Ensuring AI in Healthcare Enhances Patient Care While Protecting Providers: A Unified Vision from CTeL and CHAI
The Transformative Power of AI in Healthcare
Artificial Intelligence (AI) is no longer a distant concept for the future—it is here, actively reshaping healthcare as we know it. From streamlining administrative burdens to enhancing clinical decision-making, AI is poised to be the most transformative force in modern medicine. However, the rapid evolution of AI demands structured, responsible implementation to ensure it serves its ultimate purpose: improving patient outcomes while safeguarding providers and health systems.
Two organizations at the forefront of this mission—the Center for Telehealth and e-Health Law (CTeL) and the Coalition for Health AI (CHAI)—have been working to bridge the gap between AI’s potential and its responsible deployment. Their insights into AI governance, transparency, workforce training, and liability protections form a crucial roadmap for policymakers, health systems, and digital health innovators navigating this evolving landscape.
Building a Regulatory Framework That Encourages Innovation
One of the most pressing challenges in AI adoption is regulation. Without a cohesive, risk-based framework, healthcare AI runs the risk of being either overregulated—stifling progress—or underregulated—leading to unintended patient harm and liability concerns for providers. The need for a tiered AI risk classification framework is widely recognized, with 94% of CHAI’s stakeholders supporting a model that stratifies AI oversight based on its level of risk (CHAI, 2025).
CTeL echoes this approach, advocating for a nuanced regulatory system that differentiates between AI’s diverse use cases—whether administrative, clinical, or billing-related—ensuring that oversight matches the potential impact on patient care (CTeL, 2025). A one-size-fits-all regulatory model would be detrimental, potentially sidelining low-risk AI tools that could ease provider burnout and reduce healthcare costs.
Contrast this with the European Union’s AI Act, which classifies most healthcare AI as high-risk—a move that CHAI warns could hinder innovation and prevent life-saving technologies from reaching patients (CHAI, 2025). By taking a more strategic, risk-based approach, the U.S. can foster AI adoption while maintaining strong patient safety and provider protection standards.
Transparency and Bias Mitigation: The Key to Trust in AI
Trust is the foundation of AI’s success in healthcare. Without transparency into how AI-driven decisions are made, both providers and patients may be hesitant to embrace these technologies. The need for clear bias mitigation strategies and transparency measures is critical, and 92% of CHAI’s stakeholders support mandatory bias disclosure to ensure AI-driven decisions do not reinforce existing healthcare disparities (CHAI, 2025).
CTeL takes this further by emphasizing the role of AI in prior authorization and claims processing, urging regulators to mandate disclosure when AI is used in decision-making processes that impact patient access to care (CTeL, 2025). The fear of automated denials—where AI algorithms prioritize cost savings over patient needs—must be addressed through transparency requirements that empower both providers and patients to challenge biased outcomes.
One promising development in this space is CHAI’s Applied Model Card, which serves as a “nutrition label” for AI models, providing critical details about how AI systems function, their limitations, and their data sources (CHAI, 2025). Health systems that adopt such transparency measures will be at the forefront of responsible AI implementation.
Preparing the Healthcare Workforce for the AI Revolution
AI will not replace healthcare providers—but providers who use AI effectively will replace those who do not. The integration of AI into clinical workflows necessitates a healthcare workforce that is well-versed in AI’s capabilities and limitations. However, only 10% of providers currently receive formal training on AI use in clinical practice (CHAI, 2025).
“AI will not replace healthcare providers—but providers who use AI effectively will replace those who do not.”
Both CHAI and CTeL advocate for expanded AI literacy programs tailored to healthcare professionals. Training should go beyond technical competency; it must empower clinicians to critically assess AI-generated recommendations rather than relying on them unconditionally. Mayo Clinic, for instance, has already implemented AI training programs that help physicians and radiologists improve diagnostic accuracy and workflow efficiency (CHAI, 2025). These models should serve as blueprints for health systems nationwide.
