Cold Reflection Under the Carnival: Regulations, Ethics and Future Social Games of AI Healthcare

The FDA’s Adaptive Framework#

The FDA has developed a comprehensive AI/ML Software as Medical Device (SaMD) Action Plan that emphasizes the importance of mitigating bias in medical AI systems and ensuring continuous monitoring throughout the product lifecycle. This framework introduces several groundbreaking concepts:

Predetermined Change Control Plans: Manufacturers must specify in advance how their algorithms will change and what safeguards will prevent harmful modifications. This approach attempts to balance innovation with safety by allowing controlled evolution while maintaining regulatory oversight.

Real-World Performance Monitoring: Unlike traditional devices that undergo pre-market testing and then operate unchanged, AI medical systems require continuous surveillance. The FDA now mandates post-market studies to ensure algorithms maintain their safety and effectiveness as they encounter new patient populations and clinical scenarios.

Algorithm Bias Assessment: Recognizing that AI systems can perpetuate or amplify existing healthcare disparities, the FDA requires comprehensive bias testing across demographic groups before approval.

The European Union’s Comprehensive Approach#

The European Commission, FDA, Health Canada, and the World Health Organization have intensified their efforts to establish stricter frameworks for AI in healthcare, recognizing the critical need to uphold principles of fairness, equity, and explainability.

The EU’s General Data Protection Regulation (GDPR) provides a framework for health data protection, though it may leave loopholes in data sharing regulations that could be exploited. The EU AI Act, which came into effect in August 2024, represents the world’s first comprehensive AI legislation, with specific implications for healthcare:

High-Risk AI Classification: Medical AI systems are automatically classified as high-risk, requiring rigorous conformity assessments, transparency measures, and bias testing before deployment.

Algorithmic Transparency: Healthcare AI systems must provide clear explanations of their decision-making processes, addressing the “black box” problem that has long plagued machine learning applications.

Continuous Compliance: Like the FDA’s approach, the EU requires ongoing monitoring and reporting of AI system performance, with mandatory updates when bias or safety issues are detected.

The Challenge of Global Harmonization#

As AI healthcare companies operate across borders, the lack of harmonized international standards creates significant challenges. A system approved in one jurisdiction may face entirely different requirements elsewhere, potentially slowing innovation and creating regulatory arbitrage opportunities.

The Algorithmic Bias Crisis#

Bias in medical AI can lead to substandard clinical decisions and the perpetuation of longstanding healthcare disparities by influencing AI decisions in ways that disadvantage certain patient groups. The sources of this bias are multifaceted:

Historical Data Bias: Many populations—including vulnerable and historically underserved groups—remain underrepresented in the datasets used to train healthcare AI tools, ranging from gender, race, and ethnicity to socioeconomic status and sexual orientation.

Institutional Bias Amplification: Algorithmic bias in healthcare technology often reinforces longstanding institutional biases, as race, ethnicity, and socioeconomic status already impact health outcomes due to deeply ingrained systemic discrimination.

Clinical Practice Variations: Expertly annotated labels used to train supervised learning models may reflect implicit cognitive biases or substandard care practices, embedding these flaws into AI systems.

Real-World Consequences#

The impact of algorithmic bias extends far beyond statistical measures. Consider these documented examples:

• Pulse Oximetry Bias: Pulse oximeters systematically overestimate oxygen saturation levels in non-white patients, with Black patients being three times more likely to suffer from undetected hypoxemia compared to white patients.

• Diagnostic AI Disparities: Insufficient sample sizes for certain patient groups can result in suboptimal performance, algorithm underestimation, and clinically meaningless predictions for underrepresented populations.

• Risk Assessment Algorithms: Healthcare AI systems trained primarily on data from affluent populations may systematically underestimate disease risk in low-income patients, leading to delayed or inadequate care.

The Explainability Imperative#

Several powerful AI algorithms employ a “black box” approach, where it is difficult or impossible to understand how results are achieved. Explainable AI (XAI) includes interpretable models where the strengths and weaknesses of decision-making processes are transparent.

Regulators including the FDA have indicated that deterministic algorithms and explainable AI—including interpretability, trustability, and liability—are essential for fully vetting AI for clinical use.

The challenge lies in balancing model performance with interpretability. More complex AI models often achieve better diagnostic accuracy but at the cost of explainability. Healthcare requires a careful calibration: enough transparency to build trust and enable clinical reasoning, while maintaining the performance advantages that make AI valuable.

Responsibility Attribution in the AI Age#

When an AI-assisted medical decision leads to harm, who bears responsibility? This question has profound implications for medical practice, insurance, and legal frameworks:

• The Treating Physician: Should doctors be held liable for following AI recommendations, even if the algorithm’s reasoning is opaque?
• Healthcare Institutions: Do hospitals and clinics bear responsibility for the AI systems they choose to deploy?
• AI Developers: What liability do technology companies have for the real-world performance of their algorithms?
• Regulatory Bodies: How do approval agencies share responsibility for systems they’ve certified as safe and effective?

