10 Questions to Ask AI Vendors Before Audits
Post Summary
When evaluating AI vendors in healthcare, asking the right questions can safeguard patient data, ensure compliance, and avoid costly mistakes. With regulatory bodies like the FDA and EMA introducing stricter guidelines, such as the "Guiding Principles of Good AI Practice" in January 2026, healthcare organizations must be prepared. Here’s a quick breakdown of the ten critical areas to address when selecting and managing AI vendors:
- Performance Guarantees: Does the vendor offer measurable clinical performance thresholds and remedies for unmet benchmarks?
- Data Protection: How is Protected Health Information (PHI) secured and used? Are Business Associate Agreements (BAAs) in place?
- Liability Terms: What indemnification and liability caps exist to cover harm caused by AI errors?
- Long-Term Governance: How does the vendor monitor AI performance over time and address model drift?
- Regulatory Compliance: Are systems aligned with evolving regulations like HIPAA, FDA guidelines, and the EU AI Act?
- AI-Specific Security: What safeguards are in place to prevent adversarial attacks and data breaches?
- Explainability: Does the system provide transparent decision-making and audit trails?
- Risk Assessment Tools: How does the vendor speed up risk evaluations and manage third-party risks?
- Human Oversight: What processes ensure human review and intervention for AI decisions?
- Collaboration Support: Does the vendor enable benchmarking and shared risk management across the healthcare ecosystem?
These questions help mitigate risks, protect patient safety, and ensure compliance with evolving legal standards. Don’t rely solely on vendor promises - demand evidence like model cards, bias audits, and SOC 2 reports to make informed decisions.
10 Critical Questions to Ask AI Vendors Before Healthcare Audits
How do you audit software vendors?
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1. What Warranties and Performance Guarantees Do You Provide for AI Outputs?
AI vendors often present technical performance metrics that can obscure actual shortcomings in practice. Take the Epic sepsis model, for example - it achieved a technical AUC of 0.76–0.83 in internal tests [4]. But when validated externally, it showed an 88% false positive rate and identified only 7% of sepsis cases before clinicians did [6]. This gap between lab results and real-world performance highlights why healthcare organizations need warranties that go beyond vendor-provided data.
Contracts should establish measurable clinical performance thresholds, validated by at least three independent sites and supported by peer-reviewed studies. These benchmarks should focus on clinical outcomes like mortality, length of stay, and readmissions, rather than relying solely on AUC metrics [4]. It's equally important to address fairness. For instance, research on the OPTUM care management algorithm revealed that 46.5% more Black patients should have been enrolled to ensure equitable care coordination [4]. Setting such standards is essential before considering corrective measures.
"AUC is a technical metric. Has deployment demonstrably improved patient outcomes?" - Physician AI Handbook [6]
Warranties should include clear remedies if these thresholds aren't met. Options like service credits, penalty-free contract terminations, or full refunds are essential safeguards. A notable example occurred in September 2024, when the Texas Attorney General reached a settlement with a Dallas-based healthcare technology company over misleading claims about AI accuracy [7]. This case highlights the need for safety exit clauses to protect both patients and healthcare providers.
Be wary of "plug and play" promises. For example, while Google's diabetic retinopathy AI boasted 96% lab accuracy [4], 55% of images were deemed ungradable in real-world settings [6]. Similarly, integrating clinical AI into electronic health records often takes months, despite some vendors advertising a two-week timeline. To verify these claims, consider running a three-month pilot program in select clinical units using local data [10]. This approach can help ensure the AI performs as promised in your specific environment.
2. How Is Our Data Protected and Used in AI Training?
Before sharing any data, every AI vendor handling Protected Health Information (PHI) must sign a Business Associate Agreement (BAA). This isn't optional - it's a legal requirement under HIPAA. Be wary of vendors claiming a "conduit exception", as most AI services that process or analyze data don't meet the criteria for this exemption [11].
