Optimizing AI Governance in Healthcare with IDMC

The rapid advancement of artificial intelligence in healthcare presents unprecedented opportunities to transform patient care, improve operational efficiency, and accelerate medical research. However, alongside these promising applications comes the critical need for robust governance frameworks that ensure AI technologies are deployed safely, ethically, and effectively. As healthcare organizations increasingly adopt AI-powered solutions, establishing comprehensive governance structures has become not just a best practice, but a fundamental requirement for maintaining patient safety, regulatory compliance, and organizational trust.

Healthcare AI governance encompasses the policies, processes, and systems that oversee the development, deployment, and ongoing management of artificial intelligence technologies within healthcare settings. This governance framework must address unique challenges inherent to healthcare environments, including stringent regulatory requirements, complex data privacy obligations, and the high-stakes nature of clinical decision-making where errors can have life-threatening consequences.

Understanding the Healthcare AI Governance Landscape

The Imperative for AI Governance in Healthcare

The healthcare industry generates an estimated 30% of the world's data volume, with this figure expected to grow exponentially as digital health technologies proliferate1. This massive data ecosystem provides the foundation for powerful AI applications, from diagnostic imaging and predictive analytics to personalized treatment recommendations and operational optimization. However, the complexity and sensitivity of healthcare data, combined with the potential for AI systems to directly impact patient outcomes, creates a unique governance challenge that extends far beyond traditional IT oversight.

Healthcare AI governance must address several critical dimensions simultaneously. First, it must ensure patient safety by establishing rigorous validation processes for AI models and implementing continuous monitoring systems to detect performance degradation or unexpected behaviors2. Second, it must maintain compliance with an intricate web of healthcare regulations, including HIPAA, GDPR, and emerging AI-specific legislation3. Third, it must address ethical considerations such as algorithmic bias, transparency, and fairness to ensure that AI systems do not exacerbate existing healthcare disparities4.

The stakes for effective governance are particularly high in healthcare. According to recent research, only 16% of hospitals have established system-wide AI governance policies, despite the rapid proliferation of AI tools across clinical and administrative functions2. This governance gap creates significant risks, including potential patient harm from biased or inaccurate AI outputs, regulatory violations that can result in substantial financial penalties, and erosion of public trust in healthcare institutions5.

Key Challenges in Healthcare AI Implementation

Healthcare organizations face numerous barriers when implementing AI governance frameworks. Data quality and accessibility represent fundamental challenges, as healthcare data is often fragmented across disparate systems, inconsistently formatted, and variable in quality6. These data quality issues can significantly impact AI model performance and reliability, leading to inaccurate predictions or biased outcomes that could compromise patient care.

The complexity of healthcare workflows presents another significant governance challenge7. Unlike other industries where AI applications may operate in relatively controlled environments, healthcare AI must integrate seamlessly with existing clinical workflows while maintaining the flexibility to adapt to diverse patient populations and care scenarios. This integration complexity is compounded by the need to ensure that AI systems enhance rather than disrupt the physician-patient relationship and clinical decision-making processes.

Regulatory compliance adds another layer of complexity to healthcare AI governance. The healthcare industry operates under some of the most stringent regulatory frameworks globally, and AI applications must navigate these requirements while maintaining their effectiveness and utility5. The evolving nature of AI-specific regulations, such as the EU AI Act and emerging FDA guidelines for AI-enabled medical devices, requires governance frameworks that can adapt quickly to changing compliance requirements.

The Role of IDMC in Healthcare AI Governance

Introduction to Informatica's Intelligent Data Management Cloud

Informatica's Intelligent Data Management Cloud (IDMC) represents a comprehensive platform specifically designed to address the complex data management and governance challenges facing modern healthcare organizations. As the industry's first cloud-neutral, AI-powered data management platform, IDMC provides healthcare organizations with the tools and capabilities necessary to establish robust AI governance frameworks while maintaining the flexibility and scalability required for modern healthcare operations8.

IDMC for Healthcare is built on a foundation of advanced AI capabilities, powered by Informatica's CLAIRE AI engine, which provides intelligent automation for data discovery, classification, and governance processes9. This AI-powered approach enables healthcare organizations to achieve unprecedented visibility into their data assets while automating many of the manual processes traditionally associated with data governance, thereby reducing both the complexity and cost of maintaining comprehensive oversight.

