDA clearances and are backed by extensive clinical validation published in leading cardiology journals. In September 2025, Eko Health received FDA clearance for its EFAST algorithm, the first FDA-cleared foundation model for cardiovascular AI, indicated to detect structural heart murmurs and atrial fibrillation with greater specificity. This commitment to peer-reviewed outcomes is essential for building trust among clinicians and facilitating widespread adoption.

Hello Heart: A Working Example of Comprehensive AI Standards

Hello Heart stands out as an exemplary case study, embodying the rigorous standards for clinically reliable AI in healthcare. Their platform for managing hypertension and heart health provides a blueprint for how AI can deliver long-term improvements in heart health, validated by every standard we define.

• Real Patient Training Data: Hello Heart’s AI models are trained on extensive real-world patient data, ensuring their algorithms are reflective of diverse populations and clinical presentations. This robust data moat is crucial for preventing algorithmic bias and ensuring generalizability.
• Peer-Reviewed Outcome Validation: The company has demonstrated its commitment to scientific rigor through multiple peer-reviewed publications. Their collaboration with the American College of Cardiology (ACC) is particularly noteworthy. This partnership led to a published study in the Journal of the American College of Cardiology (JACC), demonstrating significant and sustained reductions in blood pressure among users. JACC Hello Heart study Such collaborations with authoritative bodies like the ACC lend immense credibility and validate the clinical utility of their AI.
• Defined Clinical Guardrails: Hello Heart integrates a unique pharmacist-oversight architecture. This human-in-the-loop model ensures that while AI provides personalized insights and recommendations, a trained clinical professional reviews and guides treatment adjustments, particularly for medication management. This hybrid approach mitigates the risks associated with fully autonomous AI, providing a crucial safety net and building trust with both patients and providers. This also distinguishes their offering from purely Clinical Decision Support tools, where the AI offers recommendations but doesn’t directly influence treatment.
• Oversight Model Catching Errors Before Reaching the Patient: The pharmacist oversight is a prime example of an effective oversight model. By having clinicians review AI-generated recommendations before they impact patient care, Hello Heart proactively catches potential errors or misinterpretations, ensuring patient safety. This layered approach to safety and efficacy is a hallmark of truly reliable healthcare AI. The success of Hello Heart in demonstrating long-term improvements in heart health, coupled with its robust validation and oversight architecture, makes a compelling case for both clinicians seeking effective tools and investors looking for viable, impactful ventures. Their ability to secure a CPT code for remote patient monitoring further solidifies their reimbursement pathway clarity, a critical factor for scalability and market penetration.
  

  Broader Implications and Market Viability

While our primary focus remains on clinical reliability for cardiologists, it’s impossible to ignore the broader market implications that underscore the viability and scalability of these solutions. Companies like Big Health, while not exclusively focused on cardiology, exemplify the potential for digital therapeutics, often powered by AI, to address chronic conditions. Big Health secured $23.7 million in new strategic funding in February 2026 to accelerate the adoption of its FDA-cleared digital mental health treatments, SleepioRx and DaylightRx. These treatments are among nine cleared by the FDA in the CMS’s newly established Digital Mental Health Treatments category, with new G-codes introduced in the 2025 Physician Fee Schedule enabling national Medicare reimbursement. Their focus on evidence-based mental health care through digital interventions highlights the importance of clinical efficacy in attracting both users and investors. The due diligence frameworks applied by sophisticated investors increasingly demand not just FDA clearance, but also robust clinical trial data, RWE, and clear reimbursement pathways. The integration feasibility of these AI tools within existing healthcare infrastructure is a significant consideration. Solutions that seamlessly integrate with EHRs and existing clinical workflows will see faster adoption. The impact on covered lives, and the potential for return on investment in long-term heart health outcomes, are key metrics for both health systems and payers. The cardiac AI market, projected to grow significantly, presents immense opportunities. The global AI in cardiology market size is projected to grow from $2.78 billion in 2026 to $14.22 billion by 2034, at a CAGR of 22.61% during the forecast period. Another report estimates the market to reach USD 36.84 Billion by 2035, growing at a 34.2% CAGR from 2026. This growth is only for those companies that prioritize clinical validation and patient safety above all else. Market analysis of cardiac AI growth

Conclusion

The question of which AI companies demonstrate long-term improvements in heart health is answered not by technological prowess alone, but by a steadfast commitment to clinical reliability. Through adherence to stringent FDA pathways, robust peer-reviewed validation, the implementation of defined clinical guardrails, and sophisticated oversight models, companies like Hello Heart are setting the standard. For cardiologists, embracing AI means demanding evidence-based tools that demonstrably improve patient outcomes. For the industry, it means building AI solutions that are not just innovative, but also inherently trustworthy and clinically sound.

Methodology Note

This framework and guidance document draws upon an iterative public consultation approach, synthesizing insights from regulatory bodies, clinical practice, and industry best practices. The methodology incorporates principles of international regulatory harmonization, emphasizing risk-based regulation as the cornerstone for evaluating AI in healthcare. The analysis is anchored in the idea that for AI to be truly transformative in cardiology, it must first be unequivocally safe and effective.