AI Service Agreements in Health Care: Indemnification Clauses, Emerging Trends, and Future Risks

Agreements with AI vendors present unique legal and operational risks, particularly around indemnification. Unlike traditional software or information technology (IT) service contracts, indemnification clauses in AI agreements must address a rapidly evolving risk landscape, including data privacy breaches, algorithmic bias, regulatory compliance failures, and intellectual property (IP) disputes. This alert explores the distinctive features of indemnification clauses in health care AI contracts, current market trends, and key considerations in light of evolving regulatory and technological developments.

How Indemnification Clauses in AI Agreements Differ From Traditional Service Contracts

Broader and More Complex Scope of Covered Risks

Traditional indemnification clauses in non-AI IT or software as a service (SaaS) agreements typically focus on data security and privacy incidents, third-party IP infringement claims, and bodily injury or property damage, where applicable. In contrast, AI service agreements, particularly in health care, must address a wider array of risks.

• Algorithmic Errors and Inaccurate Outputs: AI systems, particularly those using generative or probabilistic models, can produce unpredictable or erroneous outputs. 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.

• Bias and Discrimination: AI tools can inadvertently perpetuate or amplify bias, leading to discrimination claims. Health care providers should consider seeking indemnity for regulatory actions or lawsuits arising from biased AI outputs, especially as state and federal scrutiny of algorithmic fairness increases.

• Regulatory Non-Compliance: With health care AI subject to the Health Insurance Portability and Accountability Act (HIPAA), the US Food and Drug Administration (FDA) oversight, and emerging state laws, indemnification should extend to cover regulatory penalties or enforcement actions resulting from the AI vendor’s failure to comply with applicable laws.

• Data Use and Training Risks: AI vendors may seek rights to use client data for model training and improvement. Indemnification provisions should address claims arising from improper use of protected health information (PHI) or failure to de-identify data as required by law.

Carve-Outs and Limitations Unique to AI

AI vendors often seek to limit their indemnification obligations in ways that differ from those in standard IT contracts.

• Output-Related Exclusions: Vendors typically seek to disclaim responsibility for outputs generated in response to user prompts or inputs, particularly in cases where the health care provider modifies, customizes, or fine-tunes the model. Health care providers should carefully evaluate such disclaimers against applicable standards of care and regulatory requirements.

• Use-Case Restrictions: Indemnity may be limited to “authorized” or “intended” uses, with no coverage for outputs generated outside the agreed scope or for misuse by the health care provider.

• Liability Caps and Supercaps: Given the potentially high stakes of AI errors in health care, parties may negotiate higher liability caps for certain types of claims (for example, patient harm or fines and penalties), or “supercaps” for high-risk applications, while maintaining lower caps for general breaches.

Shared and Layered Responsibility

AI agreements often involve a “shared risk” model, where responsibility for harm may be distributed among the vendor (for model design and training), the health care provider (for deployment and oversight), and even the end user (for prompt engineering or data input). Indemnification clauses are increasingly tailored to reflect this complexity, with hybrid models that allocate risk based on the source of the error.

Current Trends and Market Practices

Expansion Beyond IP Indemnity

While indemnification for third-party IP infringement claims remains standard practice, certain AI vendors now offer expanded indemnification coverage for third-party claims arising from model outputs, which may include allegations of defamation, privacy violations, and regulatory non-compliance, subject to specific limitations and conditions which must be carefully evaluated against applicable health care regulations and patient safety requirements. However, these broader protections are often subject to significant negotiation and may be limited by carve-outs.

Increased Negotiation of Data-Related Indemnity

Health care providers are pushing for indemnification covering unauthorized use or disclosure of PHI, improper data training practices, and failure to comply with de-identification standards. AI vendors, in turn, may resist broad indemnity, especially where data is used to improve models for other clients.

Mandatory Bias Audits

Emerging state and federal regulations are increasingly focused on algorithmic fairness and the prevention of discriminatory outcomes in health care AI, including specific requirements for PHI and clinical decision-making. For example, states like California and Colorado have already enacted or proposed laws targeting algorithmic decision-making and bias in health care applications, with mandated documentation, testing protocols, and disclosure requirements that must be specifically addressed in vendor contracts. Indemnification clauses should expressly address liability for claims arising from algorithmic bias or disparate impact on protected classes. Providers are likely to require vendors to indemnify them for regulatory actions, lawsuits, or penalties resulting from biased AI outputs. Contracts may include representations and warranties that the AI model has been validated on diverse datasets and is subject to regular bias audits, with indemnity triggered by failures in these areas.

