Breast Care A-Z · Concept · AISCRIBE

Medical AI scribe

also: AI medical scribe, clinical AI scribe, ambient AI scribe, Heidi, consultation AI scribe

A medical AI scribe is software that listens to a clinical consultation, transcribes what's said, and produces a structured clinic note for the clinician to review and sign off1. The clinician remains the author of the record2; the AI does the typing. At Breastory, the AI scribe used by Dr Tsang-Wright is Heidi4. You can opt out at the start of any appointment3. See [AI in your care](/about/ai-in-your-care/) for the practice's overall approach.

Quick answers

Is my consultation recorded?

Yes — audio is captured during the consultation, processed to generate the note, then deleted per Heidi's retention policy (currently 30 days)4. The structured clinic note that comes out of the process is the long-term record.

Can I opt out?

Yes, at any appointment. Tell Fiona at the start of the visit that you'd rather she takes notes by hand3. There is no need to justify it and it does not affect your care2.

Who can read the consultation transcript?

The same people who can read your other clinical notes — Fiona, Sarah for administrative work, your GP if a copy of the consultation letter is sent. Heidi's own staff do not routinely access individual consultation transcripts4.

TECHNOLOGY · CLINICAL AI medical AI scribe ambient AI documentation · clinical NLP · large language model scribe AI systems that listen to clinical consultations and automatically generate structured clinical notes, letters, and documentation — reducing administrative burden and improving note quality. PLATE LXXVIII FIG 01 — Clinical AI scribe workflow MD Pt “...presenting complaint... ...examination findings...” ▼ Live consultation (ambient AI) ◆ Large language model (LLM) speech-to-text + clinical NLP 📄 Clinic letter 📄 Medical notes 📄 Referral letter Auto-generated structured docs ✔ Clinician review + sign-off (mandatory) Callouts: i ambient microphone — passive listening during consult ii speech-to-text transcription (NLP) iii clinical entity extraction (diagnoses, drugs, plans) iv structured document generation v clinician review + approval (mandatory before sending) How it works Ambient mic captures consultation audio Speech-to-text model transcribes Clinical NLP identifies entities (diagnoses, medications, plans) LLM generates structured note Clinician reviews, edits, signs Current evidence and adoption Reduces documentation time by ~50% in early pilots Note quality equal or superior to manually dictated notes Not yet MHRA Class IIb governance varies by trust FIG 02 — Traditional dictation vs AI scribe Dimension Traditional dictation AI scribe Documentation time 30–60 min per clinic ~5–10 min review Method Manual dictation + typing Ambient capture + AI draft Accuracy Variable High (if reviewed) Clinician effort High Low (review only) Turnaround Hours to days Minutes Note completeness Variable Often more complete Patient interaction Disrupted (typing) Uninterrupted Cost Staff time Software subscription Regulatory status Standard Varies (MHRA evolving) Risk Human error in dictation AI hallucination (must review) FIG 03 — AI scribe applications in breast surgery Application Description Example Clinic letter generation Auto-draft from consultation Breast clinic follow-up MDT proforma Structured MDT output Cancer discussion documentation Consent documentation Draft consent from discussion Surgical consent Referral letters GP → specialist 2WW referral drafts Discharge summary Post-op documentation Day surgery discharge Operative notes Surgical note generation WLE / mastectomy notes Follow-up plans Surveillance plans Post-treatment plans Research summaries Patient-friendly summaries Trial recruitment letters Pre-op letters Patient preparation letters Pre-operative instructions FIG 04 — AI scribe implementation pathway 1 Clinician installs AI scribe system (Trust-approved) 2 Patient informed + consent for AI note-taking 3 Consultation proceeds (ambient recording) 4 AI generates draft documentation (clinic letter + notes) 5 Clinician reviews, edits, and approves 6 Letter sent / note filed FIG 05 — AI scribe system types Ambient consultation AI (Nuance DAX) Post-dictation AI (Dragon Medical) LLM-powered clinic letters MDT documentation AI AI-generated discharge summaries Clinician approval workflow FIG 06 — Key statistics ~50% reduction in documentation time in early pilots [1] Equal+ note quality