How to Ensure Responsible AI Use in Healthcare

In our last blog post, we explored how healthcare AI has evolved over the years and discussed why the industry is on the precipice of an AI revolution. There is naturally a lot of excitement about AI’s potential to completely reshape healthcare delivery and administration. However, implementing healthcare AI also carries a variety of unique risks that can result in patient harm, regulatory violations, and significant financial consequences.

Understanding these risks is essential for balancing AI innovation with the development of appropriate safeguards that protect patients, organizations, and stakeholders. In this article, we’ll discuss the most significant risks to data privacy, systems security, and systems control, and how healthcare organizations can effectively address them.

Protecting Patient Data and Organizational Assets

The Risk: Patient Data Exposure and Competitive Intelligence Leaks

When AI systems operate outside an organization’s control, patient health information (PHI) can be inadvertently exposed to external systems, enabling use for training future AI models or access by unauthorized parties.

For example, if an AI system processes patient records through external APIs, that data could be logged, cached, or used to improve the vendor’s models, exposing your patients’ most sensitive information and potentially allowing its unintentional recall from the next version of that foundational model. This creates HIPAA violations, patient privacy breaches, and potential competitive intelligence leaks.

The Solution: Secure Infrastructure Deployment

Deploying AI systems within secure virtual private clouds (VPCs) with healthcare-grade encryption and access controls can mitigate these risks. Leading implementations use cloud services like AWS Bedrock with AI models operating entirely within organizational VPCs. This infrastructure creates a protective barrier around patient data and ensures data is not exposed to external model training or third-party systems while still enabling AI capabilities.

The Risk: AI-Enabled Privilege Escalation and Unauthorized Access

AI agents can potentially be exploited to access data or systems beyond what the requesting user should access. Malicious actors could use AI systems as backdoors to sensitive information, or well-intentioned users could inadvertently access protected data through AI interactions. Without proper controls, an administrative staff member might use an AI system to access sensitive information, such as clinical data or patient records.

The Solution: Role-Based Access Control for Agents

Healthcare organizations can avoid unauthorized access by implementing granular permissions that mirror existing roles. With these permissions, AI agents will inherit the access privileges of the requesting user, preventing privilege escalation or unauthorized data access. This architecture prevents both accidental exposure and malicious exploitation of AI systems while maintaining audit accountability.

The Risk: Inability to Trace AI Decisions and Actions

Without comprehensive logging, organizations cannot understand what AI systems did, why they made specific recommendations, or how they accessed data. This creates multiple problems, including:

• No way to debug issues when AI systems make errors
• Limited ability to defend AI-assisted decisions in malpractice cases or investigations
• A lack of capability to identify patterns of bias or systematic errors
• Failure to meet regulatory requirements for audit trails

The Solution: Comprehensive Audit Logging

Maintaining complete, immutable logs of all AI interactions, decisions, and data access patterns is crucial to minimizing this risk. These logs are essential for both regulatory compliance and continuous improvement. With comprehensive audit logging, healthcare organizations can analyze AI decision patterns, identify potential issues proactively, and demonstrate compliance during audits or investigations.

Accountability and Professional Responsibility

The Risk: AI Making Incorrect Decisions Due to Hallucinations

For all their strengths, AI systems lack clinical judgment, contextual understanding, and professional accountability required for patient care decisions. They might not have the data to consider factors like patient preferences, family dynamics, or complex clinical scenarios that require human empathy and reasoning—and without that information, hallucinations are possible.

When AI systems make clinical recommendations without human oversight, the result can be inappropriate care plans, missed critical diagnoses, or recommendations that conflict with patient values and preferences. Furthermore, AI decisions in patient care rightfully garner intense regulatory and compliance scrutiny.

The Solution: Human Oversight for Patient Care Decisions

Any AI recommendation affecting direct patient care must include human clinician review and approval. This includes care management protocols, treatment recommendations, clinical alerts, and diagnostic suggestions. In matters of patient care, AI must be used as a decision support tool, which enables organizations to maintain clinical accountability and adhere to their professional and ethical responsibilities.

The Risk: Regulatory Non-Compliance and Legal Liability

Regulatory submissions require legal accountability that AI systems cannot provide. AI-generated regulatory reports might contain errors, omissions, or interpretations that violate regulatory requirements. When submitted without human review, these errors can result in regulatory penalties, audit failures, and legal liability for the organization and its leaders.

The Solution: Human Review of Regulatory Submissions

All compliance reporting, regulatory filings, and audit responses must include human review and approval, with AI serving in a supportive rather than autonomous role. This ensures accuracy, completeness, and professional accountability for regulatory interactions.

Preventing Discrimination and Health Inequities

The Risk: Perpetuating and Amplifying Healthcare Disparities

AI systems learn from historical healthcare data; unfortunately, this data often reflects systemic biases that have historically led to disparities in care delivery. As an example, a recent report from Blue Cross Blue Shield found that in majority Black and Hispanic communities, major depressive disorders are respectively 31% and 39% less likely to be diagnosed and treated than in majority white communities.

Without proper monitoring, AI systems can prolong discrimination against protected classes, exacerbate existing health inequities, and create new forms of algorithmic bias. An AI system trained on historical data might systematically recommend less aggressive treatment—or none at all—for certain demographic groups, thereby perpetuating disparities rather than supporting optimal care protocols.

The Solution: Comprehensive Bias Monitoring

To mitigate the impact of implicit bias in historical healthcare data, organizations must continuously assess AI outputs across demographic groups, geographic regions, and socioeconomic segments. Implementing statistical tests can help detect disparate impact and performance variations. In addition, organizations should perform regular analysis of AI outputs to determine whether AI recommendations vary inappropriately across protected classes or vulnerable populations.

The Risk: Misaligned Clinical Recommendations

AI systems recognize and respond to patterns in data, and in rare cases, they may develop recommendations based on these patterns that don’t align with current evidence-based medicine or clinical guidelines. This can result in suboptimal care recommendations, inappropriate resource utilization, or treatment protocols that run counter to professional standards.

The Solution: Ongoing Clinical Validation

To ensure alignment with evidence-based practices and identify any potential clinical biases, clinical teams should be engaged in regular reviews of AI recommendations. This includes analyzing whether AI systems are appropriately accounting for clinical complexity and patient-specific factors. The goal is to ensure the AI system’s decisions are rooted in clinically-validated data and rules.

Ensuring Coordinated Oversight and Continuous Improvement

The Risk: Uncoordinated AI Implementation and Governance Gaps

Without proper organizational structure, AI implementations can proceed without appropriate oversight, creating inconsistent standards, conflicting policies, and gaps in accountability. This can result in compliance failures, increased liability, and suboptimal return on your AI investments.

The Solution: Structured Governance Framework

The AI system needs an established and clearly-defined infrastructure in which to operate—and your organization does too. Establish multidisciplinary AI oversight committees, develop comprehensive policies for AI use, and implement ongoing staff training programs. This ensures coordinated implementation, consistent standards, and continuous improvement in AI capabilities and governance.

Risk Mitigation: A Key Part of AI Enablement

Risk mitigation will become increasingly important as AI systems continue to evolve and take a more prominent role in healthcare operations, particularly where agentic AI is concerned. However, the safeguards discussed above should not be viewed as barriers to AI adoption; rather, they are enablers. Implementing appropriate guardrails allows healthcare organizations to harness AI capabilities while protecting patients, maintaining compliance, and preserving the trust that is essential to effective care delivery.