"Any AI system that 'creates, receives, maintains, or transmits' PHI requires a Business Associate Agreement." – Jennifer Walsh, Chief Compliance Officer, Health1st AI [1]
A strong BAA should include specific safeguards. For instance, it must prohibit using PHI for vendor training unless you provide explicit written consent. It should also require breach notifications within a set timeframe - ideally within 24–72 hours, but no later than 60 days - and mandate data deletion within 30 days after the contract ends [11]. The consequences of non-compliance can be costly. One hospital faced a $2.3 million fine when data like ZIP codes, ages, and diagnoses in a dataset led to patient re-identification [1].
When it comes to data de-identification, HIPAA provides two approved methods:
- Safe Harbor Method: This involves removing all 18 specific identifiers from the data.
- Expert Determination: A qualified statistician certifies that the risk of re-identification is less than 0.05% [1][11].
Always ask for documented expert validation if a vendor claims their data is anonymized [5].
Another critical aspect is third-party risk management, specifically data segregation and the subprocessor chain. If your vendor relies on third-party providers like AWS, Azure, or OpenAI, ensure BAAs are in place for every entity handling your data. This includes logging services, vector databases, and large language model (LLM) providers. Weak links in this chain can lead to breaches, and the stakes are high: in 2025, 13% of organizations reported breaches tied to AI models, with third-party compromises averaging $4.91 million in damages [5].
Beyond contracts, technical safeguards are non-negotiable. Insist on AES-256 encryption for stored data, TLS 1.3+ for data in transit, and maintain audit logs for at least seven years [1].
Finally, plan for the end of your vendor relationship. Your agreement should require certified data destruction or crypto-shredding, with a senior officer certifying that all data - including backups and derivatives - has been erased. If any data is retained, it must be thoroughly documented [11]. To verify compliance, request technical architecture diagrams or SOC 2 Type II attestations to confirm the vendor can segregate and delete your data as promised. To streamline this process, healthcare providers can use tools to automatically answer security questionnaires and verify vendor documentation.
3. What Liability and Indemnification Terms Are in Place for AI Risks?
Once performance warranties and data protection protocols are in place, the next critical step is addressing liability and indemnification to tackle risks unique to AI. Healthcare AI contracts must account for challenges that go beyond those seen in traditional IT systems. Unlike conventional software, AI models can sometimes generate "hallucinations" - outputs that seem believable but are factually incorrect[12]. As Douglas A. Grimm, Partner and Health Care Practice Leader at ArentFox Schiff, points out:
"Indemnification provisions should cover harm caused by inaccurate or false outputs that appear plausible but are incorrect, which is not typically a concern in deterministic software."[12]
Limiting liability to just the contract fees often falls short, especially when a single AI error could result in severe patient harm or regulatory penalties. Experts suggest setting liability caps at no less than five times the annual contract value[4]. For high-risk scenarios, negotiating "supercaps" is crucial to address situations involving patient injury, bodily harm, or significant fines[12][13].
Modern contracts for AI in healthcare often adopt shared risk models. Under this approach, vendors take responsibility for design and training flaws, healthcare organizations oversee deployment and management, and end users are accountable for incorrect data inputs. It's also essential to confirm that vendors carry robust insurance policies, such as cyber liability, technology errors and omissions, and professional liability. Make sure your organization is listed as an additional insured on all relevant policies[12].
Pay close attention to output exclusions in contracts. Some vendors may limit their liability for outputs generated from specific prompts, leaving your organization vulnerable if harmful results occur[12][13]. Instead, insist on indemnification that explicitly covers risks like algorithmic bias, regulatory violations (such as HIPAA or FDA non-compliance), and third-party claims stemming from the AI's outputs.
4. How Do You Maintain Governance and Monitor AI Performance Over Time?
Once your AI system is up and running, keeping it on track requires constant attention. Unlike traditional software, AI models can experience model drift - a gradual decline in accuracy as the data they encounter in the real world starts to differ from their original training data [14][15]. If this drift goes unchecked, it can lead to significant problems before you even realize there's an issue. That's why ongoing monitoring is critical, not just during initial vendor evaluations but throughout the system's lifecycle.
