US dataswitch to UK
Medical Transcriptionists
returning dictated reports in printed or electronic form for physician's review, distinguishing between homonyms and recognizing inconsistencies and mistakes in medical terms and setting up and maintaining medical files and databases. If that's your week, this page is about your job.
The honest answer
Most tasks in this job are the kind AI has learned to do: returning dictated reports in printed or electronic form for physician's review. The tasks, though, are not you.
It would be a lie to soften that, and another lie to promise a rebuilt version of this job inside this job.
So the hope here is what you already carry: the judgment you bring to a variety of clerical and office tasks is real, and the moves below are built from it. The first step is down this page.
Your week, as this page understands it
Transcribe medical reports recorded by physicians and other healthcare practitioners using various electronic devices, covering office visits, emergency room visits, diagnostic imaging studies, operations, chart reviews, and final summaries. Transcribe dictated reports and translate abbreviations into fully understandable form. Edit as necessary and return reports in either printed or electronic form for review and signature, or correction. The job title says “medical transcriptionists”. The real job is the part underneath: performing a variety of clerical and office tasks. That is the thing someone has to be right about.
The exposed part of this job is specific, and we won’t pretend it is coming back. But medical transcriptionists is not one task. It is 15 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is performing a variety of clerical and office tasks, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 79%
- changing shape
- 4%
- staying human
- 17%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 71 out of 100 (66–77 allowing for uncertainty): high exposure, across 15 scored tasks. The number is the support for the sentence above it, not a headline about anyone’s future.
How we know this
What is measured: Every published task statement for medical transcriptionists is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.
How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.
Release: 2026-q4.1, scores computed 2026-08-05. Read the full method.
Your job, task by task
These are the official task statements for this occupation, in plain English, sorted by what the evidence says is happening to each one. The official wording sits under every line so you can check the rewrite against it.
- Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
Shifting to AI
11 tasksTasks today’s tools can already do most of. This is the part we will not soften: where these rows are the bulk of your week, the week changes.
Returning dictated reports in printed or electronic form for physician's review
This is reading one thing and writing another: dictated reports in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Return dictated reports in printed or electronic form for physician's review, signature, and corrections and for inclusion in patients' medical records.” (O*NET task statement)
How this row was scored
Exposure score: 88 out of 100 (84–92 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Returning finished reports for the doctor’s signature is routine document handling that systems already do.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Translating medical jargon and abbreviations into their expanded forms to ensure the accuracy of patient and health care facility records
This is reading one thing and writing another: medical jargon in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Translate medical jargon and abbreviations into their expanded forms to ensure the accuracy of patient and health care facility records.” (O*NET task statement)
How this row was scored
Exposure score: 88 out of 100 (84–92 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Expanding medical abbreviations into full terms is dictionary work software does consistently.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Transcribing dictation for a variety of medical reports
This is reading one thing and writing another: dictation in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Transcribe dictation for a variety of medical reports, such as patient histories, physical examinations, emergency room visits, operations, chart reviews, consultation, or discharge summaries.” (O*NET task statement)
How this row was scored
Exposure score: 88 out of 100 (84–92 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Turning dictated audio into a written medical report is the job speech recognition was built for.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Identifying mistakes in reports and checking with doctors to obtain the correct information
This is reading one thing and writing another: mistakes in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Identify mistakes in reports and check with doctors to obtain the correct information.” (O*NET task statement)
How this row was scored
Exposure score: 61 out of 100 (54–68 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Software flags likely errors well, but checking a doubtful phrase means asking the doctor who dictated it.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 4/4.
Producing medical reports, correspondence, records, patient-care information, statistics, medical research and administrative material
This is reading one thing and writing another: medical reports, correspondence, records, patient-care information, statistics in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Produce medical reports, correspondence, records, patient-care information, statistics, medical research, and administrative material.” (O*NET task statement)
How this row was scored
Exposure score: 88 out of 100 (84–92 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Producing medical reports and records from dictation and notes is what speech and language software now does well.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Performing data entry and data retrieval services
This is reading one thing and writing another: data entry in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Perform data entry and data retrieval services, providing data for inclusion in medical records and for transmission to physicians.” (O*NET task statement)
How this row was scored
Exposure score: 81 out of 100 (77–85 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Entering and retrieving record data is routine computer work.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Changing shape
1 taskTasks where the machine takes the producing and a person keeps the checking, the deciding, or the answering-for-it. For most jobs this is the biggest group, and it is where "transformation, not termination" is literally visible.
