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AI is becoming a support tool in personal-injury work, not an autonomous decision-maker. A law office may use it to sort medical records, search discovery, build a chronology, or prepare a draft for attorney review. An insurer may use systems that analyze accident images or estimate an ultimate claim settlement value. Those functions can reduce clerical effort, but they do not establish whether a claimant is legally entitled to compensation or what a particular case is worth.
The lawyer remains accountable for the advice, filings, calculations, confidentiality protections, client communication, and strategic choices. Insurers remain subject to insurance and consumer-protection requirements when they use automated systems. The practical question is therefore not whether a case is “handled by AI,” but which task is being assisted, what data enters the system, who verifies the output, and how a person can challenge an error.
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What AI can—and cannot—decide in a personal-injury case
Artificial-intelligence tools identify patterns in text, images, and structured data. In a personal-injury matter, that can mean finding repeated medical terms across thousands of pages, grouping documents by date, extracting names and invoice amounts, or producing a first draft of a letter. These are assistance functions. They do not give the software legal authority.
The American Bar Association’s Formal Opinion 512, issued July 29, 2024, discusses technology-assisted review in electronic discovery and generative-AI drafting of letters, contracts, briefs, and other legal documents. The opinion does not report a personal-injury adoption rate or show that AI improves case outcomes. Applying those general examples to injury work is a practical inference, so every result still needs a lawyer’s examination against the original records and the governing law.
- AI may help with: locating information, organizing files, summarizing records, spotting apparent inconsistencies, and creating a draft for review.
- AI does not, by itself, establish: negligence, causation, liability, damages, a claimant’s legal entitlement, or a fair settlement value.
- The controlling standard: a licensed professional must make and own the legal judgment, with the rules and court requirements of the relevant jurisdiction.
Law-firm uses that can reduce document work
Electronic discovery and file review
Personal-injury files can contain incident reports, photographs, emergency records, diagnostic images, therapy notes, bills, wage documents, text messages, and insurance correspondence. Technology-assisted review can classify or prioritize those materials so a lawyer or paralegal can examine likely relevant items first. A sensible use is to ask a system to identify documents mentioning a body part, treatment date, prior injury, or disputed event, then verify each hit in the source file.
Search results are not proof. Optical-character-recognition errors, inconsistent names, duplicate records, and missing pages can produce false positives or false negatives. The review protocol should preserve the original documents, record the search terms and date, and include a quality check using a sample of both included and excluded documents.
Medical-record summaries and chronologies
A tool can turn a stack of records into a proposed timeline of the accident, symptoms, examinations, treatment, work restrictions, and charges. That draft may reveal a date gap or a contradiction worth investigating. It should never replace checking the actual note, because a generated summary can omit a qualifier such as “patient reports,” confuse a planned procedure with one that occurred, or merge two people with similar names.
Keep the summary linked to page numbers or document identifiers. When a fact matters to causation or damages, the lawyer should read the underlying record and, where necessary, obtain clarification from a provider or expert.
Drafting correspondence and pleadings
Generative AI can produce a starting draft for a demand letter, discovery request, client update, or brief. The ABA’s opinion treats drafting as an assistance use, not as permission to file unverified text. Counsel must confirm every factual assertion, quotation, citation, deadline, and requested remedy. A polished paragraph that cites a nonexistent case or invents a medical finding is still a professional error.
Case organization and issue spotting
When used inside an approved case-management environment, automation can tag documents, identify unanswered discovery requests, or flag that a bill has no matching treatment entry. These are administrative aids inferred from the document-review and drafting uses described by the ABA. The firm should define what the tool is allowed to do and what requires a person’s sign-off; a convenience feature should not silently make a litigation decision.
A review model for lawyer-side AI work
| Task | Possible AI assistance | Human verification before use | Typical failure to watch for |
|---|---|---|---|
| Record review | Classify, search, and summarize documents | Compare findings with original pages and check a sample of excluded files | Missed records, duplicated entries, or altered context |
| Chronology | Extract dates, providers, symptoms, and treatment | Confirm each material entry against the source record | Wrong date, merged patient, or omitted qualifier |
| Demand or brief draft | Suggest structure and language | Validate facts, law, citations, calculations, and filing rules | Hallucinated authority or unsupported damages statement |
| Discovery administration | Flag missing responses or recurring issues | Apply the case-specific order, agreement, and deadline | False alert or missed exception |
How insurers may use AI in claims
The National Association of Insurance Commissioners (NAIC) says insurers use AI in claims for functions including accident-image analysis and estimating ultimate claim settlement values. That establishes that automated systems may be part of the claims environment; it does not mean every carrier uses them, that every injury claim is processed by AI, or that an estimate is fair or accurate.
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Image models may identify visible vehicle damage, roadway features, or other patterns that help route a claim or estimate repair-related information. A photograph cannot reliably establish every injury, the force experienced by a person, or legal fault. Lighting, camera angle, concealed damage, and incomplete images can all distort an output. Ask what evidence was considered and whether a human adjuster reviewed the result.
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Settlement-value estimates
An insurer may use historical and claim data to estimate an ultimate settlement value or prioritize files. An estimate is a business or workflow input, not a legal ruling and not a guaranteed offer. It may omit facts that are not yet documented, such as continuing symptoms, future treatment, lost earning capacity, or a disputed liability theory. A claimant can provide additional records and challenge the factual assumptions behind an offer.
What an automated claim decision means for a claimant
There is no universal requirement that a claimant accept an unexplained algorithmic result. The available regulatory material instead emphasizes compliance with insurance and consumer-protection law. Ask the carrier for the basis of a decision, the documents it relied on, and the process for human reconsideration or appeal. Keep copies of submissions and note dates, names, and stated reasons.
