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AI can reduce the time personal-injury teams spend sorting records, reviewing discovery and preparing drafts, while insurers may use it to analyze accident images or estimate settlement values. It does not, on the evidence currently available, independently establish a claimant’s legal entitlement or reliably price an individual injury case. A lawyer must verify the facts, law, calculations and generated language, protect confidential information, communicate appropriately with the client and remain accountable for every filing and recommendation.
The American Bar Association’s Formal Opinion 512 (July 29, 2024) treats generative AI as a tool that operates inside existing professional duties, not as a transfer of those duties to software. The National Association of Insurance Commissioners (NAIC), in material updated April 3, 2026, describes AI uses in insurance claims while emphasizing that insurance and consumer-protection rules still apply. Pennsylvania’s Insurance Department Notice 2024-04, published April 6, 2024, illustrates how one state is addressing risks such as inaccuracy, unfair discrimination, data vulnerability and poor transparency.
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What AI means in a personal-injury case
In this context, AI is software that finds patterns in text, images or other data and produces a classification, summary, prediction or draft. A law firm might use it to locate medical-record references, organize discovery or prepare a first draft of a demand letter. An insurer might use a model to examine vehicle-damage photographs or generate an estimate of an eventual claim settlement value.
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Those are assistance functions. They are not proof that a case meets the elements of negligence, that an injury was caused by a particular incident, that damages are legally recoverable, or that a proposed settlement is fair. The reviewed ABA guidance does not report personal-injury-specific adoption rates or outcome improvements, and the NAIC material does not establish that every carrier, claim or jurisdiction uses AI.
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Where a law firm can use automation
Organizing a large case file
Personal-injury matters often contain hospital charts, physician notes, imaging reports, bills, wage records, photographs, police reports, witness statements, correspondence and prior-claim documents. A text-analysis system can help sort files by date or document type, identify repeated names and dates, and produce a searchable index. That can make it easier for a lawyer or paralegal to find a particular treatment entry without treating the software’s index as a complete medical chronology.
Reviewers should sample the underlying records after every automated pass. Scanned pages, handwriting, unusual abbreviations, poor image quality and records that use different names for the same condition can cause omissions or false matches. The original document, not a generated summary, remains the source for a factual assertion.
Technology-assisted discovery review
Formal Opinion 512 discusses technology-assisted review in electronic discovery. In a personal-injury matter, a system may help group emails, messages, photographs or employer files for human review and flag documents containing selected terms. Counsel still has to define the collection, search and privilege protocol, test whether relevant material is being missed, and make responsive and privilege decisions under the applicable rules and court orders.
Summaries and chronology drafts
Generative tools can produce a first-pass chronology or summarize a deposition, treatment episode or expert report. A useful review process compares each important date, quotation, diagnosis and limitation with the source record. A summary that silently changes “possible” to “confirmed,” or confuses a pre-existing condition with a new injury, can distort liability and damages analysis.
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Drafting routine correspondence
The ABA identifies drafting letters, contracts, briefs and other legal documents as possible generative-AI uses. A lawyer may ask for a draft client update, records request, discovery response or demand-letter outline. The draft must be rewritten or approved by counsel for the actual facts, requested relief, tone, deadlines and jurisdiction. Generated citations and quotations require independent checking; fluent wording is not evidence that an authority exists or says what the draft claims.
Internal issue spotting
A firm can use a controlled system to suggest questions for an intake interview or identify gaps such as a missing wage statement, an unclear medical causation opinion or an unconfirmed insurance policy limit. These prompts are planning aids. They do not replace an attorney’s investigation, consultation with experts or decision about whether a claim is legally and factually supportable.
How insurers may use AI in claims
The NAIC says insurers use AI in claims for functions including accident-image analysis and estimating ultimate claim settlement values. Image analysis might classify visible vehicle damage or compare photographs with repair information. A valuation model might draw on claim data to generate a recommendation for reserves or settlement handling.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsNeither description proves that a particular insurer uses a particular model in a particular personal-injury claim. Nor does a model output establish the correct value of an individual claim. A claimant can have damages that are not visible in an image, such as traumatic symptoms, future care needs, lost earning capacity or disputed causation. A lawyer should ask what information was considered, what was excluded, whether a human reviewed the output and how the recommendation can be challenged.
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Why human judgment remains legally essential
Formal Opinion 512 applies existing professional responsibilities when lawyers use generative AI. The practical implications are easiest to see by duty:
| Responsibility | What counsel must do in an AI-assisted workflow | Typical failure to prevent |
|---|---|---|
| Competent representation | Understand the tool’s capabilities and limitations, select an appropriate use and verify the result against authoritative sources. | Relying on a confident but incomplete chronology, incorrect legal rule or fabricated citation. |
| Confidentiality and client information | Determine where prompts and files go, how they are retained, who can access them and whether the arrangement protects client information. | Putting medical records, identifying details or privileged strategy into an unsuitable service. |
| Communication | Explain material AI-assisted work when consultation is needed and answer reasonable client questions about how the matter is handled. | Allowing a client to assume a machine made a reliable legal or settlement decision. |
| Supervision | Supervise employees, vendors and tools used in legal work; set review procedures and document approvals. | Treating an automated output as self-authenticating or allowing untrained staff to send it. |
| Meritorious claims and candor to the tribunal | Check every factual assertion, authority, quotation and calculation before filing or presenting it. | Submitting an invented case, altered quotation or unsupported damages figure. |
| Reasonable fees | Assess whether AI-assisted work is billed fairly and explain billing practices when the client needs that information. | Charging for time not actually spent or passing through an unexplained technology cost. |
The opinion interprets the ABA Model Rules as guidance. The controlling obligations come from the rules, court orders and professional requirements in the jurisdiction handling the matter.