The platform's cloud-native architecture supports hybrid and multi-cloud deployments, addressing the diverse technology environments typical of healthcare organizations10. This flexibility is particularly important for healthcare institutions that must integrate data from multiple sources, including electronic health records, medical devices, research databases, and third-party applications, while maintaining strict security and compliance standards.

Core IDMC Capabilities for AI Governance

IDMC provides several key capabilities that directly support effective AI governance in healthcare settings. The platform's comprehensive data cataloging and discovery capabilities enable organizations to maintain complete visibility into their data assets, including understanding data lineage, identifying sensitive information, and tracking data usage across AI applications11. This visibility is essential for ensuring that AI models are trained on appropriate, high-quality data and that sensitive patient information is properly protected throughout the AI lifecycle.

The platform's advanced data quality management features help ensure that AI systems receive reliable, consistent input data12. IDMC can automatically profile data sources to identify quality issues, apply standardization rules to improve consistency, and monitor data quality metrics in real-time. These capabilities are particularly critical for healthcare AI applications, where poor data quality can directly impact patient safety and care outcomes.

IDMC's integrated privacy and security features provide comprehensive protection for sensitive healthcare data13. The platform includes advanced data masking and encryption capabilities, fine-grained access controls, and comprehensive audit trails that support compliance with healthcare privacy regulations. These security features are designed to work seamlessly with AI workflows, ensuring that privacy protection does not impede the development and deployment of beneficial AI applications.

IDMC's AI-Powered Governance Features

One of IDMC's most significant advantages for healthcare AI governance is its use of artificial intelligence to automate and enhance governance processes themselves. The CLAIRE AI engine can automatically classify data based on sensitivity levels, identify potential compliance risks, and recommend appropriate governance policies14. This AI-powered approach to governance enables healthcare organizations to scale their oversight capabilities without proportionally increasing administrative overhead.

The platform's automated data lineage tracking provides comprehensive visibility into how data flows through AI systems, from initial collection through model training and deployment to final output generation13. This end-to-end lineage tracking is essential for healthcare organizations that must demonstrate compliance with regulations requiring transparency in AI decision-making processes and the ability to trace decisions back to their source data.

IDMC also provides sophisticated bias detection and mitigation capabilities specifically designed for AI applications13.The platform can analyze training datasets to identify potential sources of bias related to demographic factors, geographic regions, or other characteristics that could lead to unfair treatment of patient populations. This capability is particularly important in healthcare, where biased AI systems could exacerbate existing health disparities and undermine efforts to provide equitable care.

Building an Effective AI Governance Framework with IDMC

Establishing Governance Foundations

The implementation of effective AI governance in healthcare begins with establishing clear organizational foundations that define roles, responsibilities, and decision-making processes. Healthcare organizations should form dedicated AI governance committees that include representatives from clinical departments, IT, legal, compliance, and patient advocacy groups15. These multidisciplinary committees ensure that AI governance decisions consider all relevant perspectives and stakeholder interests.

IDMC supports these organizational foundations by providing a centralized platform for governance activities. The platform's role-based access controls enable organizations to implement governance workflows that route decisions to appropriate stakeholders while maintaining audit trails of all governance activities11. This systematic approach to governance helps ensure consistency in decision-making and provides the documentation necessary for regulatory compliance and internal auditing.

The establishment of clear governance policies and procedures is essential for effective AI oversight. These policies should address key areas including data usage standards, model validation requirements, performance monitoring protocols, and incident response procedures4. IDMC's policy management capabilities enable organizations to codify these governance requirements into automated workflows that can be consistently applied across all AI initiatives.

Data Governance as the Foundation for AI Success

Effective AI governance in healthcare must begin with robust data governance practices, as the quality and integrity of training data directly impact the safety and effectiveness of AI systems. IDMC provides comprehensive data governance capabilities specifically designed to support AI applications in healthcare environments16. These capabilities include automated data discovery and classification, which enables organizations to maintain complete inventories of their data assets and understand the sensitivity and regulatory requirements associated with different types of healthcare information.

The platform's data quality management features are particularly critical for healthcare AI applications. IDMC can automatically profile data sources to identify quality issues such as missing values, inconsistent formatting, or outliers that could compromise AI model performance12. The platform provides both automated and configurable data quality rules that can be applied consistently across all data sources feeding into AI systems, ensuring that models receive reliable, high-quality input data.