Insurance and Financial Backstops

Given the uncertainty and potential magnitude of AI-related claims, providers should require vendors to maintain appropriate insurance coverage with specified minimum coverage amounts, including (1) cyber liability insurance, (2) technology errors and omissions coverage, (3) professional liability insurance, and (4) commercial general liability insurance. All policies should name the health care provider as an additional insured. Insurance requirements and certificates must be reviewed by qualified insurance and health care counsel annually, upon any material change to the AI system’s functionality or use case, and whenever there are significant changes to applicable health care regulations or standards of care.

Tiered and Use-Case-Specific Indemnity

Market practice is evolving toward risk-based indemnification structures that align with the specific use case and potential liability exposure. For example, clinical decision support applications may warrant enhanced indemnification provisions and higher liability caps compared to administrative automation tools, subject to careful analysis of applicable health care regulations and standards of care. Some agreements also include “trigger” provisions, requiring renegotiation or review of indemnity terms if the AI system is materially updated or repurposed.

Notice, Cooperation, and Control Provisions

Given the complexity of AI-related claims, indemnification clauses increasingly include detailed procedures for notice, cooperation, and control of defense, ensuring that both parties are aligned in responding to third-party claims.

Sample Indemnification Language From Recent AI Service Agreements

Below are examples of indemnification provisions from AI service agreements, illustrating the range of approaches in the market.

AI Output Disclaimer

“Vendor warrants that its AI-generated outputs will meet or exceed industry standard accuracy as measured by specific performance metrics and benchmarks as stated in Exhibit A, which shall not be modified without Customer’s prior written consent. While Customer maintains responsibility for clinical decisions and the provision of health care services, Vendor remains liable for outputs that fail to meet warranted accuracy standards as stated in Exhibit A or contain material errors directly attributable to the Vendor Platform. Outputs generated by the Vendor Platform, which includes third-party Large Language Models (LLMs) as identified in Exhibit B, are based on probabilistic methods and must meet the minimum accuracy requirements and performance standards set forth in Exhibit A. Any changes to the LLMs identified in Exhibit B, or introduction of additional models, shall require Customer’s prior written approval. While Customer will exercise reasonable diligence in evaluating outputs, Vendor remains responsible for the accuracy and reliability of its Platform’s outputs as warranted above.”

Shared Responsibility Indemnity

“Third-party claim directly and solely arising from: (a) Customer Inputs or (b) Customer Data that violate third-party rights or applicable laws Customer will defend Vendor against any. Customer will indemnify Vendor for any damages, costs, and, if applicable, attorneys’ fees finally awarded against Vendor or agreed in settlement by Customer resulting from such claim.”

IP-Only Indemnity

• “Vendor will defend, indemnify and hold harmless Customer, its affiliates, directors, officers, employees, contractors, and agents, provided that Customer (a) promptly notifies Vendor in writing of such claim; (b) gives Vendor primary control of the defense and settlement of such claim, provided that no settlement affecting Customer’s rights or obligations shall be entered into without Customer’s prior written consent, which shall not be unreasonably withheld; and (c) provides reasonable cooperation to Vendor at Vendor’s expense against any claim by a third party alleging that the Vendor Platform, when used in accordance with the terms of this Agreement, infringes any intellectual property right of such third party and will pay any damages, costs, and attorneys’ fees finally awarded against Customer or agreed in settlement by Vendor resulting from such claim.”

• “Vendor has no obligation to defend or indemnify with respect to outputs that result from Customer Inputs or Customer Data that is in violation of Vendor’s terms, or results from Customer Inputs or Customer Data that Customer had actual knowledge would lead to infringing output at the time of submission.”

IP Indemnity Carve-Outs

“Vendor will have no liability or obligation with respect to any IP Claim if such claim is caused in material part by (a) unauthorized use of the Vendor Platform by Customer or its users in violation of the Agreement; (b) modification of the Vendor Platform by anyone other than Vendor or its authorized representatives; or (c) the combination, operation or use of the Vendor Platform with third-party data, hardware, or software not approved by Vendor, where the claim would not have arisen but for such combination.”