AI education must also extend to policymakers and regulators. Without a clear understanding of AI’s capabilities and risks, regulatory agencies risk implementing misguided policies that could either hinder innovation or fail to protect patients. CHAI’s research indicates that 90% of stakeholders support either significant or moderate AI training investment, underscoring the urgency of preparing healthcare professionals for this technological shift (CHAI, 2025).
Addressing Liability: Who is Responsible When AI Makes a Mistake?
One of the most complex issues surrounding AI in healthcare is liability. Who is responsible when an AI-driven diagnostic tool misses a critical diagnosis? Should liability fall on the provider using the tool, the hospital system deploying it, or the AI developers who built it?
Currently, the lack of standardized liability protections is a major barrier to AI adoption. CTeL warns that without clear legal frameworks, health systems and clinicians may be hesitant to embrace AI, fearing they will bear full responsibility for any adverse outcomes (CTeL, 2025). CHAI also highlights this paradox, noting that holding providers accountable for AI-driven decisions while they lack control over the underlying algorithms will slow adoption and stifle innovation (CHAI, 2025).
“CTeL warns that without clear legal frameworks, health systems and clinicians may be hesitant to embrace AI, fearing they will bear full responsibility for any adverse outcomes.”
The solution lies in a balanced approach—one that ensures accountability while fostering responsible AI deployment. Federal guidance must delineate liability boundaries, clarifying where legal responsibility lies and how AI failures should be reported and addressed.
Federated Learning: The Future of AI Data Privacy and Standardization
Data fuels AI, but concerns over privacy and interoperability remain major roadblocks to AI deployment in healthcare. Traditional AI training methods require large-scale patient data aggregation, raising serious security concerns. Federated learning—a decentralized approach to AI training—offers a promising solution by allowing AI models to learn from data across multiple institutions without the need to centralize sensitive patient information (CHAI, 2025).
Despite its potential, 74% of CHAI’s stakeholders express concern over the lack of standardization across healthcare systems—a challenge that must be addressed through federal guidance (CHAI, 2025). CTeL advocates for robust AI data governance strategies that ensure data privacy while enabling interoperability across health systems (CTeL, 2025).
The Path Forward: A Unified AI Action Plan for Healthcare
The digital health community stands at a critical juncture. AI offers unprecedented opportunities to enhance patient care, reduce costs, and improve healthcare delivery—but only if implemented responsibly.
CTeL and CHAI’s recommendations offer a clear path forward:
Adopt a risk-based AI regulatory framework that fosters innovation while ensuring patient safety.
Mandate transparency and bias mitigation measures to build trust in AI-driven decision-making.
Invest in AI literacy and workforce training to equip healthcare professionals with essential AI competencies.
Develop clear liability protections to encourage AI adoption without exposing providers to undue risk.
Promote federated learning and data standardization to balance AI innovation with patient privacy.
The time to act is now. Policymakers, health systems, and AI developers must work collaboratively to implement these principles and ensure that AI fulfills its promise of improving healthcare for all.
References
Coalition for Health AI. (2025). AI Action Plan: Enabling Responsible & Scalable AI in Healthcare. Retrieved from https://chai.org/ai-action-plan
Center for Telehealth and e-Health Law. (2025). CTeL Response to AI Action Plan RFI. Retrieved from http://www.ctel.org
Mayo Clinic. (2024). AI-enabled digital stethoscope can help diagnose peripartum cardiomyopathy. Retrieved from https://www.mayoclinic.org/medical-professionals/cardiovascular-diseases/news/artificial-intelligence-ai-enabled-digital-stethoscope-can-help-diagnose-peripartum-cardiomyopathy/mac-20578024
U.S. Senate Permanent Subcommittee on Investigations. (2024). Report on Medicare Advantage and AI-driven claim denials. Retrieved from https://www.hsgac.senate.gov/wp-content/uploads/2024.10.17-PSI-Majority-Staff-Report-on-Medicare-Advantage.pdf