Data lineage tracking capabilities within IDMC provide comprehensive visibility into how data flows through AI systems, from initial collection through processing, model training, and final output generation. This end-to-end lineage tracking is essential for healthcare organizations that must demonstrate compliance with regulations requiring transparency in AI decision-making processes and the ability to trace decisions back to their source data11.

Implementing Risk-Based AI Oversight

Healthcare AI governance must adopt a risk-based approach that prioritizes oversight activities based on the potential impact of AI systems on patient safety and care outcomes17. IDMC supports this risk-based approach by providing automated risk assessment capabilities that evaluate AI applications based on factors such as the sensitivity of input data, the criticality of decisions being made, and the potential for patient harm if the system fails or produces incorrect outputs.

The platform's continuous monitoring capabilities enable healthcare organizations to implement real-time oversight of AI systems in production. IDMC can monitor key performance indicators, detect anomalies in AI system behavior, and alert governance teams to potential issues before they impact patient care13. This proactive monitoring approach is essential for maintaining the safety and reliability of healthcare AI applications over time.

Risk mitigation strategies must be tailored to the specific characteristics of different AI applications. For high-risk clinical decision support systems, IDMC can implement enhanced validation procedures, require human oversight of AI recommendations, and maintain detailed audit trails of all decisions18. For lower-risk administrative applications, governance procedures can be streamlined while still maintaining appropriate oversight and compliance monitoring.

Privacy and Security in Healthcare AI Governance

Protecting Patient Data in AI Applications

Healthcare AI systems typically require access to vast amounts of sensitive patient information, creating significant privacy and security challenges that must be addressed through comprehensive governance frameworks. IDMC provides advanced privacy protection capabilities specifically designed for healthcare AI applications12. These capabilities include sophisticated data masking and anonymization techniques that can protect patient privacy while preserving the statistical properties of data necessary for effective AI model training.

The platform's data classification capabilities automatically identify sensitive information such as personally identifiable information (PII), protected health information (PHI), and other regulated data types. This automated classification enables healthcare organizations to apply appropriate privacy protections consistently across all data sources and AI applications13. The classification process is powered by machine learning algorithms that can identify sensitive information even in unstructured data sources such as clinical notes and research documents.

IDMC's privacy-preserving analytics capabilities enable healthcare organizations to derive insights from sensitive data without exposing individual patient information. The platform supports techniques such as differential privacy, federated learning, and synthetic data generation that can provide the statistical insights necessary for AI development while maintaining strong privacy protections19. These advanced privacy techniques are particularly important for healthcare research applications where insights must be derived from patient data without compromising individual privacy.

Ensuring Compliance with Healthcare Regulations

Healthcare AI governance must address a complex web of regulatory requirements, including HIPAA in the United States, GDPR in Europe, and emerging AI-specific regulations such as the EU AI Act3. IDMC provides comprehensive compliance management capabilities that help healthcare organizations navigate these regulatory requirements while maintaining the flexibility necessary for AI innovation.

The platform's automated compliance monitoring capabilities can continuously assess AI systems against relevant regulatory requirements and alert governance teams to potential compliance risks13. This proactive approach to compliance management helps healthcare organizations avoid costly violations and maintain the trust of patients and regulatory authorities.

IDMC supports the documentation and audit trail requirements common to healthcare regulations by maintaining comprehensive records of all data processing activities, AI model decisions, and governance actions11. These detailed audit trails provide the evidence necessary to demonstrate compliance during regulatory inspections and support incident investigations when issues arise.

Ethical AI and Bias Mitigation

Addressing Algorithmic Bias in Healthcare AI

Algorithmic bias represents one of the most significant ethical challenges facing healthcare AI applications, with the potential to exacerbate existing health disparities and undermine trust in AI-powered healthcare systems4. IDMC provides comprehensive bias detection and mitigation capabilities specifically designed to address these challenges in healthcare environments.

The platform's bias detection algorithms can analyze training datasets to identify potential sources of bias related to demographic characteristics, geographic factors, socioeconomic status, and other variables that could lead to unfair treatment of patient populations13. This analysis helps healthcare organizations understand the potential for bias in their AI systems before deployment, enabling proactive mitigation strategies rather than reactive corrections after problems emerge.

