Healthcare AI agents must maintain complete, automated, and auditable documentation of their entire lifecycle through standardized AI Model Passport implementation. This ensures full traceability of data lineage, model development, and decision processes to support regulatory compliance, clinical accountability, and patient safety requirements across all healthcare jurisdictions.
Establish comprehensive explainability frameworks ensuring clinicians, auditors, and patients can meaningfully interpret AI agent rationales and decision processes for enhanced clinical accountability and patient trust.
| # | Description | Level | Role |
|---|---|---|---|
| 2.1.1 | Verify that healthcare AI agents provide clinically relevant explanations at two distinct levels: detailed domain-specific rationales for healthcare professionals and simplified non-technical summaries for patients ensuring transparency and informed consent. | 1 | D/C |
| 2.1.2 | Verify that clinician-facing explanations include specific decision drivers such as highlighted features in medical imaging, evidence sources in diagnostic reasoning, and key clinical variables influencing AI agent recommendations. | 1 | D/C |
| 2.1.3 | Verify that patient-facing explanations provide accessible summaries of AI agent involvement in care decisions, maintaining appropriate health literacy levels and supporting patient autonomy in clinical decision-making. | 1 | C/H |
| 2.1.4 | Verify that explainability assessment includes standardized evaluation metrics such as clinician comprehension scores, contestability metrics, and patient comprehension scores to ensure explanations are auditable rather than subjective. | 2 | D/V |
| 2.1.5 | Verify that explainability frameworks include automation bias mitigation strategies enabling clinicians to meaningfully challenge and override AI recommendations when contradictory clinical evidence exists. | 2 | D/C |
| 2.1.6 | Verify that formal explainability assessment protocols include standardized testing for both clinician and patient comprehension, documentation of explanation generation methods, and verification that explanations accurately reflect underlying decision processes throughout the AI agent lifecycle. | 3 | D/V |
Implement comprehensive data provenance tracking with bias assessment and demographic diversity analysis.
| # | Description | Level | Role |
|---|---|---|---|
| 2.2.1 | Verify that healthcare AI agents maintain complete data lineage documentation including source identification, collection methodology, processing history, and healthcare data classification (PHI, anonymized, synthetic). | 1 | D/V |
| 2.2.2 | Verify that data lineage includes Health Canada SGBA+ (Sex and Gender-Based Analysis Plus) documentation demonstrating dataset representativeness across patient demographics, including age, sex, gender, ethnicity, socioeconomic status, and relevant health conditions. | 1 | D/V |
| 2.2.3 | Verify that all healthcare training datasets undergo bias assessment with documented mitigation strategies for identified demographic, clinical, or socioeconomic biases. | 1 | D/V |
| 2.2.4 | Verify that data provenance includes healthcare data governance compliance documentation (HIPAA, GDPR, institutional IRB approvals, patient consent frameworks). | 2 | D/V |
| 2.2.5 | Verify that synthetic healthcare data generation processes, if used, maintain statistical validity and clinical relevance while preserving patient privacy protections. | 2 | D/V |
| 2.2.6 | Verify that data lineage documentation is machine-interpretable and supports automated compliance verification across multiple regulatory frameworks. | 3 | D/V |
Document all aspects of model architecture, training processes, and version control for regulatory transparency.
| # | Description | Level | Role |
|---|---|---|---|
| 2.3.1 | Verify that AI Model Passport documents complete model architecture including algorithms used, hyperparameters, training methodologies, and validation approaches with versioning for each component. | 1 | D/V |
| 2.3.2 | Verify that model development process documentation includes clinical expert involvement, medical domain knowledge integration, and healthcare-specific validation protocols. | 1 | D/C |
| 2.3.3 | Verify that all model updates and modifications are tracked with version control, change rationale, validation results, and clinical impact assessment. | 2 | D/V |
| 2.3.4 | Verify that model training includes clinical performance metrics relevant to healthcare outcomes (sensitivity, specificity, positive/negative predictive values, clinical utility measures). | 2 | D/C |
| 2.3.5 | Verify that development documentation includes healthcare-specific testing protocols covering edge cases, rare conditions, and diverse patient populations. | 2 | D/C |
| 2.3.6 | Verify that advanced versioning includes automated impact analysis predicting clinical and regulatory implications of model modifications. | 3 | D/V |
Implement explainable AI capabilities that provide clinically meaningful insights into AI agent decision processes.
| # | Description | Level | Role |
|---|---|---|---|
| 2.4.1 | Verify that healthcare AI agents provide explainable decision outputs using clinically appropriate methods (SHAP, LIME, attention mechanisms) that healthcare professionals can interpret and validate. | 1 | D/C |
| 2.4.2 | Verify that decision explanations include confidence levels, uncertainty quantification, and identification of key clinical features driving AI agent recommendations. | 1 | D/C |
| 2.4.3 | Verify that XAI outputs are tailored to healthcare professional expertise levels and clinical workflow requirements, providing actionable insights for patient care decisions. | 2 | D/C |
| 2.4.4 | Verify that decision transparency includes identification of training data examples similar to current patient cases, supporting clinical reasoning validation. | 2 | D/C |
| 2.4.5 | Verify that explainability systems maintain audit trails linking patient-specific AI decisions to underlying model reasoning and data sources. | 2 | D/V |
| 2.4.6 | Verify that advanced XAI includes counterfactual explanations showing how different patient characteristics would change AI agent recommendations. | 3 | D/C |
Maintain comprehensive audit trails of AI agent decisions in healthcare contexts for clinical and regulatory accountability.