Key Takeaways

• Broader and More Specific Indemnity: Indemnification clauses will expand to cover new regulatory risks, including explainability, transparency failures, data misuse, privacy violations, regulatory penalties, bias-related claims, and IP infringement.

• Conditional and Dynamic Provisions: Indemnity may be conditioned on the vendor’s compliance with audit, documentation, and cooperation requirements, and will need to adapt as regulations change.

• Shared Responsibility: The allocation of risk is moving toward a shared responsibility model, with hybrid indemnity structures reflecting the interplay between vendor, provider, and user actions.

• Output Indemnification: Vendors often seek to limit indemnity for outputs, especially where the health care provider customizes or fine-tunes the AI or where outputs result from user prompts.

• Tiering: Tiered indemnity and liability caps are becoming more common, with higher caps for high-risk clinical applications.

• Insurance Integration: Contracts will increasingly require vendors to maintain insurance that specifically addresses AI-related regulatory risks.

• Increased Negotiation Complexity: Providers and vendors must be prepared for more detailed and frequent negotiations as the regulatory environment evolves.

Thoughts for the Future

Regulatory Evolution Will Drive Change

As federal and state regulators develop AI-specific rules — including mandatory bias audits, explainability requirements, and sector-specific liability standards — indemnification clauses must adapt to ensure compliance with these evolving standards. This includes compliance with existing regulations such as the FDA’s evolving framework for AI-enabled medical devices and state-specific AI regulations. Expect to see more explicit references to compliance with evolving laws and possibly mandatory renegotiation of indemnity terms as laws change.

Standardization and Best Practices

Industry groups and legal organizations may develop model indemnification clauses or best practice frameworks for health care AI contracts, helping to reduce negotiation friction and clarify expectations around risk allocation.

Dynamic, Use-Case-Specific Indemnity

As AI systems become more autonomous and are deployed in increasingly high-stakes clinical settings, indemnification will likely become more dynamic, adjusting to the risk profile of each use case, the degree of human oversight, and the evolving regulatory landscape. Indemnification clauses may include “trigger” provisions requiring review and possible amendment of indemnity terms if there are material changes in applicable laws or regulations. Vendors may be required to warrant ongoing compliance with all current and future regulatory requirements, with indemnity for any breach.

Insurance Market Maturation

As the insurance industry gains experience with AI-related claims, more tailored insurance products will emerge, potentially reducing the need for broad contractual indemnity or providing additional financial security for both vendors and health care providers.

Emphasis on Transparency and Oversight

Future indemnification clauses may require vendors to provide greater transparency into model training, data sources, and performance metrics, enabling health care providers to better assess and manage risk. Audit rights and ongoing monitoring obligations may become standard. Regulators are moving toward requiring greater transparency in AI systems, including the ability to explain how outputs are generated. This is particularly important in health care, where clinical decisions must be justified and auditable. Indemnification obligations may be conditioned on the vendor’s ability to provide documentation, model cards, or audit trails that explain the AI’s decision-making process. Contracts will likely require vendors to cooperate in regulatory investigations and provide access to model performance data, training data lineage, and documentation of updates or retraining.

Litigation and Judicial Guidance

As courts begin to address disputes arising from AI-related harm, judicial decisions will shape the enforceability and interpretation of indemnification clauses, potentially leading to new standards for causation, fault, and risk allocation in the AI context.

Conclusion

Indemnification in health care AI contracts is a rapidly evolving area that requires careful consideration of federal and state health care regulations, FDA requirements, and the unique risks and uncertainties of deploying advanced technology in a highly regulated, high-stakes environment. In-house counsel and health care professionals should approach these clauses with a clear understanding of the distinctive risks posed by AI, the current market trends, and the likely direction of future developments. Proactive negotiation, careful risk assessment, and ongoing monitoring will be essential to ensure that indemnification provisions provide meaningful protection as the legal and technological landscape continues to evolve.

For questions about AI-related legal or regulatory issues, contact the author or the ArentFox Schiff professional who handles your matters.

Additional research and writing by Julia Cofiño, a 2025 summer associate in ArentFox Schiff’s San Francisco office and a law student at UC Berkeley School of Law.