Terms and Conditions May Apply: What to Know Before Contracting for AI Services in Healthcare

As artificial intelligence (AI) tools continue to reshape healthcare delivery and operations, legal and compliance teams should proactively navigate a complex contracting landscape. Unlike traditional software, AI systems often involve continuous learning, probabilistic outputs, and opaque decision-making processes. These characteristics introduce unique legal and ethical challenges, especially when AI tools are used to inform patient care or handle sensitive health data.

Whether the AI solution supports diagnostics, workflow automation, or predictive analytics, healthcare organizations should ensure that contracts are tailored to address the technology’s inherent risks. Consulting experienced legal counsel can help organizations ensure AI contracts are appropriately tailored to their needs.

SCOPE, PURPOSE, AND DELIVERABLES

A threshold step in contracting for AI services is to clearly define the scope and purpose of the tool. Agreements should specify whether the AI system is, for example, intended to assist in diagnosis, automate administrative tasks, or stratify patient risk. Deliverable requirements should extend beyond implementation and may include ongoing performance expectations, implementation support, testing and monitoring, and other methods or milestones for measuring success.

For adaptive AI systems, it is essential to address how updates, retraining, and version control will be managed over time. These details help limit ambiguity and support regulatory compliance, especially when integration with electronic health records (EHRs) or clinical decision support systems is involved. AI tools are not all created equal, even among different versions of the same model or product family. Private, segregated instances may afford opportunities (e.g., custom parameters based on proprietary data) that are unworkable via public versions.

When a healthcare company is collaborating with an information technology company, it is also helpful for the parties to discuss and understand their respective objectives, concerns, and approaches. Without alignment, a clash of cultures can derail an otherwise promising relationship.

DATA USE AND OWNERSHIP

Because AI tools rely heavily on data inputs and may generate proprietary outputs, contracts should outline which party owns—and how the other party may use—the underlying data and the resulting predictions, insights, or models. If protected health information or de-identified data is used to train or refine algorithms, this raises questions under the Health Insurance Portability and Accountability Act of 1996 (HIPAA) and state privacy laws.

The contract should identify each party’s role—as a HIPAA-covered entity, business associate, subcontractor, service provider or processor, controller, or regulated consumer health data entity—and align that role with data-use permissions, patient or consumer request handling, breach notification, and subcontractor flow-down obligations.

The agreement should limit secondary uses of data, prohibit unauthorized commercial exploitation, and require compliance with all applicable privacy regulations. It should also consider limiting or prohibiting vendor use of protected health information (PHI), limited datasets, de-identified data, usage telemetry, prompts, or outputs to train, fine-tune, benchmark, or improve models except as expressly permitted. Additionally, the agreement should require data minimization, retention schedules, deletion or return, audit rights, and documentation of de-identification methodology.

LIABILITY AND RISK ALLOCATION

Unlike conventional tools, AI systems can generate inaccurate or biased outputs that lead to clinical errors, missed diagnoses, or inappropriate care recommendations. Contracts should include robust indemnification provisions and allocate liability for malfunctions, misdiagnoses, or system errors. Indemnity and liability provisions should allocate responsibility by cause, including model design defects, inaccurate or biased outputs, integration errors, EHR mapping or configuration failures, provider-supplied data-quality issues, misuse outside intended use, failure to deliver updates or warnings, cybersecurity incidents, breach of privacy law, and infringement or misappropriation claims.

Indemnification clauses are vital in AI contracts, particularly because AI technology is still in its early stages and can be somewhat experimental. These clauses allocate responsibility for mistakes, errors, or unintended consequences that may arise from the use of AI tools. Contracts should clearly allocate risk—who pays for legal defense, who covers damages, and under what circumstances. Indemnification language should also address liability in the event of an improper use or disclosure of patient data or other data breach.

Limitations of liability should be evaluated carefully for exclusions covering confidentiality breaches, privacy-law violations, security incidents, intellectual property (IP) infringement, gross negligence or willful misconduct, regulatory penalties, and patient-safety events.