IDMC supports several bias mitigation techniques, including data augmentation to address underrepresented populations, algorithmic debiasing methods that adjust model outputs to ensure fairness across different groups, and ongoing monitoring of AI system performance across different patient demographics14. These capabilities enable healthcare organizations to develop AI systems that provide equitable care to all patient populations while maintaining high levels of clinical effectiveness.

Promoting Transparency and Explainability

Transparency and explainability are essential components of ethical AI in healthcare, enabling clinicians and patients to understand and trust AI-powered recommendations and decisions4. IDMC provides several capabilities that support transparency and explainability in healthcare AI applications.

The platform's comprehensive data lineage tracking provides visibility into the data sources and processing steps that contribute to AI model outputs, enabling stakeholders to understand the basis for AI recommendations11. This transparency is particularly important in clinical settings where physicians need to understand the reasoning behind AI recommendations to make informed decisions about patient care.

IDMC's model documentation and versioning capabilities help healthcare organizations maintain detailed records of AI model development, validation, and deployment processes13. This documentation supports regulatory requirements for transparency and provides the information necessary for clinical staff to understand the capabilities and limitations of AI systems in their work environment.

Quality Assurance and Continuous Monitoring

Implementing Robust Validation Processes

Healthcare AI systems require rigorous validation processes to ensure they meet the high standards of safety and effectiveness required for clinical applications20. IDMC provides comprehensive validation capabilities that support both initial model validation and ongoing performance monitoring throughout the AI system lifecycle.

The platform's automated validation workflows can assess AI models against predefined performance metrics, clinical guidelines, and regulatory requirements18. These workflows can be customized to address the specific requirements of different types of healthcare AI applications, from diagnostic imaging systems that must demonstrate high sensitivity and specificity to population health models that require fairness across diverse demographic groups.

IDMC supports both technical validation, which focuses on model performance metrics and statistical reliability, and clinical validation, which assesses the real-world effectiveness of AI systems in healthcare settings13. This comprehensive approach to validation helps ensure that AI systems not only perform well in controlled testing environments but also provide meaningful benefits in actual clinical practice.

Continuous Performance Monitoring

The dynamic nature of healthcare environments requires continuous monitoring of AI system performance to detect changes in effectiveness, identify emerging bias issues, and ensure ongoing compliance with regulatory requirements17.IDMC provides sophisticated monitoring capabilities that can track AI system performance in real-time and alert governance teams to potential issues before they impact patient care.

The platform's performance monitoring capabilities can track a wide range of metrics, including prediction accuracy, processing speed, data quality indicators, and fairness measures across different patient populations13. This comprehensive monitoring approach enables healthcare organizations to maintain high standards of AI system performance while quickly identifying and addressing issues that may arise over time.

IDMC's anomaly detection capabilities can identify unusual patterns in AI system behavior that may indicate technical problems, data quality issues, or emerging bias concerns18. These capabilities are particularly important for healthcare AI applications, where subtle changes in system behavior could have significant implications for patient safety and care quality.

Integration with Healthcare Systems and Workflows

Seamless EHR and Clinical System Integration

The success of healthcare AI governance depends heavily on the ability to integrate AI systems seamlessly with existing healthcare technology infrastructure, particularly electronic health record (EHR) systems and other clinical applications16. IDMC provides extensive integration capabilities specifically designed to support healthcare environments, including pre-built connectors for major EHR systems, medical devices, and healthcare applications.

The platform's HL7 and FHIR support enables seamless data exchange with healthcare systems using industry-standard interoperability protocols12. This standards-based approach to integration ensures that AI systems can access the clinical data necessary for effective operation while maintaining compliance with healthcare interoperability requirements and data exchange standards.

IDMC's real-time data processing capabilities enable AI systems to access and analyze current patient information as it becomes available in clinical systems10. This real-time capability is essential for AI applications such as early warning systems that must respond quickly to changes in patient condition or clinical decision support tools that provide recommendations during patient encounters.

Supporting Clinical Decision-Making Workflows

Healthcare AI governance must ensure that AI systems enhance rather than disrupt clinical decision-making processes21.IDMC supports this objective by providing flexible deployment options that can be tailored to different clinical workflows and decision-making environments.

The platform's API-based architecture enables AI systems to integrate seamlessly with clinical workflows, providing recommendations and insights at appropriate decision points without requiring clinicians to leave their familiar work environments9. This seamless integration approach helps ensure that AI systems provide value to clinical staff while maintaining the efficiency and effectiveness of existing care processes.

