In May 2026, the National Association of Independent Review Organizations (NAIRO) released a formal position paper on AI use in the independent review industry. With the paper, NAIRO explicitly set out its position on where (and how) AI can be used. Medical record summarization is one of the areas where NAIRO found AI appropriate for widespread use.
For organizations that use AI to power their medical review process, this endorsement is exciting. Medical record summarization helps teams keep up in industries like claims, risk management, and workers’ compensation – and the official guidance is that AI should be used to enhance both timeliness and accuracy. As the conversation has shifted away from “should we use AI for medical record summarization?” to “what does NAIRO-standard summarization look like” organizations are now getting a clearer idea of what compliant AI use looks like in claims.
For IMEs, claims managers, and review organization leaders, the NAIRO paper is a powerful milestone. Medical record summarization is ready for AI – and AI tools are ready for your organization. Now that AI summarization is the accepted standard for claims and review work, what does responsible AI summarization require? Here is what industry users need to know:
What does responsible AI medical record summarization look like?
NAIRO supports the responsible adoption of AI and holds the position that the final result of every independent review must rest, at the final stage, with an independent and qualified human reviewer. Responsible AI medical records summarization leaves the final decision to a human expert, who acts as more than just a rubber stamp. Responsible AI medical record summarization should include:
- Source citations: Responsible AI medical record summarization should be grounded to a trusted source record. A policy database, clinical guideline, claim file, or other trusted source for information should be behind each AI-generated claim.
- Human QA: AI models don’t have lived experience like human reviewers. Clinical experts are capable of making complex decisions that take many factors into account – like tradeoffs, operational constraints, or moral dilemmas. AI models do not have this human insight, knowledge, or creativity. Human oversight is necessary to ensure all cases are fairly weighed.
- Claims-specific training: Not all AI models are alike! Good models for claims are trained with claims in mind. This means datasets that are tailored to your specific market, industry, and use case.
- An audit trail: Health data is sensitive, and so is data related to a healthcare claim. An audit trail ensures the file is tracked each time it is opened or modified.
When someone’s life (or necessary medical coverage) is on the line, human reviewers should have the final say about whether or not they’ll be able to make a claim. However, this doesn’t mean that AI is not useful (and recommended) at other points in the process – and responsible AI medical record review should use source citations, human QA, claims specific training, and an audit trail to protect the integrity (and safety) of data.
What AI summarization tools support audit trails for claims decisions?
A useful AI summarization tool should follow guidance for responsible AI medical records summarization. This means citations that link to a trusted source, human oversight and QA, claims specific training, and an audit trail. Using off the shelf LLM tools (like ChatGPT or Claude) can leave your organization open to missed compliance standards, data integrity issues, privacy breaches, and errors – so it is better to choose a software that is already built for your organization’s needs. AI summarization tools like Wisedocs support audit trails for claims decisions, and are built to be compliant from the very first file review.
What is the NAIRO standard for AI in medical record review?
NAIRO set out 5 principles for AI-augmented medical records review. These principles are:
1. Human determination:
Claims should be decided by human experts. Although AI cannot make the final determination, it can help with plenty along the way. AI can help speed up the process of reading medical records, classifying and indexing documents, creating medical timelines, and checking documents for consistency, before synthesizing the information that they contain.
In short, AI can be used at every part of the process leading up to the final claim decision. Having a robust and organized set of documents relating to each claim is what will help the manual reviewer do their job well. The end result is a process that is faster, easier, and more fair before the human expert even gets involved, meaning both parties get access to a timely (and unbiased) claim.
2. Reviewers should be AI-equipped:
Not only is AI use compliant, it is encouraged. NAIRO principles suggest that human reviewers should work alongside AI tools, not just for compliance reasons but to improve and refine the technology – a human reviewer can interrogate the AI output and mark when it is compliant and when it is not. This is a major milestone for insurers, claims organizations, and IMEs, as the official standard using AI for medical records review is not just faster and more accurate, it’s the standard.
3. Review should maintain independence and defensible governance:
Your AI medical records review tool should remain compliant with standards that are already recognized, including URAC AI, the NIST AI Risk Management Framework, ISO/IEC 23894 and ISO/IEC 42001. These standards are designed to ensure patients and insurers remain unbiased and compliant, and include controls like continuous monitoring, audit trail creation, and override authority documented at the case level.
4. Reviewers should not sacrifice judgement for speed:
Many of NAIRO’s most pro-AI recommendations are out of necessity: matching the speed of AI payers and providers is necessary in medical records review and case decisions. When claims are delayed because there’s too much paperwork, patients (and profits) suffer. To keep up, insurers, IMEs, and claims organizations will need to harness the power of AI – but this added speed can’t take the place of independent clinical or legal judgement. NAIRO emphasizes integrity of output and real decision making, not just a rubber stamp.
5. NAIRO operates as the industry’s first line of enforcement
NAIRO aims to help translate federal pledges and state statutes into operational practice. Right now, this federal and state guidance is relatively simple: when a healthcare claim is denied, there should be a human decision maker doing it. This is a practical way to translate federal (and state) initiatives into practical, industry-wide action.
Does AI medical record summarization require human oversight?
Yes, absolutely. According to NAIRO guidance, a human decision maker must be behind each and every decision about a claim. However, other parts of the process also require oversight. Human QA is still necessary at many points in the claims process, and combining AI with expert human validation increases confidence in the output by 4x. Even casual users report higher trust in AI outputs when human reviewers are involved.
Human QA done early in the process makes outputs that much more reliable, trustworthy, and useful to the end user.
What governance requirements apply to AI summarization in insurance claims?
These features are consistent with the direction set by the department of Health and Human Services (HHS), the Centers for Medicare and Medicaid Services (CMS), and the majority of US state legislatures. In June of 2025, American health insurers pledged six reforms aimed at cutting red tape, accelerating care decisions, and enhancing transparency in the prior authorization system. The first of these reforms states that medical professionals should review all clinical denials – this means a trained human reviewer must be the one behind every denial, on every claim. Today, 43 states have introduced more than 240 healthcare AI bills, all with a similar goal: at the end of the day, human medical professionals are the ones who should be approving or denying claims that have human consequences.
NAIRO’s position falls in line with guidance from the US federal government. Independent judgement matters, and the process of independent review is intended to bring objective clinical judgement into high-stakes medical coverage decisions.
How do I evaluate AI summarization tools for independent medical review?
AI powered summarization tools can add speed and accuracy when used well, but which one meets your team’s needs? Unless you are a very large organization, the answer is probably not to try and build one yourself. The cost of building an AI tool from scratch can quickly outpace the utility when you’re talking about billions of tokens, billions of parameters, millions of hours of processing, constant support for data security issues, and a team large enough to catch AI hallucinations around the clock.
Depending on the size of your organization, IMEs, claims teams, and insurers should ensure that the AI summarization tool that they choose includes:
- Industry-specific training relevant to the types of claims documents that they use, including healthcare records, labs, workers comp, bodily injury, or disability documents.
- Integration with their existing claims management system and workflows.
- A layer of human QA over AI-generated outputs.
- Turnaround times compatible with your organization’s needs.
- Capacity for high volume files.
Now that AI summarization is the accepted standard for claims, it is time to look beyond whether organizations should use AI for medical records summarization, and instead look at how NAIRO-standard summarization can be put into action to support more organizations, and more claims.
AI is quickly becoming part of the independent review process—but responsible adoption requires clear standards for how these tools are built, validated, and used.
Read NAIRO’s full position paper on the responsible use of AI in the independent review industry to explore the organization’s recommendations and what they mean for review teams evaluating AI-powered technology.
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