Risk-Based Frameworks for AI in Healthcare

Artificial intelligence has become one of the most promising tools in modern healthcare, offering the potential to improve diagnostics, personalize treatments and streamline clinical workflows. Yet beneath this optimism lies a widening gap between regulatory clearance and real-world responsibility. Many AI tools approved for clinical use still lack rigorous fairness testing, consistent monitoring or safeguards against unintended harm. A new science advisory from the American Heart Association signals a turning point by proposing a practical, risk-based framework aimed at elevating clinical AI from experimental curiosity to trustworthy infrastructure. (American Heart Association)

This article examines the current state of healthcare AI, highlights the AHA’s recommendations and explores what it will take to make AI both beneficial and safe for patients and providers.

Introduction: Promise and pitfalls of AI in healthcare

AI-driven systems in healthcare offer transformative possibilities. Machine-learning models can outperform clinicians in specific imaging tasks, predict patient deterioration hours earlier than conventional tools, and enhance operational efficiency in overstretched clinical environments. These systems also hold promise for supporting underserved regions where specialist access is limited.

But as adoption grows, so do concerns. AI models trained on skewed datasets can propagate or amplify existing inequities, disproportionately affecting marginalized populations. Black-box systems often deliver predictions without clear explanations, complicating clinical decision-making. And once deployed, many models degrade when applied to populations or environments that differ from their training data.

These concerns underscore a central challenge: the technology is advancing faster than the frameworks meant to govern it.

Current state: FDA clearance vs real-world scrutiny

In the United States, dozens of clinical AI/ML devices have been cleared by the FDA. Yet clearance does not guarantee responsible use. FDA review focuses principally on safety and effectiveness under specific conditions and datasets, but it is not designed to provide ongoing oversight once a model is deployed. (American Heart Association)

Moreover, many institutions adopt AI tools without full visibility into how they are developed or validated. Research has documented a lack of standardized reporting practices, limited transparency into training data and inconsistent post-market performance monitoring. The result is a regulatory-compliance gap: tools may be legally cleared but not rigorously evaluated for fairness, bias, generalizability or long-term patient impact.

This gap is precisely what the AHA advisory aims to address.

New AHA advisory: key elements of the risk-based framework

According to the AHA, the healthcare community urgently needs a structured approach that accounts for both clinical risk and algorithmic uncertainty. (professional.heart.org) Their proposed framework emphasizes several core components:

1. Risk stratification based on clinical impact

Not all AI models pose equal risk. An administrative scheduling tool and a diagnostic classifier do not carry the same potential for harm. The advisory recommends categorizing AI systems by the severity of possible negative outcomes, similar to medical device classifications. Higher-risk models warrant deeper validation, more extensive bias evaluation and stricter monitoring.

2. Fairness and bias assessment as mandatory, not optional

The advisory highlights a persistent problem: many FDA-cleared tools have undergone little or no fairness analysis. The AHA proposes that bias testing be integrated throughout the lifecycle, including model development, post-deployment monitoring and updates. Importantly, fairness checks must consider intersectional attributes (such as race, gender, age and socioeconomic status), not just isolated demographic factors.

3. Lifecycle governance and continuous monitoring

AI performance is not static. Models drift as populations, clinical practices and data pipelines evolve. The advisory emphasizes ongoing monitoring for calibration, accuracy, error rates and disparities. Monitoring should be continuous, transparent and accompanied by clear processes for remediation. (American Heart Association)

4. Documentation and transparency requirements

To reduce the “black box” barrier, the advisory advocates for standardized reporting templates detailing model architecture, training data characteristics, validation methods and known limitations. This transparency can help clinicians make informed decisions and allow institutions to perform independent evaluations.

5. Cross-disciplinary oversight and accountability

Implementing responsible AI requires collaboration among clinicians, data scientists, ethicists, compliance teams and patient representatives. The AHA calls for governance structures that formally integrate these voices, aligning technical design decisions with clinical and ethical responsibilities.

Implementation challenges: fairness, bias and continuous monitoring

While the AHA guidance offers a strong foundation, implementation will not be easy. Healthcare institutions face several obstacles:

Data quality and representativeness

Clinical datasets often under-represent key populations, including racial and ethnic minorities, rural patients and those with rare diseases. Even large hospital networks may lack the data diversity required for robust fairness evaluation. Without representative data, bias testing can produce false assurances. (arXiv)

Technical capacity constraints

Deploying and monitoring AI responsibly requires significant infrastructure—model observability tools, expertise in machine-learning operations (MLOps), statistical evaluation skills and governance maturity. Many health systems, especially community hospitals, lack the resources to operationalize these capabilities.

Regulatory fragmentation

The FDA’s evolving framework for adaptive AI/ML Software as a Medical Device (SaMD) does not yet fully address fairness or long-term monitoring requirements. Meanwhile, state, federal and professional-association guidelines vary widely, creating confusion for developers and clinicians.

Vendor transparency limitations

Commercial AI tools often provide limited visibility into model internals, including training data sources and feature importance. Without transparency, institutions cannot adequately assess fairness or safety risks.

Workflow integration

Even well-validated AI systems can introduce risk if poorly integrated into clinical workflows. Alert fatigue, unclear recommendations or conflicting signals can erode clinician trust and affect patient care.

Implications for providers, regulators and AI developers

For healthcare providers

Hospitals and clinics must move beyond passive adoption toward active governance. This means establishing AI oversight committees, requiring detailed transparency documentation from vendors, implementing performance dashboards and training clinicians to critically interpret algorithmic recommendations.

For regulators

The AHA framework can complement emerging regulatory reforms. Incorporating fairness assessments, robustness testing and post-market monitoring into FDA processes would help close the current oversight gap. Regulators may also consider requiring algorithmic impact assessments for high-risk clinical AI tools.

For AI developers

Developers must design with real-world uncertainty in mind. This includes using diverse datasets, documenting known limitations, building mechanisms for model retraining and enabling end-user interpretability. Proactive transparency will increasingly be a competitive advantage, not merely a compliance obligation.

Conclusion: next steps for responsible health-AI deployment

The AHA advisory represents a meaningful step toward harmonizing innovation with accountability in clinical AI. By embracing a risk-based framework, the healthcare sector can move from fragmented experimentation to structured, evidence-based deployment.

The path forward requires sustained cooperation: clinicians who understand the stakes, developers who take responsibility for fairness and safety, and regulators who adapt oversight to match the realities of algorithmic systems. With this shared commitment, AI can fulfil its potential as a trustworthy partner in healthcare, improving patient outcomes while upholding the principles of safety, fairness and equity.

References

1. American Heart Association. “New guidance offered for responsible AI use in health care.” Nov 10 2025. URL: https://newsroom.heart.org/news/new-guidance-offered-for-responsible-ai-use-in-health-care (American Heart Association)
   
2. American Heart Association. “Pragmatic Approaches to the Evaluation and Monitoring of Artificial Intelligence in Health Care.” Circulation (2025). (AHA Journals)
   
3. Lekadir, K., et al. “FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare.” arXiv (2023). (arXiv)
   
4. Weiner, E. B., et al. “Ethical Challenges and Evolving Strategies in the Integration of Artificial Intelligence into Clinical Practice.” arXiv (2024). (arXiv)