IDMC's audit and documentation capabilities provide comprehensive records of AI system interactions within clinical workflows11. This documentation supports quality improvement initiatives, regulatory compliance requirements, and clinical research activities that depend on understanding how AI systems impact care delivery and patient outcomes.

Regulatory Compliance and Risk Management

Navigating Complex Healthcare Regulations

Healthcare AI systems must comply with a complex and evolving regulatory landscape that includes traditional healthcare regulations such as HIPAA and emerging AI-specific requirements3. IDMC provides comprehensive compliance management capabilities that help healthcare organizations navigate these regulatory challenges while maintaining the agility necessary for AI innovation.

The platform's automated compliance assessment capabilities can evaluate AI systems against relevant regulatory requirements and provide detailed reports on compliance status13. These assessments can be performed continuously or on-demand, enabling healthcare organizations to maintain ongoing compliance while quickly identifying and addressing potential regulatory risks.

IDMC supports the documentation and audit trail requirements common to healthcare regulations by maintaining comprehensive records of all AI system activities, including data access, model decisions, and governance actions11.These detailed records provide the evidence necessary to demonstrate compliance during regulatory inspections and support incident investigations when issues arise.

Managing AI-Related Risks

Healthcare AI systems introduce new categories of risk that must be carefully managed through comprehensive risk management frameworks22. IDMC provides several capabilities that support effective risk management for healthcare AI applications, including automated risk assessment, continuous monitoring, and incident response capabilities.

The platform's risk assessment capabilities can evaluate AI systems based on factors such as the criticality of decisions being made, the sensitivity of data being processed, and the potential for patient harm if the system fails or produces incorrect outputs18. This risk-based approach enables healthcare organizations to prioritize their governance activities and allocate resources appropriately across different AI applications.

IDMC's incident response capabilities provide structured workflows for investigating and responding to AI system issues, including automated notification systems, evidence collection tools, and corrective action tracking13. These capabilities help healthcare organizations respond quickly and effectively to AI-related incidents while maintaining comprehensive documentation for regulatory reporting and quality improvement purposes.

Implementation Strategies and Best Practices

Phased Implementation Approach

Healthcare organizations should adopt a phased approach to implementing AI governance frameworks using IDMC, beginning with foundational data governance capabilities and gradually expanding to more sophisticated AI-specific oversight functions23. This phased approach enables organizations to build governance capabilities incrementally while learning from early experiences and adapting their approaches based on practical insights.

The initial phase of implementation should focus on establishing comprehensive data discovery and cataloging capabilities using IDMC's automated scanning and classification features11. This foundational phase helps organizations understand their data landscape, identify sensitive information, and establish the data governance practices necessary to support AI applications.

Subsequent phases can add AI-specific governance capabilities such as bias detection, model validation, and performance monitoring13. This gradual expansion approach enables healthcare organizations to develop the expertise and processes necessary for sophisticated AI governance while maintaining operational continuity and minimizing disruption to existing systems and workflows.

Building Organizational Capabilities

Effective AI governance requires healthcare organizations to develop new organizational capabilities and expertise that combine traditional healthcare knowledge with advanced data science and AI skills24. IDMC supports this capability development by providing user-friendly interfaces and automated workflows that enable healthcare professionals to participate in governance activities without requiring deep technical expertise.

The platform's collaborative features enable multidisciplinary teams to work together effectively on AI governance activities, with role-based access controls ensuring that each team member can contribute their expertise while maintaining appropriate security and privacy protections11. This collaborative approach is essential for healthcare AI governance, which requires input from clinical, technical, legal, and ethical perspectives.

IDMC's comprehensive documentation and reporting capabilities support knowledge management and organizational learning by capturing best practices, lessons learned, and governance decisions in accessible formats13. This knowledge management approach enables healthcare organizations to build institutional expertise in AI governance over time and ensure continuity as staff members change roles or leave the organization.

Measuring Success and Continuous Improvement

Healthcare organizations must establish clear metrics and measurement frameworks to assess the effectiveness of their AI governance programs and identify opportunities for continuous improvement4. IDMC provides comprehensive analytics and reporting capabilities that support these measurement activities by tracking key governance metrics and providing detailed insights into AI system performance and compliance status.