vs manual dictation in studies [2] Burnout admin burden partly driven by EHR — AI aims to reduce [3] 2024–25 regulatory framework for clinical AI scribes evolving [4] FIG 07 — References 1. Knoll M et al. AI scribe documentation time. JAMIA 2023 2. Tierney AA et al. AI ambient documentation quality. NEJM Catalyst 2024 3. Shanafelt T et al. Clinician burnout and EHR. Mayo Clin Proc 2016 4. MHRA. AI as a medical device regulatory framework 2023 5. NHS England. AI implementation framework 2024 Clinically authored by Dr Fiona Tsang-Wright , FRCS (Gen Surg) GMC 4549831 · ORCID 0000-0003-4801-026X TECHNOLOGY · CLINICAL AI medical AI scribe ambient AI documentation · clinical NLP · large language model scribe AI systems that listen to clinical consultations and automatically generate structured clinical notes, letters, and documentation — reducing administrative burden and improving note quality. PLATE LXXVIII FIG 01 — Clinical AI scribe workflow MD Pt “...presenting complaint... ...examination findings...” ▼ Live consultation (ambient AI) ◆ Large language model (LLM) speech-to-text + clinical NLP 📄 Clinic letter 📄 Medical notes 📄 Referral letter Auto-generated structured docs ✔ Clinician review + sign-off (mandatory) Callouts: i ambient microphone — passive listening during consult ii speech-to-text transcription (NLP) iii clinical entity extraction (diagnoses, drugs, plans) iv structured document generation v clinician review + approval (mandatory before sending) How it works Ambient mic captures consultation audio Speech-to-text model transcribes Clinical NLP identifies entities (diagnoses, medications, plans) LLM generates structured note Clinician reviews, edits, signs Current evidence and adoption Reduces documentation time by ~50% in early pilots Note quality equal or superior to manually dictated notes Not yet MHRA Class IIb governance varies by trust FIG 02 — Traditional dictation vs AI scribe Dimension Traditional dictation AI scribe Documentation time 30–60 min per clinic ~5–10 min review Method Manual dictation + typing Ambient capture + AI draft Accuracy Variable High (if reviewed) Clinician effort High Low (review only) Turnaround Hours to days Minutes Note completeness Variable Often more complete Patient interaction Disrupted (typing) Uninterrupted Cost Staff time Software subscription Regulatory status Standard Varies (MHRA evolving) Risk Human error in dictation AI hallucination (must review) FIG 03 — AI scribe applications in breast surgery Application Description Example Clinic letter generation Auto-draft from consultation Breast clinic follow-up MDT proforma Structured MDT output Cancer discussion documentation Consent documentation Draft consent from discussion Surgical consent Referral letters GP → specialist 2WW referral drafts Discharge summary Post-op documentation Day surgery discharge Operative notes Surgical note generation WLE / mastectomy notes Follow-up plans Surveillance plans Post-treatment plans Research summaries Patient-friendly summaries Trial recruitment letters Pre-op letters Patient preparation letters Pre-operative instructions FIG 04 — AI scribe implementation pathway 1 Clinician installs AI scribe system (Trust-approved) 2 Patient informed + consent for AI note-taking 3 Consultation proceeds (ambient recording) 4 AI generates draft documentation (clinic letter + notes) 5 Clinician reviews, edits, and approves 6 Letter sent / note filed FIG 05 — AI scribe system types Ambient consultation AI (Nuance DAX) Post-dictation AI (Dragon Medical) LLM-powered clinic letters MDT documentation AI AI-generated discharge summaries Clinician approval workflow FIG 06 — Key statistics ~50% reduction in documentation time in early pilots [1] Equal+ note quality vs manual dictation in studies [2] Burnout admin burden partly driven by EHR — AI aims to reduce [3] 2024–25 regulatory framework for clinical AI scribes evolving [4] FIG 07 — References 1. Knoll M et al. AI scribe documentation time. JAMIA 2023 2. Tierney AA et al. AI ambient documentation quality. NEJM Catalyst 2024 3. Shanafelt T et al. Clinician burnout and EHR. Mayo Clin Proc 2016 4. MHRA. AI as a medical device regulatory framework 2023 5. NHS England. AI implementation framework 2024 Clinically authored by Dr Fiona Tsang-Wright, FRCS (Gen Surg) GMC 4549831 · ORCID 0000-0003-4801-026X
Visual Reference · Medical AI scribe
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Common questions The questions patients ask first