Some contracting parties will attempt to limit potential liability by restricting high-risk use cases, but such limitations should be drafted in a manner that comports with the parties’ actual intended use.

Insurance Obligations

Parties should consider requiring vendors to carry technology errors and omissions insurance, establish performance guarantees through service level agreements, and include obligations to notify providers of known defects or risks. These issues are especially relevant when AI tools qualify as software as a medical device (SaMD) and fall under the US Food and Drug Administration’s (FDA’s) evolving regulatory framework. See FDA, Proposed Regulatory Framework for Modifications to AI/ML-Based Software as a Medical Device, Apr. 2019.

Given the potential risks associated with AI in healthcare, specific insurance obligations should be included in the contract. Healthcare organizations should consider requiring vendors to carry appropriate coverage for cyber security incidents, malpractice claims, and technology errors. Such provisions will protect healthcare providers from financial losses and ensure that AI vendors are prepared to handle any claims related to their technology.

Notice to Patients Regarding Use of AI

Providing adequate notice to patients is a fundamental aspect of any AI healthcare contract. Patients should receive adequate notice regarding what information is collected, how that information is used or shared, why their information is collected, and who has access to their information.

Contracts should include detailed provisions outlining the notice requirements to ensure compliance with privacy laws and regulations, including the California Consumer Privacy Act and the Washington My Health My Data Act, while separately ensuring compliance with applicable HIPAA use and disclosure limitations, including the minimum necessary standard under HIPAA. Experienced legal counsel is critical to ensure that appropriate patient notice is in place.

TRANSPARENCY AND EXPLAINABILITY

AI systems, particularly those that operate as “black boxes,” often lack explainability. This can create major legal challenges when the AI informs clinical decisions or impacts patient outcomes. To mitigate this risk, contracts should require documentation sufficient to evaluate the tool’s performance characteristics, validation methods, limitations, intended use, and available audit information.

Providers should also establish policies ensuring that human oversight remains part of the decision-making process. The provider should retain final clinical decision authority; the agreement should require documentation of overrides, escalation pathways, and mechanisms to report suspected model errors or patient-safety issues. Recent federal guidance emphasizes the importance of transparency in AI design and deployment. See US Department of Health and Human Services, Trustworthy AI Playbook: Executive Summary.

INTELLECTUAL PROPERTY AND TERMINATION

AI contracts often involve complex questions about IP, especially when the vendor customizes the tool using provider data or when new models are developed jointly. Agreements should clearly specify IP rights and confidentiality obligations related to derivative works, model refinements, and custom features as well as model outputs. Termination clauses should include obligations to return or destroy provider data, allow transitional access during offboarding, and ensure continued data security.

Organizations should evaluate the likely cost, resources, and time required to transition to a new provider or application. Additionally, contracts could include a “change in law” clause requiring vendors to adapt their services in compliance with future regulations issued by the FDA, Office of the National Coordinator for Health Information Technology (ONC), or Federal Trade Commission (FTC).

CONSIDERATIONS

Contracting for AI services is not a plug-and-play process. It requires specialized provisions that address unique legal, technical, and ethical risks, especially when patient data is at stake. Before contracting, organizations should conduct a pre-contract AI risk assessment covering intended use, regulatory status, data flows, privacy notices and consents, model validation, cybersecurity, bias testing, human oversight, vendor subcontractors, offboarding, and incident-response procedures.

Healthcare providers should work closely with legal counsel, compliance professionals, and IT leaders to ensure their AI use agreements contain strong safeguards around liability, privacy, explainability, and regulatory compliance. With careful consideration, organizations can harness the power of AI while upholding their obligations to patients, regulators, and the public.

HOW WE CAN HELP

Our lawyers stand ready to assist organizations with AI contracting strategies and related legal considerations. For more AI in healthcare news, subscribe to Health Law Scan and explore all articles in our AI in Healthcare series.