Medical report
Produce in minutes a structured medical report from raw notes or consultation transcript.
Medical report writing (consultation, hospitalization, examination, peer correspondence) represents 1 to 2 hours daily for an active doctor. Generative AI lets you drop to 10-20 minutes, freeing considerable time for clinical practice. The challenge: absolute confidentiality (medical secrecy, GDPR, HDS) prohibits using public LLMs on identifying patient data. This guide presents secure workflows and tools suitable for the medical world.
Step-by-step workflow
Choose a compliant solution
Never public ChatGPT/Claude on identifying patient data. Solutions: ChatGPT Enterprise, Claude for Work, or ideally dedicated medical solutions with HDS hosting (Doctolib AI, Nabla, Posos).
Capture structured data
Either voice dictation in consultation, or raw notes after. The richer the raw material, the better the generated report.
Generate the structured report
Request standard medical format: motive, history, clinical exam, additional exams, diagnosis, action to take. Adapted to consultation type.
Verify critical elements
Every clinical information generated must be validated: dosages, contraindications, diagnostic codes, references to guidelines. AI can hallucinate on medical figures.
Customize and sign
Doctor adds clinical nuances AI can't guess (patient feeling, family context, personalized therapeutic choices), validates and signs. It's the final report engaging their responsibility.
Copyable prompts
2 tested and optimized prompts. Adapt the bracketed variables [VARIABLE] to your context.
Consultation report
You are a doctor [SPECIALTY]. Here are raw consultation notes: [PSEUDONYMIZED NOTES] **Consultation type**: [FIRST / FOLLOW-UP / EMERGENCY] **Recipient**: [PATIENT / GP / SPECIALIST] Produce a structured report: 1. **Consultation motive** 2. **History**: medical, surgical, family, current treatments 3. **Disease history** 4. **Clinical exam**: structured by system 5. **Additional exams**: prescribed, upcoming, prior 6. **Diagnosis** or diagnostic hypotheses 7. **Action to take**: treatment, monitoring, next consultation 8. **Recommendations to patient** Respect precise medical terminology. Keep factual tone. If critical information missing in notes, signal explicitly with [MISSING INFO TO COMPLETE].
Patient information sheet
From this medical report: [REPORT] Produce a patient information sheet in simple language: - What happened in consultation (clearly)? - What's the diagnosis (explained with analogy if relevant)? - What are treatments and why - What signs require urgent re-consultation - What appointments or exams to schedule - Answers to 3 most likely patient questions Language: middle school level, reassuring professional tone, airy format. 1 page max.
Top tools for this use case
Curated selection of the 3 best AI tools for medical report.
Why for this use case: Advanced reasoning on complex cases. Lower clinical hallucinations than generalist competitors.
Why for this use case: Excellence on structured English writing, precise medical terminology, tolerance to messy notes.
Why for this use case: Unbeatable to verify scientific references of a clinical decision with peer-reviewed sources.
Estimated ROI
Time saved
60-70% on writing (5-10 min vs 15-30 min per report)
Quality gain
Structured and complete reports, standardized terminology
Stack cost
$30-100/month depending on chosen HDS-compliant solution
Estimates based on 2026 benchmarks and user feedback. Actual ROI depends on your context.
Frequently asked questions
Can you dictate to ChatGPT during a consultation?
Not with public version. Patient data must never transit a non-HDS service. Solutions: Doctolib AI, Nabla, Posos (all HDS). For Claude/ChatGPT, only in DPO-validated enterprise mode after pseudonymization.
Can AI be wrong on dosages?
Yes, and dangerous. Any prescription, dose, frequency proposed by AI must be validated by official drug references before prescribing. Consider AI as writing draft, never as pharmacological source.
What impact on patient relationship?
If well integrated (discreet dictation, no screen facing patient during consultation): positive (more presence time as less note-taking). If poorly integrated: negative (AI captures attention to patient's detriment). Prefer audio recording + post-consultation processing.
How to train doctors on AI?
Three axes: (1) technical use (prompting, verification, business software integration), (2) ethics and GDPR (patient consent, retention, transparency), (3) critical thinking (knowing how to detect hallucinations, never delegate medical decision). Continuous training essential.