Is the conversation recorded?
Yes — audio is captured during the consultation, processed to generate the note, then deleted per Heidi's retention policy (currently 30 days)4. The structured note that comes out of the process is the long-term record.
Can I opt out?
Yes. Tell Fiona at the start of the appointment that you'd rather she takes notes by hand3. There's no need to justify it2.
Where is my consultation audio stored?
On Heidi's infrastructure — typically cloud servers in the UK or EU, operated under Heidi's data-handling policy4. Audio is deleted after the retention period; the structured clinical note lives in your record at the practice.
Who can read the consultation transcript?
The same people who can read your other clinical notes — Fiona, Sarah for administrative purposes, and your GP if a copy of the consultation letter is sent. Heidi's own staff do not routinely access individual transcripts4.
Is the AI making clinical decisions?
No. The AI drafts a note from the conversation. The clinical reasoning, the diagnosis, and the treatment plan are Fiona's2. She reviews and edits the note before signing it off; her clinical judgment is what determines what's in your record and what the next steps are12.
Is this the same as voice recognition (Dragon, etc.)?
No. Older voice-recognition tools transcribed verbatim what the clinician dictated. AI scribes listen to the two-way consultation between clinician and patient, then structure the conversation into a clinical note. Different technology, different output, different patient experience.
What if Heidi gets something wrong in the note?
Fiona reviews and corrects the note before signing it off. If she misses an error and you spot it later (for example, in your GP letter), tell Sarah and we'll correct the record.
Do other doctors use AI scribes too?
Yes — AI scribing is increasingly common across UK NHS and private practice in 2026. Most major hospital systems have either deployed or are evaluating AI scribe tools. The specific tool varies; Heidi is one of several leading options.

A medical AI scribe is a software tool that listens to a clinical consultation, transcribes what is said, and produces a structured clinic note1 — typically organised under standard clinical headings (history, examination, assessment, plan) — that the clinician then reviews, edits, and signs off. The clinician remains the author of the clinical record; the AI scribe does the typing. At Breastory, the AI scribe used by Dr Fiona Tsang-Wright is Heidi, a clinical-grade tool widely deployed across UK NHS and private practice in 2026.

The practical effect for a patient is more eye contact during the consultation, less time watching the consultant type. The conversation is the same; what changes is who handles the note-taking afterwards. The clinical note that lands in your record and goes to your GP is the consultant’s note — they review and edit what the AI produces before signing it off. You can opt out of AI scribing at the start of any appointment, and the consultant will take notes by hand for that visit. See AI in your care for the practice’s overall approach.

Related terms: AI in your care · AI-assisted radiology · Multidisciplinary team · Triple assessment

Definition What it is, and what it does

A medical AI scribe is a software application that:

  1. Listens to a consultation through a microphone (typically the consultant’s laptop or tablet in the consulting room).
  2. Transcribes the conversation in real time using speech-recognition technology trained on medical vocabulary.
  3. Generates a structured clinical note by analysing the transcript — usually organised under standard headings such as History of presenting complaint, Past medical history, Examination findings, Discussion, Plan — and putting the right pieces of the conversation under the right heading.
  4. Presents the draft note to the clinician at the end of the consultation (or shortly afterwards).
  5. Waits for the clinician to review, edit, and sign off the note before it becomes part of the patient’s clinical record.

The point of the tool is workflow time. Clinicians traditionally spent a significant proportion of every consultation typing notes, or wrote them up afterwards from memory or scribbled notes — neither of which produces an ideal record. An AI scribe shifts that burden to software, freeing the clinician to focus on the conversation in the moment and to review a structured draft afterwards.

It does not replace the clinician’s judgment. The note is the clinician’s note, and they are accountable for what it says — the AI is a drafting tool, not a colleague.

How it differs How it differs from older voice-recognition systems

Voice-recognition transcription has existed in clinical workflows since the 1990s — most consultants have, at some point, used Dragon Medical or a similar tool to dictate letters and notes. Those systems transcribed verbatim what the clinician dictated; they didn’t structure the content.

The current generation of AI scribes (introduced from around 2022 onwards, accelerated by large-language-model technology)4 does something different — it listens to the two-way conversation between clinician and patient, then structures what was said into a clinical note. This requires the AI to:

  • Distinguish clinician speech from patient speech.
  • Identify which parts of the conversation are relevant to which section of the note.
  • Generate clinical-quality language — appropriate medical terminology, correct grammar, a register consistent with how clinicians write.
  • Filter out irrelevant content (small talk, interruptions).

This is the technology shift that has made AI scribing widely useful — and widely deployed — over the last 2 to 3 years. Heidi4 is one of the leading UK and Australian-origin tools in this space, alongside others like Nabla, Suki, Abridge, and Augmedix.

At Breastory Heidi specifically — what’s used at Breastory

Heidi is a clinical-grade AI scribe used by Dr Fiona Tsang-Wright at consultations. Things worth knowing:

  • Designed for clinical use. Heidi is built specifically for healthcare consultations — its transcription, vocabulary, and output structures are clinical, not generic. It is registered with the MHRA as a Class I medical device for clinical summarisation.
  • UK and Commonwealth deployment. Heidi is used in UK NHS and private practice as well as in Australia, New Zealand, and the US4. The scale of deployment matters because it shapes the data-handling guarantees the company can reasonably make.
  • Transparent data handling. Heidi publishes its data-retention policy, its data-processing locations, and the specific UK GDPR-relevant guarantees34. The current published policy at the time of writing retains the audio for a defined period typically within 7 days (often deleted soon after the note is verified) before deletion.
  • Clinician remains the author. The note Heidi produces is a draft. Fiona reviews it, edits it for accuracy and clinical appropriateness, and signs it off. The note that lands in your record is hers, not the AI’s.
  • No autonomous decision-making. Heidi does not generate a diagnosis or a treatment plan. It produces the note that documents the consultation; the diagnosis and plan are the clinician’s, formed through the same clinical reasoning that has always applied.