These questions remain largely unresolved, creating uncertainty that may slow AI adoption and leave patients without clear recourse when things go wrong.

The De-identification Illusion#

New algorithms have successfully re-identified supposedly anonymized patient health data, potentially increasing privacy risks.

Re-identification Risks: A 2018 study found that algorithms could re-identify 85.6% of adults and 69.8% of children in a physical activity cohort study, despite de-identification efforts.

Cross-Dataset Correlation: Modern AI systems can correlate health data with seemingly unrelated information sources—social media activity, purchasing patterns, location data—to infer sensitive health information about individuals who never consented to such analysis.

Regulatory Frameworks and Their Limitations#

Different jurisdictions take varying approaches to health data protection: Europe’s GDPR provides comprehensive data protection rules, while the United States relies on health-specific laws like HIPAA, potentially creating regulatory loopholes.

GDPR’s Comprehensive Approach: The General Data Protection Regulation promotes the creation of digital systems that respect user privacy, with strict consent requirements and data minimization principles.

HIPAA’s Sectoral Limitations: HIPAA compliance is required only for covered entities and their business associates, potentially leaving gaps when AI systems process health-related data from non-covered sources.

The Consent Conundrum#

Traditional informed consent models break down in the AI era. How can patients meaningfully consent to uses of their data that may not even be conceived at the time of collection? How do we balance individual privacy rights with the collective benefits of AI research that could save countless lives?

AI applications involving predictions based on behavioral and lifestyle patterns may have clinical, social, and occupational ramifications, as the probability of future health events could impact employment, insurance, and social relationships.

Patient Trust and Acceptance#

Research shows that individuals may not be ready to accept AI clinical applications due to risk beliefs, including privacy concerns, trust issues, communication barriers, concerns about regulatory transparency, and liability risks.

Demographic Variations: The interactions between types of healthcare service encounters and health conditions significantly influence individuals’ perceptions of privacy concerns, trust issues, communication barriers, and intention to use AI systems.

The Personal Connection Concern: 57% of Americans say AI use in diagnosis and treatment would make the patient-provider relationship worse, highlighting concerns about the human element in healthcare.

Healthcare Professional Adoption#

For health AI to work effectively, there must be trust from both doctors and patients, requiring the right regulatory environment to build that confidence.

Physician Priorities: Physicians’ priorities for digital health adoption are straightforward—they need to know: “Does it work?” This practical focus on efficacy over novelty shapes adoption patterns.

Trust Building Requirements: A 2025 AMA survey found that 68% of physicians saw value in AI tools and 66% were already using them, but 47% cited increased oversight from medical practitioners as the most important regulatory step to build trust.

Workflow Integration Challenges#

Trust in healthcare is shaped by interactions between key stakeholders, and its dynamics shift depending on relationships between patients and providers, providers and AI, and patients and health systems.

Implementation Friction: Trust friction emerges when AI systems do not align with real-world clinical needs, leading to situations where radiologists hesitate before accepting AI interpretations or nurses override AI-generated alerts.

Training and Support: Successful AI integration requires comprehensive training programs, ongoing technical support, and workflow redesign to accommodate new technologies without increasing physician burden.

Technical Solutions#

Bias Detection and Mitigation: Solutions to mitigate bias must include the collection of large and diverse datasets, statistical debiasing methods, thorough model evaluation, emphasis on model interpretability, and standardized bias reporting requirements.

Privacy-Preserving Technologies: Federated learning, differential privacy, and homomorphic encryption offer promising approaches to enable AI development while protecting individual privacy.

Explainable AI Development: Continued investment in XAI research is essential to create systems that can provide meaningful explanations for their decisions without sacrificing performance.

Regulatory Evolution#

Adaptive Frameworks: Regulatory bodies must develop more flexible, adaptive frameworks that can evolve with rapidly changing AI technologies while maintaining safety standards.

International Harmonization: Global cooperation is needed to develop consistent standards that facilitate innovation while ensuring patient safety across borders.

Multi-Stakeholder Governance: Governance infrastructure must be co-designed by vendors, healthcare institutions, and regulators to embed accountability and trust into daily workflows.

Social and Ethical Imperatives#

Inclusive Development: AI healthcare systems must be developed with diverse stakeholder input, including patients, healthcare workers, ethicists, and community representatives.

Equity Focus: Among Americans who see racial and ethnic bias as a problem in healthcare, 51% believe AI could help reduce bias and unfair treatment, suggesting potential for AI to address rather than exacerbate health disparities.

Public Education: Comprehensive public education programs are needed to build understanding and trust in AI healthcare technologies.