To ensure proper oversight, you'll need a cross-functional team. Your vendor should involve a mix of experts, including a clinical champion to assess real-world outcomes, a data scientist to track technical performance, and an administrative leader to address ethical and enterprise risk concerns [15]. As one technical leader explained:
"In addition to making sure that it is keeping the lights on and still working, as with AI and ML, [the product] is going to change over time. So we're gonna have to keep revalidating that the model [to make sure it] is not drifting." [15]
Demand real-time performance dashboards from your vendor to stay on top of both technical metrics (like sensitivity and specificity) and operational metrics (such as false positive rates and alert burden). These dashboards should include clear performance thresholds outlined in your contract [4]. On top of that, ask for quarterly bias audits that evaluate the system's performance across different demographic groups - factors like race, ethnicity, age, and sex should all be considered [4].
For a more structured approach, vendors should follow frameworks like the NIST AI Risk Management Framework (AI RMF). This ensures that drift tracking and incident response are built into the system, particularly focusing on the "Measure" and "Manage" functions [14][16]. Your service-level agreement (SLA) should also spell out when and how the vendor will notify you about necessary retraining or updates, as well as who is responsible for making adjustments to workflows and optimizations [17][9].
"No reported adverse events likely means no monitoring system, not that it's safe." – Public Health AI Handbook [4]
Start with a phased implementation, such as a pilot program lasting one to three months. This controlled rollout helps establish baseline performance and ensures that monitoring systems and vendor responsiveness are up to par before expanding the system across your organization. A pilot phase also doubles as an audit preparation measure [4].
5. How Do You Ensure Compliance with Evolving Regulatory Standards?
Navigating the regulatory landscape for healthcare AI is no small task. Vendors must juggle compliance with HIPAA, FDA guidelines, the EU AI Act, and a growing patchwork of state laws. A major milestone came in January 2026, when the FDA and EMA jointly introduced the Guiding Principles of Good AI Practice. These principles emphasize responsibility, transparency, and risk awareness, as highlighted by Kathie Clark, Technology & Innovation Partner at Just in Time GCP:
"AI cannot be evaluated like traditional software. In January 2026, the FDA and EMA jointly published the Guiding Principles of Good AI Practice... it sets a clear bar for responsibility, transparency, and risk awareness." [3]
Unlike traditional software, AI systems require more than pass/fail testing. Vendors must document a "context of use" that defines the system's scope, intended users, and impact. This is paired with structured risk assessments to address AI's unpredictability [3].
State-level regulations further complicate compliance. For instance, California's SB1120 enforces human oversight and fairness in AI-driven healthcare decisions to reduce automated biases in clinical and coverage determinations [7]. By late 2025, the FDA had already approved nearly 1,000 AI-driven medical devices [7], and states like Colorado and California began rolling out new AI laws in 2026 and 2027. To keep up, vendors need robust processes for tracking legal updates and informing their clients. Contracts should include "Changes in Regulatory Landscape" clauses, ensuring systems are updated as laws evolve [7]. This adds layers of documentation and verification to meet these legal demands.
To confirm a vendor's compliance readiness, look for the following:
- Updated BAAs and subprocessor lists reflecting the latest regulations.
- SOC 2 Type II reports issued within the past 12 months [11].
- Annual third-party penetration tests targeting AI-specific vulnerabilities, such as prompt injection [18].
The Zedly AI Editorial Team underscores the importance of these measures:
"The legal trigger is the PHI, not the contract. Sharing PHI with a vendor that has not signed a BAA is itself a HIPAA violation, regardless of the vendor's security posture." [11]
Beyond current compliance, vendors must demonstrate ongoing audit readiness. Ask about their processes for monitoring regulatory changes. Do they have dedicated compliance teams or participate in industry working groups? In September 2024, the Texas Attorney General reached a groundbreaking settlement with a Dallas-based healthcare technology company over AI hallucinations and misleading claims [7].
Audit readiness should include transparent documentation, quarterly bias audits broken down by race and ethnicity [4], and clear incident notification protocols [18]. These practices are essential for staying ahead of regulatory scrutiny and ensuring your organization is prepared when audits arise.
