Medical ReportSummarization
Concise, source-linked summaries of long medical records for clinicians to review, with strict privacy controls and no automated diagnosis.
Medical Report Summarization: what the work involves
Clinicians spend large parts of their day reading: referral letters, lab reports from several laboratories, discharge summaries, scanned prescriptions, prior consultation notes. Before an appointment, a doctor skims pages to find what matters, and patients carrying folders of paper reports from different hospitals leave important history unread. Time pressure leads to missed details, and administrative reading eats into patient time.
We extract text from PDFs and scans with layout-aware OCR, then normalise lab values, units and dates. A language model produces a structured summary with sections for history, medications, abnormal results and follow-up items, and every statement carries a pointer to the page and line it came from. Abnormal flags come from reference ranges in your lab data, not from the model's opinion. The output never states a diagnosis or treatment recommendation. A clinician reads and signs off every summary before it enters a record. We prefer deployments where patient data stays within your network or a compliant region, using self-hosted models when policy requires it.
Core features
Scan and PDF reading
Paper reports and scanned documents are converted to text and structured tables, with low-quality pages flagged.
Structured summary template
Output follows sections your clinicians approve, such as history, medications, abnormal findings and follow-up.
Source pointers
Each statement links to the page and line it came from, so a clinician can verify in one click.
Lab value normalisation
Units, ranges and dates from different labs are standardised, with abnormal flags taken from reference ranges.
Clinician sign-off
Nothing enters the patient record until a clinician reviews and approves, with edits tracked.
Record system connection
Approved summaries are pushed into your EMR or hospital system through its API or standard formats.
What we get right before launch
Patient data protection
Medical records are highly sensitive and regulated, with rules differing between Pakistan, Australia and the United States. We use minimal data, encryption, access logging, and self-hosted models where outside processing is unacceptable.
Omission and hallucination
A summary can drop a critical detail or state something the report never said. We require source links, run checks against the original text, and design the process so clinicians always review.
Not a diagnostic device
Software that diagnoses or recommends treatment may be regulated as a medical device. We keep the scope to summarising existing content and advise you to confirm regulatory status for your market.
Tools and technology
- Anthropic Claude
- OpenAI
- Llama
- Google Document AI
- Tesseract
- Python
- FastAPI
- PostgreSQL
Common questions, answered
Can the AI diagnose patients from reports?
No, and we design it not to. It summarises what the documents already state and cites sources. Diagnosis and treatment decisions remain with the clinician, and the interface says clearly that it is a drafting aid.
Is patient data safe with AI models?
It can be, with the right design: encryption, minimal data, de-identification where possible, contracts that prevent training on your data, or a self-hosted model. We document the data flow for your compliance lead.
How do you handle errors in summaries?
Every statement links to its source, summaries are tested on sample records before launch, and clinicians approve each one. Errors found in review are logged and used to improve prompts and checks.
Does it work with Urdu or handwritten notes?
Printed English and Urdu are workable with good scans. Handwriting, especially doctors' handwriting, is unreliable, so we flag it for manual reading instead of guessing.
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Ready to start your Medical Report Summarization project?
Tell us what you need and we will come back with a clear scope, timeline and the questions worth answering before any build starts.