AI in Cardiology: Is Informed Consent a New Investment Risk?

The integration of artificial intelligence into healthcare promises unprecedented advancements, from accelerating diagnostics to personalizing treatment plans. Yet, as AI-driven tools increasingly influence clinical recommendations, a critical ethical and practical question emerges: Should patients always be informed when AI contributes to their care decisions? This analytical question is not merely academic; it strikes at the core of patient autonomy, trust, and the very definition of informed consent in a technologically evolving medical landscape.

The Shifting Sands of Informed Consent in the Age of AI

The AI informed consent debate, whether patients should be notified when AI influences their care, has significant implications for trust and safety. Traditional informed consent emphasizes a patient’s right to understand their diagnosis, proposed treatments, and potential risks and benefits, empowering them to make autonomous decisions. The introduction of AI complicates this. When a clinician uses an AI-powered tool to, for instance, identify subtle patterns in medical imaging or predict disease progression, is that an AI “making” a recommendation, or merely assisting a human expert? The distinction is crucial for patient understanding and the allocation of responsibility. Multiple AI health companies are actively developing and deploying tools that provide clinical recommendations, ranging from risk stratification to treatment pathway suggestions. The opacity surrounding some of these systems, often due to proprietary algorithms and trade secrets, presents a challenge to truly informed consent. As Ruha Benjamin, a prominent scholar on race, science, and technology, has articulated, the “new Jim Code” embedded in some technological systems can perpetuate and even amplify existing biases, making transparency about algorithmic influence paramount. If AI is deployed with unacknowledged biases or limitations, patients could unknowingly receive suboptimal or inequitable care. The CHAI Coalition, a collective advocating for responsible AI in healthcare, emphasizes the need for clear communication regarding AI’s role. Their work suggests that merely stating “AI was used” is insufficient; patients need to understand how AI was used, its known limitations, and the human oversight involved. Dr. Eric Topol, a leading voice in digital medicine, frequently highlights the importance of “human-in-the-loop” AI, where clinicians retain ultimate decision-making authority, using AI as a sophisticated assistant rather than a replacement. This model implies that the clinician is still the primary source of information for informed consent, but it doesn’t fully resolve the patient’s right to know about the AI’s contribution. The challenge deepens when considering the probabilistic nature of many AI outputs. Unlike deterministic rules, AI often provides probabilities or likelihoods. Explaining these nuances to patients in an understandable way, without causing undue alarm or confusion, requires careful thought. Ziad Obermeyer, known for his research on algorithmic bias in healthcare, has shown how certain algorithms, despite appearing objective, can embed and exacerbate health disparities. This underscores why patients, particularly those from marginalized communities, have a heightened need for transparency about the tools influencing their care. Without this, the trust barrier between patients and the healthcare system could widen, undermining the very foundation of patient-centered care.

Navigating the Regulatory and Ethical Landscape

The regulatory environment for AI in healthcare continues to evolve rapidly, with significant legislative activity at the state level in 2026, often outpacing federal efforts. The FDA published a draft guidance on “Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations” on January 6, 2025, which provides recommendations for marketing submissions and lifecycle considerations for AI-enabled medical devices. While this draft guidance and the broader FDA SaMD Framework address safety and efficacy, they do not explicitly mandate patient notification about AI involvement in clinical decision-making. Similarly, the HIPAA Privacy Rule governs the protection of patient health information, but its scope doesn’t directly dictate the level of transparency required regarding AI’s role in generating clinical insights. The American Medical Association (AMA) has significantly advanced its stance on these ethical dilemmas, adopting new policies at its Annual Meeting in June 2026. These policies emphasize that AI should serve as an assistive tool, not an autonomous decision-maker, and stress transparency, accountability, and physician oversight. The AMA has also developed a comprehensive new policy addressing the development, deployment, and use of healthcare AI, with particular emphasis on oversight, transparency, generative AI, physician liability, data privacy, and payer use of AI. This reflects a growing recognition within the medical community that AI’s impact on clinical practice cannot be ignored. However, the definition of “appropriate” remains subjective and can vary widely among clinicians and institutions. Leading medical journals, such as the NEJM (New England Journal of Medicine), frequently feature articles debating the ethical implications of AI in medicine, including informed consent. These discussions often highlight the tension between the potential benefits of AI (e.g., improved efficiency, more accurate diagnoses) and the imperative to maintain patient trust and autonomy. The CHAI Coalition, through its advocacy, pushes for a more standardized approach, urging healthcare providers and AI developers to collaborate on best practices for disclosing AI’s role in patient care. This collaboration is essential to ensure that as AI becomes more pervasive, it does so in a manner that upholds ethical principles and strengthens the patient-provider relationship. CHAI Coalition guidelines on AI transparency

Building Trust Through Transparent AI Integration

Ultimately, the question of whether patients should be told when AI makes clinical recommendations boils down to a fundamental principle: trust. For AI health tools to be truly reliable and widely adopted, they must earn and maintain the trust of both clinicians and patients. This requires a commitment to transparency, not just in the technical workings of algorithms, but in how these tools are integrated into the clinical workflow and communicated to those receiving care. Positive signals of clinical accountability for AI health products include clear documentation of training data sources, published outcomes evidence demonstrating efficacy and safety across diverse populations, robust guardrail design to prevent unintended consequences, a transparent regulatory pathway, and a well-defined oversight model that ensures human accountability. When vendors can demonstrate these elements, it provides a strong foundation for clinicians to confidently discuss the role of AI with their patients. Framework for evaluating AI health product accountability The implication for patient safety advocates and clinicians is clear: we must actively champion policies and practices that prioritize transparent communication about AI in healthcare. This includes advocating for regulatory clarity, promoting educational initiatives for both patients and providers, and demanding that multiple AI health companies adhere to the highest standards of ethical deployment. Only by fostering an environment of open dialogue and accountability can we fully harness the transformative potential of AI while safeguarding patient autonomy and trust. The future of healthcare AI hinges not just on technological prowess, but on our collective commitment to responsible innovation. American Medical Association ethical guidelines on AI