6. What AI-Specific Security Measures Are in Place?
When it comes to healthcare AI systems, standard IT security measures like basic encryption and firewalls just don’t cut it. AI introduces unique risks, such as adversarial attacks and the potential for sensitive patient data to be exposed. As Jakub Szarmach points out:
"Security and safety due diligence cannot stop at generic IT questions." [22]
To address these risks, vendors must adopt advanced technical safeguards. For starters, ensure they use AES-256 encryption for data at rest, TLS 1.3+ for data in transit, and strong API authentication protocols like OAuth 2.0 or SMART on FHIR to comply with HIPAA Security Rule requirements [1]. Additionally, proper de-identification methods are critical. Jennifer Walsh, Chief Compliance Officer at Health1st AI, highlights the dangers of incomplete de-identification:
"Simply removing names isn't enough... ZIP code + age + diagnosis was sufficient for OCR to re-identify patients" [1].
Failing to meet these standards could lead to severe regulatory penalties, making thorough de-identification a non-negotiable requirement.
Tackling AI-Specific Threats
Adversarial testing is a must to uncover vulnerabilities unique to AI. Healthcare AI systems are particularly susceptible, with attackers needing as few as 100 samples to exploit them. Worse, data poisoning attacks have a success rate of over 60% [21]. Detection of such attacks can take months - 6 to 12 months in some cases, if they’re caught at all [21]. Vendors should conduct adversarial testing at least quarterly, using frameworks like NIST's AI 100-2e2023 attack classifications [19]. Testing should cover a range of scenarios, including prompt injection, jailbreaking attempts, adversarial perturbations, and indirect injection attacks where malicious instructions are hidden in user-supplied files [20]. Tools like the Adversarial Robustness Toolbox (ART) for predictive AI and Promptfoo for generative AI can help scale these efforts [20].
Privacy-Preserving Techniques
Ask vendors about privacy-preserving training methods to reduce risks during model development. Two effective approaches include:
- Synthetic data: This creates fake yet statistically accurate patient data, completely removing HIPAA risks during training.
- Federated learning: Models are trained locally on-site, sharing only patterns rather than raw patient data [1].
These methods work alongside traditional security measures to limit data exposure during training and handling.
For added protection, confirm that safeguards are in place to prevent model extraction attacks, where adversaries attempt to reverse-engineer the model to access sensitive training data [1]. Audit logs should track all PHI access and be retained for at least seven years, as required by HIPAA [1]. If third-party cloud providers like AWS or Azure are involved, ensure Business Associate Agreements (BAAs) cover every link in the supply chain [1].
Building a Secure Framework
A strong security architecture is critical because a single compromised vendor can affect multiple healthcare institutions. Look for features like Bring Your Own Key (BYOK) encryption and client-configurable data retention periods [21][22]. Request model scorecards that detail the system’s security posture and known limitations. Additionally, incorporate AI-specific breach scenarios into tabletop exercises to prepare for potential incidents [1][22].
These measures form the backbone of an audit-ready AI security framework, ensuring that healthcare AI systems are equipped to handle both traditional and AI-specific threats effectively.
7. What Explainability and Auditability Features Does the AI Provide?
Transparent decision-making and traceable audit records are essential for meeting audit standards and reducing risks associated with AI in healthcare. These systems cannot operate as mysterious "black boxes." When an AI suggests a treatment or flags a patient for intervention, clinicians need to understand why that recommendation was made, and auditors must confirm the system performs as expected. Mike Sutten, CTO and SVP at Innovaccer, puts it clearly:
"Physicians need clarity on model limitations, confidence levels, and accuracy to make informed decisions." [17]
Without this clarity, clinicians are left guessing about the rationale behind AI-driven decisions.
What Documentation Should Vendors Provide?
Vendors should supply comprehensive documentation, such as model cards that detail the AI's training data, logic, and limitations. For instance, Innovaccer's "Galaxy" platform provides full audit trails for RADV, CMS, and OIG compliance, along with transparent AI models that include fairness checks and auditable logic [17]. Look for similar transparency from your vendor to ensure independent teams can evaluate whether the system behaves as promised.