Key metrics for healthcare AI governance include measures of data quality, model performance, bias detection and mitigation, compliance status, and incident response effectiveness13. IDMC can automatically collect and analyze these metrics, providing dashboard views and detailed reports that enable governance teams to assess their programs' effectiveness and identify areas for improvement.

The platform's trend analysis capabilities enable healthcare organizations to track their governance program maturity over time and benchmark their performance against industry standards and best practices18. This analytical approach supports continuous improvement efforts and helps organizations demonstrate the value of their AI governance investments to executive leadership and board oversight committees.

Future Considerations and Emerging Trends

Evolving Regulatory Landscape

The regulatory landscape for healthcare AI continues to evolve rapidly, with new requirements emerging at both national and international levels25. Healthcare organizations must ensure that their AI governance frameworks can adapt quickly to these changing requirements while maintaining operational effectiveness and efficiency.

IDMC's flexible architecture and automated compliance capabilities position healthcare organizations to respond effectively to regulatory changes13. The platform's ability to automatically assess AI systems against new regulatory requirements and update compliance workflows helps organizations maintain compliance while minimizing the administrative burden associated with regulatory changes.

Emerging regulations such as the EU AI Act introduce new categories of requirements for high-risk AI applications, including enhanced transparency, documentation, and oversight obligations3. IDMC's comprehensive governance capabilities are designed to support these enhanced requirements while maintaining the usability and efficiency necessary for practical healthcare operations.

Integration of Advanced AI Technologies

Healthcare organizations are increasingly exploring advanced AI technologies such as large language models, generative AI, and foundation models that introduce new governance challenges and opportunities7. These advanced technologies require enhanced governance frameworks that can address their unique characteristics and risk profiles while enabling healthcare organizations to realize their potential benefits.

IDMC's AI-powered governance capabilities are designed to evolve with advancing AI technologies, providing the flexibility and scalability necessary to govern increasingly sophisticated AI applications18. The platform's metadata-driven approach to governance enables it to adapt to new AI technologies without requiring fundamental architectural changes or disruptions to existing governance processes.

The integration of advanced AI technologies also creates opportunities for more sophisticated governance approaches, including AI-powered governance systems that can automatically monitor and optimize AI system performance14.IDMC's CLAIRE AI engine represents an early example of this approach, using artificial intelligence to enhance and automate governance processes themselves.

Promoting Healthcare AI Innovation

Effective AI governance should enable rather than inhibit healthcare AI innovation by providing clear guidelines, reducing compliance uncertainty, and creating trusted environments for AI development and deployment26. IDMC supports this innovation-enabling approach by providing self-service capabilities that enable healthcare professionals to access and analyze data safely while maintaining appropriate governance oversight.

The platform's data marketplace capabilities enable healthcare organizations to share data and insights across departments and with external research partners while maintaining strict privacy and security controls11. This data sharing capability is essential for advancing healthcare AI research and development while maintaining patient privacy and regulatory compliance.

IDMC's support for emerging AI technologies and methodologies ensures that healthcare organizations can adopt new innovations quickly while maintaining robust governance frameworks18. This balance between innovation and governance is essential for healthcare organizations that must remain competitive while maintaining the highest standards of patient safety and care quality.

Healthcare AI governance represents a critical capability for organizations seeking to harness the transformative potential of artificial intelligence while maintaining the safety, privacy, and ethical standards essential to healthcare delivery. Informatica's Intelligent Data Management Cloud provides a comprehensive platform for implementing effective AI governance frameworks that address the unique challenges and requirements of healthcare environments.

Through its advanced AI-powered capabilities, comprehensive compliance support, and seamless integration with healthcare systems, IDMC enables healthcare organizations to establish governance frameworks that promote innovation while ensuring patient safety and regulatory compliance. The platform's flexible architecture and continuous evolution capabilities position healthcare organizations to adapt their governance approaches as AI technologies and regulatory requirements continue to evolve.

The successful implementation of healthcare AI governance using IDMC requires a thoughtful, phased approach that builds organizational capabilities while establishing robust oversight processes. Healthcare organizations that invest in comprehensive AI governance frameworks today will be best positioned to realize the full benefits of AI technologies while maintaining the trust and confidence of patients, clinicians, and regulatory authorities in an increasingly AI-powered healthcare future.