The professional duties that remain with the lawyer
Formal Opinion 512 applies the ABA Model Rules to generative-AI use. The controlling rules may differ by state, and courts can impose additional requirements, but the core responsibilities are practical and immediate.
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Counsel should understand what a tool can do, its known limitations, and the settings that affect retention or sharing. A lawyer must examine output before relying on it and supervise staff, contract lawyers, and vendors who use it. Delegating a task to software does not delegate the duty to check the result.
Confidentiality and data handling
Client information can include medical history, Social Security information, employment records, and litigation strategy. Before entering it into a service, determine where data is stored, whether it is used to train a model, who can access it, how long it is retained, and how deletion works. Use the firm’s approved environment and access controls. If the risk cannot be understood or controlled, do not upload the information.
Communication and informed decisions
Clients may reasonably ask whether AI is being used, what role it plays, and whether their information is sent to a third party. The lawyer should explain material risks and alternatives when the use affects the representation. A client update should distinguish a verified fact from an automated summary or prediction.
Candor, meritorious claims, and filing accuracy
Every submission to a court must be grounded in a reasonable factual and legal investigation. AI-generated citations, quotations, procedural statements, or expert descriptions require independent verification. Counsel must not present a convenient but unsupported argument, and must correct an error discovered after filing under the applicable rules.
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The ABA guidance also addresses fees. A firm should be able to explain what work was actually performed and why a charge is reasonable when automation reduces the time required for a task. Clients should not be billed as if a lawyer performed hours of work that the firm did not perform, subject to the governing engagement agreement and local rules.
Best Value
A defensible workflow from intake through settlement or trial
- Define the task. State whether the tool is searching records, extracting fields, drafting language, or assisting an insurer’s claim process. Do not begin with a vague instruction to “decide the case.”
- Check authority and security. Confirm the jurisdiction’s professional rules and court orders. Review the product’s retention, training, access, and deletion terms before entering confidential data.
- Prepare clean inputs. Separate clients, witnesses, and claimants; preserve originals; label versions; and remove irrelevant personal information where feasible.
- Run a bounded operation. Use defined date ranges, source collections, and prompts. Save the prompt or query, model version if available, and output with the case record.
- Verify against primary material. Read the underlying medical note, photograph, invoice, message, or legal authority for every fact that could affect liability, causation, damages, a deadline, or a court filing.
- Obtain a second review for high-stakes work. Have another qualified lawyer or experienced reviewer check calculations, citations, contradictions, and missing evidence before a demand, motion, expert disclosure, or settlement recommendation is sent.
- Document the decision. Record who reviewed the output, what corrections were made, what assumptions remain disputed, and how a client or claimant can challenge the result.
Compare workflows by risk, not by marketing claims
No competing products or validated performance scores are established for this topic. A useful comparison asks the same questions of a law-firm tool and an insurer system:
| Comparison axis | Lower-stakes assistance | Higher-stakes decision support |
|---|---|---|
| Task | Sorting files or suggesting a document outline | Recommending a settlement value or framing liability |
| Human review | Reviewer checks a sample and final text | Qualified decision-maker checks records, law, calculations, and assumptions |
| Data handling | Limited, de-identified material in an approved system | Highly sensitive medical or financial data requiring documented controls |
| Accountability and recourse | Internal correction before use | Clear explanation, human reconsideration, complaint, or appeal path |
| Jurisdiction | Firm policies and applicable professional rules | Those rules plus insurance and consumer-protection requirements |
Common failure modes and practical fixes
- Fabricated authority or facts: require a source citation for each material assertion and open the original authority or record before filing.
- Confidently wrong medical summaries: compare the output line by line with the provider’s note and ask a medically qualified expert when interpretation matters.
- Hidden bias or inconsistent treatment: test outputs across comparable claims, investigate unexplained differences, and apply anti-discrimination and fair-claims rules.
- Data leakage: prohibit consumer-grade accounts for confidential files, restrict access, and review retention and deletion settings.
- Automation bias: make the reviewer write the factual basis for a decision instead of merely approving a score or recommendation.
- Unclear responsibility: assign a named lawyer or claims professional who owns the final decision and the correction process.
What Pennsylvania’s notice illustrates about insurer oversight
The Pennsylvania Insurance Department’s Notice 2024-04, published April 6, 2024, identifies possible risks from insurer AI systems, including inaccuracy, unfair discrimination, data vulnerability, and lack of transparency. It describes governance, risk-management, and oversight expectations. This is Pennsylvania guidance, not a nationwide rule. Other jurisdictions may use different requirements, so a lawyer or claimant should check the state that regulates the policy and the court handling the case.
Questions an injured person can ask
- Is AI being used to review my records, evaluate my claim, or draft communications?
- Which facts and documents did the system consider, and what did a human reviewer verify?
- How is my medical and personal information stored, retained, and shared?
- If an automated estimate is wrong, who can reconsider it and what records should I provide?
- Will any AI-generated text or citation be checked by a lawyer before it is sent to me, an insurer, or a court?
- How does the fee agreement account for work performed with automation?
The evidence limit readers should keep in mind
The cited professional and regulatory materials describe capabilities, duties, and oversight. They do not provide a personal-injury-specific statistic for adoption, accuracy, settlement improvement, or courtroom success. Treat claims of guaranteed valuation, faster wins, or objective AI fairness skeptically unless the provider can identify a transparent method, an appropriate dataset, and independent validation for the exact use.
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The practical rule
Use AI to make information easier to find and drafts easier to start. Do not let it become the unexamined source of a legal conclusion, medical fact, settlement number, or court statement. In personal-injury law, the safer division of labor is automation for organized assistance and accountable humans for judgment, explanation, and the final decision.
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