Answering inquiries concerning the progress of medical cases
The software now makes the first pass at inquiries concerning the progress of medical cases, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · SupplementalSource: “Answer inquiries concerning the progress of medical cases, within the limits of confidentiality laws.” (O*NET task statement)
How this row was scored
Exposure score: 56 out of 100 (49–63 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Progress inquiries follow confidentiality rules and record data, though some callers want a person.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Staying human
3 tasksTasks that stay with a person, because they happen in the physical world, because the rules need someone accountable, or because the value is that a specific person does them.
Performing a variety of clerical and office tasks
This work happens in the physical world: a variety of clerical and office tasks, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Perform a variety of clerical and office tasks, such as handling incoming and outgoing mail, completing and submitting insurance claims, typing, filing, or operating office machines.” (O*NET task statement)
How this row was scored
Exposure score: 38 out of 100 (31–45 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Claims and typing are easy to automate; sorting and carrying the mail is not.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Receiving patients, schedule appointments and maintaining patient records
This work happens in the physical world: patients, schedule appointments and maintaining patient records, in a real place. Software cannot follow it there.
importance 5 · SupplementalSource: “Receive patients, schedule appointments, and maintain patient records.” (O*NET task statement)
How this row was scored
Exposure score: 32 out of 100 (25–39 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Booking and record keeping automate easily; receiving patients at the desk does not.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Receiving and screening telephone calls and visitors
This work happens in the physical world: telephone calls, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Receive and screen telephone calls and visitors.” (O*NET task statement)
How this row was scored
Exposure score: 32 out of 100 (25–39 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Phone calls can be handled by software, but greeting people who walk in needs somebody there.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Show the other 5 tasks
Reviewing and editing transcribed reports or dictated material
shifting to AIThis is reading one thing and writing another: transcribed reports in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Review and edit transcribed reports or dictated material for spelling, grammar, clarity, consistency, and proper medical terminology.” (O*NET task statement)
How this row was scored
Exposure score: 88 out of 100 (84–92 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Correcting spelling, grammar and medical terms in a draft is a core strength of language software.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Distinguishing between homonyms and recognizing inconsistencies and mistakes in medical terms
shifting to AIThis is reading one thing and writing another: homonyms in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Distinguish between homonyms and recognize inconsistencies and mistakes in medical terms, referring to dictionaries, drug references, and other sources on anatomy, physiology, and medicine.” (O*NET task statement)
How this row was scored
Exposure score: 88 out of 100 (84–92 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Telling similar-sounding medical words apart using references is pattern work software does reliably.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Deciding which information should be included or excluded in reports
shifting to AIThis is reading one thing and writing another: information in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Decide which information should be included or excluded in reports.” (O*NET task statement)
How this row was scored
Exposure score: 72 out of 100 (65–79 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Deciding what belongs in a report follows documented standards, with a person confirming borderline calls.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Setting up and maintaining medical files and databases
shifting to AIThis is reading one thing and writing another: and maintaining medical files in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Set up and maintain medical files and databases, including records such as x-ray, lab, and procedure reports, medical histories, diagnostic workups, admission and discharge summaries, and clinical resumes.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (59–73 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Setting up and maintaining medical record files is structured digital filing that systems largely handle.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Taking dictation using shorthand, a stenotype machine or headsets and transcribing machines
shifting to AIThis is reading one thing and writing another: dictation in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Take dictation using shorthand, a stenotype machine, or headsets and transcribing machines.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (59–73 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Speech recognition captures dictation directly, though live dictation sometimes means being with the doctor.
