Medical AI · How it works

From raw medical records to structured output — in under an hour.

Whether you need a structured medical summary or a fully indexed and paginated bundle with chronology, the same medico-legal AI handles both. Here's exactly how.

No subscription · £3,000 free credit · Pay only for what you use
Upload Drop in PDFs — any size, any provider
Pick output Summary, indexed bundle, or both
Under 1 hour Structured output ready to review
UK GDPR · SOC 2 Records encrypted at rest
Three steps, one platform

The same flow, whether you need a summary or a full bundle.

Drop in records, pick the output you want, review the result — all inside the FinLegal platform.

№ 01

Upload your records

Drop in PDFs of medical records — a few dozen to several thousand pages. Mixed providers, mixed order, duplicates included. We handle the mess.

№ 02

Pick your output

Medical summary for structured case preparation, index, paginate & chronology for indexed, paginated bundles ready for case preparation, or both. Confirm the fee, we get to work.

№ 03

Review and download

Within an hour, structured output lands in the platform — ready to review, edit in-app, and download as Word or PDF.

Output 01 · Medical summaries

Everything you need from a medical summary for personal injury.

Each summary is structured into clear, reviewable sections — ready to use immediately in case preparation and settlement negotiations.

Patient Details

Personal and demographic information, contact details, NHS or reference numbers.

Incident Confirmation

Verified incident details matched against the records — date, mechanism of injury, initial presentation.

Medical Presentations

Tabulated record of every GP visit, hospital attendance, and specialist consultation — chronologically ordered.

Pre-existing Conditions

Identified prior conditions, medications, and relevant medical history that may affect the claim.

Key Symptoms Timeline

Chronological timeline of symptoms from onset through treatment, recovery or ongoing conditions.

Diagnosis & Treatment

Diagnosis summary with treatment undertaken, medications prescribed, specialist referrals.

Comprehensive Citations

Every finding in the summary is cited back to the underlying medical records, with page-level references for auditability.

Follow-up

Outstanding referrals, recommended follow-up appointments, and ongoing treatment plans.

Unique to FinLegal

Comprehensive citations.

The AI doesn't just summarise records — every finding is cited back to the underlying medical evidence with page-level references. Reviewers can verify each statement against the source in seconds, and the output stays auditable end-to-end.

Page citations Source-linked Auditable
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Medical summary · J. Bloggs
Diagnosisp. 1247
Mechanismp. 34
Pre-existing conditionp. 892
Treatmentp. 1100
Prognosisp. 1480
Follow-upp. 1612
Supported claim types

Optimised for the most common PI claim types.

Optimised for the most common personal injury claim types, with more being added every quarter.

Road Traffic Accidents

Whiplash, soft tissue, fractures, multi-injury RTA claims.

Slip & Trip

Public liability and occupier's liability claims.

Employer's Liability

Workplace injuries, industrial disease, occupational health.

Output 02 · Index, paginate & chronology

From raw records to indexed, paginated bundles.

Medical records arrive as unstructured PDFs — often hundreds or thousands of pages from multiple providers, mixed up, duplicated, and out of order. We fix that.

Indexing

Every document is identified, classified by type (GP records, hospital notes, specialist letters, test results), and assigned to the correct medical provider.

Pagination

Pages numbered sequentially, deduplicated, and ordered correctly. Boundaries between documents from different providers are clearly marked.

Chronology

A complete timeline of medical events — consultations, treatments, prescriptions, referrals — extracted and ordered by date.

Multi-provider

Records from GPs, hospitals, specialists, physiotherapists, and other providers are separated, identified, and grouped correctly.

Transparent AI

Confidence scoring on every indexing & pagination decision.

For indexed, paginated bundles, every classification decision — document boundaries, dates, document type — comes with a transparent confidence score. Rather than blindly trusting AI output, reviewers can instantly see which sections need human attention and which can be trusted.

Boundary detection Date recognition Document classification
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Confidence scores · Bundle 4,820pp
GP consultation notesHigh
Hospital dischargeHigh
Specialist referralMed
Physiotherapy notesHigh
Handwritten notesLow
Test results · bloodsHigh
Why confidence scoring matters

Faster review, better defensibility.

Auditability

Every AI decision has a visible confidence level. No black box.

Focused review

Human reviewers only check medium and low-confidence items. High-confidence results can be trusted.

Defensibility

Demonstrate exactly how records were classified and what level of human oversight applied.

Throughput

Process more cases in less time by focusing human effort where it matters.

The difference

Days became hours. Hundreds became tens.

What used to take days and cost hundreds of pounds now takes under an hour.

Without FinLegal

3–5 days turnaround · £200–£800 per case · manual sorting and pagination · no confidence indicators · human error in classification · inconsistent formatting.

With FinLegal

Under 1 hour turnaround · from £25 per medical summary, from £150 per indexed & paginated bundle · automatic indexing and pagination · confidence scores on every item · consistent AI classification · standardised, professional output.

Transparent pricing

Pay per output. No subscription.

Top up your credit and go. Start with £3,000 free credit.

Medical summary From £25
Indexed & paginated bundle From £150

Ready to try it?

Process your first cases with £3,000 in free credit. No commitment, no subscription — see the quality before you spend a penny.

SOC 2 compliant UK GDPR compliant Data encrypted at rest Fine-grained user management

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