A Design Framework for operationalizing Trustworthy Artificial Intelligence in Healthcare: Requirements, Tradeoffs and Challenges for its Clinical Adoption

AI in healthcare shows promise but faces adoption barriers beyond technical performance: While AI models can achieve expert-level performance in disease diagnosis and prognosis, widespread clinical adoption remains limited due to ethical concerns, regulatory barriers, and lack of trust—not just technical limitations.

Trustworthy AI requires balancing multiple principles across diverse stakeholders: The framework identifies six core TAI principles (human agency and oversight, algorithmic robustness, privacy and data governance, transparency, bias avoidance, and accountability) that must be implemented while serving clinicians, patients, providers, and regulators with different needs and concerns.

Healthcare complexity creates inherent tradeoffs in implementing trustworthy AI: The paper highlights that integrating TAI principles is complicated by the diversity of healthcare processes (screening, diagnosis, prognosis, treatment) and stakeholders, meaning developers often cannot optimize for all principles simultaneously and must make deliberate design choices.

A practical design framework bridges the gap between theory and implementation: Rather than abstract principles, the paper proposes disease-agnostic requirements that developers can actually use to embed trustworthy AI into medical systems, making TAI principles actionable across different healthcare contexts.

Stakeholder-specific requirements are essential for clinical adoption: The framework recognizes that different stakeholders have distinct needs, suggesting that successful medical AI systems must be designed with input from and accountability to clinicians, patients, healthcare providers, and regulators throughout development.