A defensible human-in-the-loop workflow
- Define the task. Decide whether AI is being used for indexing, a draft, a search, a classification or another limited purpose. Do not start with an open-ended request to “decide the case.”
- Classify the information. Separate public material from privileged communications, medical information, financial data, identifying information and work product. Confirm the tool’s retention, access and training settings before uploading anything.
- Prepare reliable inputs. Preserve the original files, note missing records and use consistent identifiers. Record the date and version of the data used so a later reviewer can reproduce the step.
- Generate an output with boundaries. Ask for a summary, list of documents or draft with instructions to distinguish uncertainty and cite the supplied record. Do not request unsupported legal conclusions or a guaranteed settlement number.
- Check against primary sources. Compare every material fact with the original record; verify legal authorities in the official source; recalculate money figures independently; and have a qualified reviewer examine the result.
- Resolve conflicts. If the model’s output conflicts with a medical record, photograph, witness statement or accounting, preserve the conflict and investigate it. Do not select the version that makes the case look stronger merely because it is convenient.
- Approve and document. Identify who reviewed the output, what was corrected, what data was used and why the final work product is reasonable. Keep the audit information consistent with confidentiality obligations.
- Communicate when material. Tell the client what they need to know about the role of AI in their matter, especially when it affects advice, a significant decision, confidentiality or fees.
How to compare an assisted workflow with an insurer’s system
There is no supported product ranking or performance benchmark for personal-injury AI in the available authorities. A meaningful comparison instead asks these questions:
| Comparison axis | Law-firm document workflow | Insurer claims workflow |
|---|---|---|
| Task | Review, organization, issue spotting or drafting. | Accident-image analysis, claims triage or a settlement-value estimate. |
| Decision stakes | Often clerical at first, but a draft can affect advice, pleadings or negotiation. | A recommendation can influence investigation, reserves or an offer to a claimant. |
| Human review | Counsel checks records, law, calculations and language before use. | The carrier should identify who reviews the output and how exceptions are handled. |
| Data handling | Client confidentiality, privilege, medical records and work product require controls. | Claimant information and images require security, access and retention controls. |
| Accountability and recourse | The responsible lawyer remains answerable for the work and court submissions. | Insurance laws and consumer protections govern the carrier’s process; the claimant needs a way to question an adverse result. |
| Jurisdiction | Professional rules and court requirements vary by matter and location. | Insurance regulation varies by state and applicable line of business. |
Risks, warning signs and safeguards
Inaccuracy and invented content
Generative systems can produce plausible but false facts, citations or quotations. Warning signs include a summary with no page references, a legal proposition that cannot be found in the cited authority, and a damages calculation with unexplained assumptions. Require source links or page numbers inside the working file, then verify them manually.
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Bias and unfair discrimination
A model trained on historical claims or settlement data may reproduce patterns that disadvantage certain people or conditions. Pennsylvania’s 2024 notice identifies unfair discrimination as a possible risk. A review should test whether similarly situated claims are treated differently and whether a factor has a legitimate, documented relationship to the decision.
Data vulnerability and confidentiality loss
Uploading a full medical file to a public or poorly configured service can expose sensitive information. Use data minimization, access controls, encryption and retention limits appropriate to the matter. Redaction or de-identification can reduce exposure, but it must not remove context needed for accurate review.
Lack of transparency
If a claimant receives an offer influenced by an automated process, an unexplained number is not a substitute for a reasoned evaluation. Ask what inputs mattered, whether a human reviewed the result, how errors are corrected and what appeal or escalation path exists. A carrier’s internal model is not automatically a public standard for damages.
Automation bias and overreliance
People may defer to a machine because its output is fast, numerical or confidently written. Require the reviewer to state the independent factual and legal basis for the conclusion, not merely approve the software’s recommendation. For high-stakes decisions, use a second qualified review and preserve disagreements for escalation.
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What an injured person should ask
A claimant does not need to understand a model’s code to seek a fair process. Practical questions include:
- Was an automated tool used to review photographs, medical records or settlement information in my claim?
- What information did it consider, and what information was unavailable or excluded?
- Did a trained person review the output before the decision or offer?
- How can I correct an inaccurate record or challenge an unexplained conclusion?
- Are future treatment, lost earnings, pain, pre-existing conditions and causation being evaluated by a person with the necessary expertise?
The answers may depend on the carrier, state and stage of the claim. A personal-injury lawyer can request supporting records, analyze the insurer’s explanation and determine whether further evidence or litigation steps are appropriate.
What regulators currently establish
The NAIC’s insurance guidance places AI-supported insurance activity inside existing insurance and consumer-protection requirements. It describes governance, risk management and oversight as important controls, but it does not identify one national personal-injury algorithm or prescribe one valuation method.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchPennsylvania Notice 2024-04 is a state example rather than a nationwide mandate. Its focus on accuracy, discrimination, data vulnerability, transparency and governance shows the kinds of issues a state regulator may examine. A lawyer or insurer must check the current rules and notices in the jurisdiction involved, along with any court-specific requirements.
Limits of what is known
The available primary materials establish categories of use and professional responsibilities, not a measured personal-injury accuracy rate, adoption percentage, average savings or improvement in case outcomes. Broad insurance-sector descriptions should not be converted into statistics about personal-injury claims. Likewise, a general ABA example of document drafting does not prove that a particular firm has adopted it or that it improves a particular result.
That evidence boundary matters when evaluating a vendor, a law-firm policy or an insurer’s explanation. Ask for task-specific validation, security terms, documented oversight and an error-correction process rather than assuming that an AI label guarantees quality.
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