Data handling What happens to the audio

This is the part patients most want to know, so it’s spelled out in detail.

  1. Audio is captured during the consultation through the device microphone in the consulting room.
  2. Audio is processed to generate the transcript and the structured clinical note. The processing happens on Heidi’s infrastructure — typically a cloud service operated within UK / EU data-protection jurisdiction.
  3. Transcript and note are returned to the consultant’s interface.
  4. Audio is retained for a defined period — typically within 7 days at the time of writing4 (often deleted once the verified note is signed off) — and then deleted by Heidi’s standard data-retention policy.
  5. Transcript and structured note live in your clinical record at the practice, with the same access controls as any other clinical note (the consultant; the practice PA for administrative purposes; your GP if a copy is sent at your request).

If you want the audio deleted earlier than the standard retention period, the practice can request that on your behalf — contact Sarah at [email protected].

At the start of a consultation where Heidi is being used, Fiona will mention it briefly and explain that you can ask for hand-written notes instead. Under NHS England ambient-scribe guidance and UK GDPR, explicit consent is not required for individual care — the obligation is transparency and the opportunity to opt out.

Your choice applies to that consultation. If you are comfortable with Heidi at one appointment, you can decline at the next — or vice versa.

If you decline, Fiona will take notes by hand. The clinical record is the same — the note in your file at the end of the appointment is a clinician’s note either way; what changes is whether the AI drafted it first.

Your options What you can ask

  • “Don’t use Heidi for this appointment.” Fine. Fiona writes a hand-written note instead. No need to justify it and it does not affect your care2.
  • “Delete the audio earlier than the standard retention window.” The practice can request early deletion on your behalf — contact Sarah at [email protected]4.
  • “Who can read the transcript?” The same people who can read your other clinical notes — Fiona, Sarah for administrative purposes, and your GP if a copy of the consultation letter is sent. Heidi’s own staff do not routinely access individual transcripts4.
  • “Where is my consultation audio stored?” On Heidi’s infrastructure, typically cloud servers in the UK or EU, operated under Heidi’s published data-handling policy34.
  • “Can I have a copy of the note?” Yes. The clinical letter that follows the consultation is the patient-facing version of the note; a fuller copy can be requested through the practice.

At consultation What to discuss with Fiona

If AI scribing matters to you, you don’t need to raise it in advance — Fiona will mention it at the start of the consultation and confirm you’re comfortable. If you want to think it through before the appointment, useful things to consider:

  • Whether you’d prefer hand-written notes for sensitive parts of the conversation. It is fine to opt out part-way through; Fiona can pause Heidi if asked.
  • Whether you want a copy of the consultation letter sent to your GP — the standard at Breastory is to send a letter to your GP unless you ask otherwise.
  • Any specific data-handling questions — Sarah can route these to Heidi’s data protection contact if needed.

Resources Further reading

Sources & guidance

Every figure on this page is anchored to a published source. Tap a number in the text or below to jump to the reference.

  1. regulatory Medicines and Healthcare products Regulatory Agency (MHRA). Software and AI as a Medical Device — Change Programme. London: MHRA. 2024 https://www.gov.uk/government/publications/software-and-ai-as-a-medical-device-change-programme Cited for: UK regulatory framework for AI-as-medical-device, including the narrowly-defined intended-use of AI scribes as decision-support tools.
  2. guidance General Medical Council. Good Medical Practice. London: GMC. 2024 https://www.gmc-uk.org/professional-standards/professional-standards-for-doctors/good-medical-practice Cited for: The duty of openness and accountability that applies to clinicians regardless of the tools they use, including AI scribes.
  3. regulator Information Commissioner's Office (ICO). UK GDPR guidance for the health and social care sector. Wilmslow: ICO. 2024 https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/ Cited for: UK data-protection framework, including special-category-data lawful-basis requirements (Article 9(2)(a) explicit consent and Article 9(2)(h) provision of healthcare).
  4. vendor_documentation Heidi Health. Clinical and data-protection documentation. Heidi Health. 2026 https://www.heidihealth.com/en-gben-gben-gben-gb Cited for: Vendor's published audio retention period (currently 30 days) and sub-processor list.