Behind every personal injury claim is a mountain of paper. Medical records, bills, police reports, insurance letters, witness statements and photographs all have to be gathered, read and organised before anyone can say what a case is worth. That is precisely the sort of work that generative AI is being pitched at. The promise is real, but so are the risks, and both deserve a plain explanation.
Why this area of law fits so well
Personal injury work is document-heavy and repetitive. Lawyers and their staff spend many hours reading records, building timelines of treatment and summarising what happened. Much of it is routine but important, and a missed detail can change a claim.
Generative AI is good at turning large amounts of text into structured summaries. In principle, that means a first draft of a medical chronology, or a quick overview of a long set of records, could be ready in minutes rather than days.
Firms also receive plenty of repetitive correspondence. Standard letters, intake questionnaires and status updates for clients follow patterns that software can imitate.
Insurance companies are on the other side of the table and use their own software to evaluate claims. Some claimants’ firms see AI as a way to keep pace. Whether or not that is the case, it shows that technology is already part of how these disputes are handled, and ignoring it is not a neutral choice.
What the tools typically do
Common uses of generative AI include summarising records, pulling out dates and treatments, flagging gaps in care, drafting demand letters for a lawyer to review and answering routine client questions. Some products search through a firm’s own files and point to the relevant page.
Others help with intake, asking a new client structured questions and writing up the answers. That can speed up the first conversation, though a human should still review what has been collected.
These tools can free up time for the parts of the work that need judgment: advising clients, negotiating and appearing before a court.
Consider a typical case with several hundred pages of treatment notes. A tool might produce a timeline that shows when symptoms first appeared, which providers saw the patient and where there is a gap between treatments. A human reviewer can then check these items against the originals and use them to prepare a clear argument.
Training matters as well. Staff should know what a tool can and cannot do, what must never be pasted into it and who to ask when something looks wrong. A short written policy, updated as products change, is cheap protection.
Generative AI: the risks that matter
The best-known problem is invented information. Language models can produce confident but false statements, including fake citations to cases that do not exist. Courts have already shown little patience for lawyers who submitted unchecked output. Every fact and every reference must be verified.
Confidentiality is another issue with generative AI. Medical and financial records are among the most sensitive data there is. Firms need to know where the data goes, who can access it, whether it is used to train models and how it is deleted. Consumer chatbots are rarely the right place for client files.
Bias and gaps also matter. A summary might miss a subtle detail, misread handwriting or ignore context. A tool that works on tidy records may struggle with scans of poor quality.
Finally, there are professional rules. Lawyers have duties of competence and supervision, and those apply to the technology they use as much as to the staff they employ.
There is also the risk of over-trust. When a summary reads smoothly, people stop checking it. The more polished the writing, the more tempting it is to accept. Firms can reduce this by requiring links back to source pages and by sampling outputs for errors on a regular basis.
Another practical safeguard is keeping a record of how each document was prepared, including which tool was used and who reviewed it. If a question arises later, that trail shows the work was supervised and lets the firm correct problems quickly.
The human role stays central
Good practice treats generative AI output as a draft from a junior assistant: useful, fast, and in need of checking. A qualified person reads the underlying documents, confirms key facts and makes the decisions.
Clients may notice faster updates and clearer explanations, and perhaps lower costs over time. They may also want to ask how their information is handled and whether a person reviewed the work.
When choosing a vendor, firms should ask about data handling, accuracy testing, audit trails, the ability to see the source for each statement, and what happens if the vendor changes its terms. Pilot the tool on non-sensitive work first.
Used with care, generative AI can reduce drudgery. Used carelessly, it can damage a case. The difference lies in oversight, not in the software.
If you want to read around the subject, the Wikipedia article on generative artificial intelligence gives a plain overview, and two related pieces on this site are Five Questions to Ask Before You Trust an AI Assistant With Your Data and Picking a Podcast Host: What to Check Before You Upload Episode One.
Smaller firms may benefit most from generative AI, since they often lack large support teams. That makes careful choice of tools even more important, because they have less capacity to catch mistakes.