Additionally, request stratified performance metrics instead of a single accuracy figure. For example, don’t settle for a "95% accuracy" claim - ask for metrics broken down by race, ethnicity, age, sex, socioeconomic status, and insurance type. This level of detail is critical to uncover disparities. In the case of the OPTUM algorithm, while it accurately predicted costs, it underestimated health needs for Black patients, leaving 46.5% more Black patients without necessary care management [4][10]. A single aggregate metric would have hidden this inequity.
Audit Trails and Real-Time Monitoring
Every AI decision should be accompanied by a traceable record. Vendors must provide quarterly audit logs that document all data and API access [4]. These logs should include details like who accessed patient health information (PHI), when it was accessed, and what the AI recommended. As Deb Raji from the Health AI Partnership explains:
"Audits are independent evaluations of model performance. They enable the investigation of how well the deployed system's behavior matches articulated performance expectations." [2]
Auditors should also be able to test the AI system on demand with various inputs to evaluate its reasoning in different scenarios [2]. This is essential for identifying algorithm drift, where the AI's performance deteriorates over time due to changes in the underlying data. Without ongoing monitoring, these performance issues could go unnoticed for months. Regular tracking also supports bias testing and fairness evaluations.
Bias Testing and Fairness Audits
Transparency isn't just about understanding how AI works - it’s also about ensuring it works fairly. Request results from both internal and external fairness audits to confirm the system performs equitably across demographic groups [4][10]. Be wary of vendors who claim their system is fair simply because it doesn’t use race as a feature. As the Public Health AI Handbook highlights:
"Fairness through unawareness doesn't work; race correlated with many features." [4]
Include contractual safeguards that allow you to terminate agreements if audits reveal a disparate impact greater than 10% across patient groups [4][10]. Also, establish triggers for additional audits, such as when new ICD codes are introduced, imaging equipment changes, or biological measures shift unexpectedly [2]. For example, the Epic sepsis model showed strong technical performance (AUC of 0.76–0.83) but had an 88% false positive rate in external validation, leading to alert fatigue [4]. Independent audits across multiple sites could have flagged this issue earlier.
Human Oversight and Clinical Validation
Human oversight remains a critical layer of accountability. Transparency also involves informing patients when they are interacting with AI rather than a human. As David Marc, PhD, from The College of St. Scholastica, points out:
"There's not always a lot of transparency about whether a product is leveraging AI or whether this is some other form of automation that is happening behind the scenes... This transparency is one of those ethical concerns." [8]
External validation across at least three independent sites is also crucial [4]. For instance, Google’s diabetic retinopathy AI achieved 96% accuracy in controlled lab settings but struggled in real-world conditions, with 55% of images being ungradable during field tests in India [4][10]. Lab success doesn’t guarantee real-world reliability. As Crystal Clack, Application Consultant at Microsoft, emphasizes:
"Human oversight is crucial to help prevent inappropriate or harmful responses. Humans can identify biases, inaccuracies, and potential risks that automated systems might miss." [8]
Finally, ask for documentation on how clinicians can override AI recommendations and how those overrides are logged for system improvement. Combining technical transparency, rigorous auditing, and human oversight ensures AI systems are trustworthy for healthcare organizations and verifiable for regulators.
8. How Does Censinet AITM Accelerate Third-Party Risk Assessments?
Audits often require speed, but traditional third-party risk assessments can drag on for weeks. To address this, Censinet AITM automates critical steps in the assessment process, allowing vendors to complete security questionnaires in seconds. It also extracts key details from policies, certifications, and specifications, summarizing vendor evidence automatically. This eliminates the need for manual review of lengthy documents, enabling risk teams to focus their time on analysis rather than tedious paperwork. The result? Faster, more efficient evaluations without compromising thoroughness.
Censinet AITM goes beyond basic questionnaires by tracking product integrations and fourth-party risks - important factors when assessing AI systems that rely on external data sources or subprocessors. The platform creates detailed risk summary reports, consolidating findings across security controls, compliance issues, and potential vulnerabilities. These reports give audit teams the documentation they need upfront, saving time and ensuring readiness.