The five ratings: output a model can produce 4/4 · needs a body in a room 1/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
What this job pays, and how many people do it
- Median pay
- $40,410a year, the middle of the range, so half earn more and half earn less.bls-oews, 2025 · May 2025 estimates (national_M2025_dl.xlsx)
How we know this
Source: bls-oews
Reference period: May 2025 estimates (national_M2025_dl.xlsx)
Rounding: Shown as published.
- People doing this job
- 41,550in the US, 2025.bls-oews · May 2025 estimates (national_M2025_dl.xlsx)
What is deliberately not here: a forecast of how many of these jobs exist in ten years. Where an official projection exists for a market we publish it with its vintage; where it does not, we leave the space empty rather than borrow the other country’s number.
Why this is shifting
The reason is boringly specific. Most of what is shifting here is reading one thing and writing another: dictated reports in, a record out. The rows above are exactly that shape: returning dictated reports in printed or electronic form for physician's review and translating medical jargon and abbreviations into their expanded forms to ensure the accuracy of patient and health care facility records. What it cannot do is be there in the room, and that is still where a variety of clerical and office tasks get done. Which is why this page talks about your tasks changing, not your job ending.
Your move
Over a pint: what I’d tell you if you were my friend
The exposed part of your job is the biggest part, and I am not going to dress that up: returning dictated reports in printed or electronic form for physician's review is work today's tools do quickly and cheaply, and that is not coming back.
So, given all that: 79% of this job's task weight sits in rows the software is already learning, 4% in rows that change shape rather than disappear, and 17% in rows it is nowhere near. That is the position, measured across 15 scored tasks. It is not a forecast about you.
What you have that the software does not is performing a variety of clerical and office tasks, plus the years of knowing when something looks wrong before you can say why. That is the raw material for everything below.
This week: one thing
Sit on the machine's side of the desk. Pick one real piece of dictated reports you would normally do yourself, let whatever software you already have take the first pass at it, and then go through what it produced and write down every single thing it got wrong. One evening this week. Do not fix anything yet. Just catch it.
- What you end up holding
- a written list of the machine’s mistakes, in your handwriting
- How long it takes
- an evening, or an hour if you pick one job rather than one client
If you can’t run software on your employer’s or your clients’ data: Do the same hunt on paper. Take one printed piece of dictated reports, work through it the way you always do, and mark every point where you made a call rather than followed a rule. Same evening, same list, nothing to log into and nobody to ask permission from. That list is the same artifact: it is the judgment written down.
Over the next 90 days
Change one sentence about what you do. Not on a CV. Out loud, to whoever pays you. From “my job is returning dictated reports in printed or electronic form for physician's review” to “I check what the software does and tell you what it means.” Your error list from this week is what makes that sentence true instead of a claim, so use it: show it once, to one person, inside the next ninety days. Same skills, priced as judgment rather than as typing.
Over the next 12 months
Walk toward the end of this job that answers for things, and get it recognised. Pick the one part of performing a variety of clerical and office tasks you are already best at, and spend the year making it formal: a qualification, a named responsibility, a specialism people ask for by name. Price it honestly: that is evenings, it is months rather than weeks, and the seats are competitive because everyone in your position is looking at the same door. Before you pay for anything, use CareerOneStop - Find local training. It is free, it is the Labor Department's own service, and it is listed below with the rest of the free routes.
The roads out of here, and why I am not sending you down them
I looked at the obvious moves out of this job, and here is what I found.
I checked the 12 nearest US occupations to medical transcriptionists (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was medical records specialists: only about 3% of its durable work is work you already do and it is under the same pressure this job is. I am not going to pretend that is comfortable news: 79% of your own task list is already in the top exposure band. But the answer on this evidence is not a sideways jump into a job with the same problem. It is to walk toward the end of this one that answers for things. “perform a variety of clerical and office tasks” is the part that stays with a person, and spending a year getting formally recognised for it beats a standing start somewhere else.
How that was checked: this job was compared against all 830 US occupations in this release on their official task statements, and the 12 nearest were examined one by one. A move that turns on an industry, an employer or a qualification rather than on the work itself will not show up in a check like that. And this release carries no licence register, so anything you are weighing needs that looked up separately.
