Claim by claim
The opinion is broken down into individual statements. Each is analysed separately, so nothing gets lost in a long document or in the dominant narrative.
Controlled AI for analysing medical expert opinions
AR3S breaks the opinion down into individual claims and tests each one separately: is it supported by the records, does the conclusion actually follow from the facts, and which equally defensible interpretations did the expert leave out? The result is a structured challenge map — prioritised and linked to sources.
Fictional case and expert opinion. Explore the analysis and a saved strategy session. No new AI responses are generated.
The weakest points of an expert opinion are rarely obvious mistakes. More often, it is a conclusion that does not quite follow from the facts, or an interpretation with an equally defensible alternative.
These points are easy to miss — and they are the ones that most often ground effective objections. AR3S is built to find them systematically, not just to hunt for errors.
Expert statement
After twelve months, the treatment result can be considered essentially stabilised and the chance of further material improvement limited.
Why it is contestable
Twelve months is a clinical rule of thumb, not an individual prognostic threshold. Gaps in rehabilitation and unused treatment options may point elsewhere.
Possible move: request the basis for the cut-off and address the claimant’s individual recovery dynamics.
Example prepared for demonstration purposes — not taken from a real case.
Why is ChatGPT not enough?
Clients can already upload an expert opinion to ChatGPT and arrive with a list of objections. That list mixes valid points with weak ones — and often misses important challenge paths altogether.
Which objections are valid?
Which are weak or misleading?
What is still missing?
What actually matters for the case?
AR3S answers these questions claim by claim — so it is clear what the lawyer’s judgement adds — and what the client is paying for.
General-purpose models keep getting better at spotting obvious mistakes. AR3S goes further: it shows which conclusions can be challenged, where the reasoning is weak and what credible alternatives exist.
The opinion is broken down into individual statements. Each is analysed separately, so nothing gets lost in a long document or in the dominant narrative.
AR3S separates what follows from the medical records from the expert’s own inferences — and checks whether each inference is supported by the material.
For contested conclusions, the system identifies equally defensible alternatives, with reasoning and an assessment of their strength — even where the expert’s conclusion may still be valid.
Findings are ranked by strength and relevance to the case, with links to sources. The litigation-strategy module is currently being developed and tested.
Controlled, multi-stage AI analysis. AR3S runs the analysis through separated stages, each responsible for a defined part of the assessment. Their outputs are then cross-checked, so the final result can be traced and reviewed.
Product detailsA neurologist and court-appointed expert designs the analytical logic. An engineer with production experience (Opera, an AI MedTech project) builds the system and its infrastructure.
Meet the teamThe build log covers product decisions, practical lessons and problems discovered while building AR3S without disclosing the system architecture.
Read the build logPilots · Partnerships · Investment
AR3S is preparing for pilots with law firms. We welcome conversations with lawyers, legal teams and investors.
contact@ar3s.tech