The platform’s human-in-the-loop approach ensures that while automation speeds up processes, critical oversight remains intact. Risk teams maintain control through customizable rules and review processes, using automation as a tool to enhance - not replace - decision-making. Findings and tasks related to AI risks are routed to the appropriate stakeholders, such as members of an AI governance committee, for review and approval. With real-time risk data displayed in Censinet RiskOps, the platform acts as a centralized hub for managing AI-related policies, risks, and tasks, much like "air traffic control" for AI governance. This combination of speed, oversight, and centralized management not only accelerates assessments but also strengthens proactive risk management when evaluating AI vendors.
9. What Human Oversight Is Built into AI Governance Processes?
When it comes to evaluating AI vendors - especially in healthcare - human judgment plays a critical role. Decisions in this field directly affect patient safety and data privacy, making human oversight indispensable. While automated tools can monitor systems, humans are essential for interpreting data and stepping in when necessary. Regulatory frameworks like the NIST AI Risk Management Framework and the EU AI Act emphasize this need by requiring ongoing human monitoring, model retraining, and third-party audits for AI systems in healthcare settings [5][24]. Vendors are now expected to include checkpoints for human review as part of their processes.
It's important to ask vendors for detailed workflows that outline how evidence is validated and how stakeholder approvals are integrated. For example, before finalizing any AI-generated output - whether it's a risk assessment, a security recommendation, or a compliance report - there should be documented processes ensuring human review. Additionally, vendors should maintain audit logs that confirm these reviews have taken place, providing concrete proof of oversight [5].
Platforms like Censinet RiskOps™ take these protocols further by incorporating streamlined oversight mechanisms. Through human-in-the-loop workflows, the platform routes findings and tasks to designated stakeholders for review and approval. Teams can set customizable rules to maintain control, ensuring automation supports human decision-making rather than replacing it. For high-stakes risks, findings are escalated to an AI governance committee, creating a clear accountability structure. The platform also features a real-time AI risk dashboard, which acts as a centralized hub for tracking policies, risks, and tasks - making it easier to identify areas that need human intervention.
In addition, vendors should provide role-based training protocols that address AI limitations, along with clear documentation of decision boundaries. For instance, there should be guidelines prohibiting unauthorized sharing of protected health information (PHI) and mechanisms for staff to override AI recommendations when necessary [17][23]. These measures ensure healthcare staff know when and how to step in. To verify compliance, vendors must also ensure that audit logs are accessible and reviewable [5].
10. How Do You Support Collaborative Risk Management and Benchmarking?
Healthcare organizations often share vendors, face similar threats, and benefit from pooling insights. This makes collaboration essential when managing risks and benchmarking performance. To round out your audit readiness for AI governance, it’s important to assess how AI vendors enable collaborative efforts - both within your organization and across the broader healthcare ecosystem. The ideal platform should integrate risk data, streamline workflows, and benchmark performance against industry standards like NIST CSF, Health Industry Cybersecurity Practices (HICP), and Healthcare and Public Health (HPH) Cybersecurity Performance Goals (CPGs). A shared approach to risk lays the groundwork for continuous monitoring and collaborative benchmarking.
Modern RiskOps platforms play a key role by centralizing vendor scoring, allowing organizations to evaluate AI and supply chain vendors more effectively. This is especially critical given the growing healthcare supply chain security challenges. Automated assessment platforms have shown to reduce assessment costs by 40–60% while improving coverage across vendor portfolios [5]. Features such as AI-powered document analysis, which verifies vendor claims against evidence like SOC 2 reports or ISO certifications, help compliance teams cut down on manual review efforts.
Censinet RiskOps™ takes this a step further with a platform specifically designed for managing risks tied to Protected Health Information (PHI), medical devices, and the healthcare supply chain. It continuously monitors external signals - such as security incidents, compliance breaches, and operational disruptions - to provide real-time insights into supply chain health. Through centralized dashboards, risk teams can gain a clear view of third-party risks, including PHI and medical device vulnerabilities. Acting like "air traffic control" for AI governance, Censinet RiskOps™ routes key findings and tasks to the appropriate stakeholders, including AI governance committee members, for timely action. Its AI risk dashboard aggregates real-time data, serving as a central hub for managing AI-related policies, risks, and tasks.