| # | Description | Level | Role |
|---|---|---|---|
| 2.5.1 | Verify that every healthcare AI agent decision maintains immutable audit trails including timestamp, patient context, input data, decision output, confidence level, and human oversight actions. | 1 | D/V |
| 2.5.2 | Verify that audit trails integrate with healthcare information systems (EHR, CPOE, clinical documentation) maintaining patient care continuity and clinical workflow efficiency. | 1 | H/D |
| 2.5.3 | Verify that clinical audit trails support retrospective analysis for quality improvement, clinical research, and regulatory compliance demonstration. | 2 | H/V |
| 2.5.4 | Verify that audit systems provide role-based access controls ensuring appropriate clinical stakeholders can review AI agent decisions relevant to their patient care responsibilities. | 2 | H/V |
| 2.5.5 | Verify that audit trail analysis includes automated detection of concerning patterns (bias emergence, performance degradation, unusual decision clustering) requiring clinical review. | 2 | D/V |
| 2.5.6 | Verify that comprehensive audit trails support clinical liability frameworks and malpractice defense documentation requirements. | 3 | H/V |
Ensure AI Model Passport documentation supports regulatory submissions and compliance verification across healthcare jurisdictions.
| # | Description | Level | Role |
|---|---|---|---|
| 2.6.1 | Verify that AI Model Passport generates automated compliance reports aligned with FDA 510(k), EU CE marking, Health Canada medical device licensing, and other relevant regulatory submission requirements. | 2 | V |
| 2.6.2 | Verify that compliance documentation includes clinical validation evidence, safety analysis, and effectiveness demonstration aligned with medical device regulatory pathways. | 2 | V |
| 2.6.3 | Verify that regulatory documentation supports post-market surveillance requirements including adverse event reporting, clinical performance monitoring, and safety update reporting. | 2 | H/V |
| 2.6.4 | Verify that compliance verification includes automated checking against regulatory requirement databases ensuring current regulatory alignment. | 3 | V |
| 2.6.5 | Verify that advanced compliance documentation supports regulatory pathway optimization, predicting optimal submission strategies across multiple jurisdictions. | 3 | V |
Implement standardized, machine-readable formats enabling automated compliance verification and regulatory reporting.
| # | Description | Level | Role |
|---|---|---|---|
| 2.7.1 | Verify that AI Model Passport documentation uses standardized machine-interpretable formats (JSON-LD, RDF, FHIR R4) enabling automated processing and compliance verification. | 2 | D/V |
| 2.7.2 | Verify that machine-readable documentation supports healthcare interoperability standards (HL7 FHIR, DICOM, IHE profiles) for seamless integration with healthcare IT infrastructure. | 2 | D/H |
| 2.7.3 | Verify that automated documentation systems maintain synchronization with operational AI agents, preventing documentation drift and ensuring real-time accuracy. | 2 | D/V |
| 2.7.4 | Verify that machine-interpretable formats support multi-stakeholder access including clinical teams, regulatory affairs, quality assurance, and external auditors with appropriate access controls. | 3 | D/V |
| 2.7.5 | Verify that advanced automation includes natural language generation creating human-readable summaries of complex technical documentation for clinical and regulatory stakeholders. | 3 | D/V |
Document AI agent supply chain dependencies including pre-trained models, healthcare datasets, and clinical knowledge integration.
| # | Description | Level | Role |
|---|---|---|---|
| 2.8.1 | Verify that AI Model Passport documents all supply chain dependencies including pre-trained models, healthcare datasets, clinical ontologies, and third-party AI services with version tracking and security assessment. | 2 | D/V |
| 2.8.2 | Verify that supply chain documentation includes security validation for all external dependencies, vulnerability assessment, and continuous monitoring for supply chain risks. | 2 | D/V |
| 2.8.3 | Verify that healthcare-specific supply chain components (clinical decision support tools, medical device integrations, healthcare data processors) undergo enhanced security and compliance verification. | 3 | D/V |
| 2.8.4 | Verify that supply chain transparency supports clinical liability frameworks ensuring clear accountability across all healthcare AI agent components and dependencies. | 3 | H/V |
Control Category C2 ensures complete traceability and transparency of healthcare AI agents through comprehensive AI Model Passport implementation, supporting clinical accountability, regulatory compliance, and patient safety across the entire AI agent lifecycle.
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. (https://www.nature.com/articles/s42256-019-0088-2)
Tonekaboni, S., Joshi, S., McCradden, M.D., & Goldenberg, A. (2019). What clinicians want from explainable AI: A review and conceptual framework. NPJ Digital Medicine, 2(1), 97. (https://proceedings.mlr.press/v106/tonekaboni19a.html)