When evaluating vendors, ask for examples of how their platform enables collaboration. Can they prioritize vendors based on criticality, ensuring deeper assessments for those with access to sensitive data or critical workflows? Does their system validate evidence rather than relying on vague claims? These features are essential for moving beyond simple data collection to truly managing enterprise risk effectively.
Conclusion
AI operates differently from traditional software, making vendor selection a critical step in its adoption. Kathie Clark, Technology & Innovation Partner at Just in Time GCP, emphasizes this point:
"AI cannot be evaluated like traditional software... vendor selection is one of the most important control points when adopting AI-enabled solutions" [3].
The stakes are high. In 2024, global losses tied to AI hallucinations were estimated at $67.4 billion [18].
Healthcare organizations face a knowledge gap when it comes to AI. Vendors have deep insights into their models - where they were trained, their limitations, and potential failure modes. Meanwhile, buyers often rely solely on sales materials [4]. To level the playing field, it's essential to ask pointed questions about warranties, data security, liability, governance, and explainability. These steps reduce risks and enhance patient safety.
Regulations are catching up fast. The FDA and EMA released their "Guiding Principles of Good AI Practice" in January 2026, and the EU AI Act will enforce strict rules for high-risk systems starting August 2026. Noncompliance could result in penalties of up to €35 million or 7% of global annual revenue [18]. Joe Braidwood, CEO of GLACIS, underscores the gravity of this shift:
"Due diligence is no longer optional - it's a legal requirement" [18].
Traditional audits fall short when it comes to assessing AI. They often miss critical aspects like model behavior, data origins, and bias mitigation. For example, the Epic sepsis model revealed how technical metrics can obscure clinical inefficiencies. Accuracy on paper does not guarantee effectiveness in practice. This highlights the need for external validation, error verification, and continuous monitoring.
Asking these critical questions early in the process isn’t about stalling progress - it’s about adopting AI responsibly. By embedding these considerations into your vendor evaluation, you set the stage for AI use that protects patients, meets regulatory demands, and ensures your organization thrives in an AI-powered healthcare future.
FAQs
What evidence should we ask for to prove real-world clinical performance?
To gauge the clinical performance of an AI system, it's essential to seek external validation evidence that supports its effectiveness. This includes:
- Peer-reviewed outcomes: Look for studies published in reputable journals that detail the system's performance and methodology. Peer-reviewed research adds a layer of credibility by ensuring the findings have been scrutinized by experts in the field.
- Validation across multiple sites: Evidence from diverse clinical settings or institutions shows the system's reliability in varying environments. Multi-site validation reduces the risk of bias and demonstrates broader applicability.
- Key performance metrics: Metrics like sensitivity (ability to correctly identify true positives), specificity (ability to correctly identify true negatives), and calibration (alignment of predicted probabilities with actual outcomes) are critical for assessing accuracy and reliability.
These elements collectively provide a robust picture of the AI system's ability to perform effectively in real-world clinical applications.
What should a healthcare AI Business Associate Agreement (BAA) include?
A healthcare AI Business Associate Agreement (BAA) needs to clearly define critical elements like data usage policies, breach reporting timelines, and liability terms. Additionally, it should specify security measures, such as encryption standards, to safeguard sensitive information. Most importantly, the agreement must ensure full compliance with HIPAA regulations, especially when handling Protected Health Information (PHI).
How do we monitor and audit AI drift and bias after go-live?
To keep an eye on AI drift and bias after deployment, healthcare organizations should rely on solid systems for continuous performance checks and fairness evaluations. This involves using statistical tests like the Kolmogorov-Smirnov test and tracking metrics such as AUROC, precision, and recall. By blending real-time monitoring with batch reviews and incorporating retraining approaches - like transfer learning and human-in-the-loop governance - organizations can quickly spot and address drift. These practices also help maintain compliance and manage risks effectively.