3 moves I checked and rejected
These are the obvious-looking jumps. They are here with their reasons rather than quietly dropped, because the ones that fail are worth knowing about. It is one less thing to turn over at night.
Medical Records Specialists
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already answer inquiries concerning the progress of medical cases, within the limits of confidentiality…, and their equivalent is to protect the security of medical records to ensure that confidentiality is maintained. Across both published task lists that is about 3% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 3% of the durable side of that job. That is a different job, not a next step. I will not move you off one melting floe onto another: 76% of its own task list already scores in the top exposure band (63/100 in this release), so the same software is eating it.
Medical Secretaries and Administrative Assistants
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already receive patients, schedule appointments, and maintain patient records, and their equivalent is to schedule and confirm patient diagnostic appointments, surgeries, or medical consultations. Across both published task lists that is about 2% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 2% of the durable side of that job. That is a different job, not a next step.
Health Information Technologists and Medical Registrars
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already answer inquiries concerning the progress of medical cases, within the limits of confidentiality…, and their equivalent is to protect the security of medical records to ensure that confidentiality is maintained. Across both published task lists that is about 2% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 2% of the durable side of that job. That is a different job, not a next step.
What I’d stop worrying about
A friend tells you what not to spend fear on. This is that list.
The headline number you read somewhere
The big “X% of jobs” figures are about the whole economy, not about you. The number that describes your job is on this page: 79% of its task weight, across 15 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
“It’s too late for me to become something else”
You are not starting from zero, and the page shows why: performing a variety of clerical and office tasks is work the software cannot do and you already do it. The move above is a repricing of what you know, not a new career. Nobody who has just left college has that.
“I should learn to code”
Almost certainly not. The value in your job is moving toward checking, deciding and answering for the output, not toward writing the software. A weekend of Python will not change your position; the error list from this week will.
The “obvious” next job everyone suggests
I checked the obvious moves and most of them did not survive. The reasons are printed with the routes above, including the pay and the gate. A move that fails on the numbers is worth knowing about so you can stop turning it over at night.
You are reading the United States figures
The United Kingdom splits this work across more than one official group, of which Medical secretaries is the closest. The pay and employment figures are not directly comparable, and we do not average them together.
Switch to the United Kingdom page →partial match
The other groups this work is counted across:
In UK official statistics this job is counted as Medical secretaries and Health care practice managers. Pay is shown separately for each of those groups (medians cannot be averaged together), while the task list and the scores on this page are for this group only.
Your route through this
Where to go next, and what it costs
Free, and complete
The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.
Anywhere in the US:
CareerOneStop - Find local training
Search what's running near you, from the Labor Department's own database, before anyone sells you a course.
Free to search; individual programs vary, and some are funded
Anywhere in the US:
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
Free
No Space for this job, but one for what is happening to it
Nothing Collab365 runs is built for medical transcriptionists, and we are not going to point you at the nearest one and call it a fit.
There is one that is not about a job title at all. The AI Authority is about being the person who directs these tools at work rather than the person they get compared to. That is worth saying here, because 79% of the work on this page is already inside what they can do.
7 days free, no card needed. Explore up to 2 Spaces before you choose a plan: you pick a plan later, not now.
The AI Authority is a general community about working with AI, not a course for medical transcriptionists. You do not need it to act on anything here: the moves above cost nothing and stand on their own. The data on this page is the same either way.
Noted, and thank you. We’ll email you if a Space for medical transcriptionists launches. Nothing else.
That did not look like an email address, so nothing was saved. Have another go below.
We could not save that. The fault is ours, not yours, and your address was not stored. Please try again later.
No deadline on any of this. The page will still be here, and the data is refreshed on a published schedule rather than when someone wants a headline.
Questions people ask about this job
- Will AI replace Medical Transcriptionists?
- Not as a job, but it is already doing parts of the work. Across the 15 official task statements scored for Medical Transcriptionists (United States, SOC 31-9094), 79% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 71 out of 100 (range 66–77, band: high). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.
- Which tasks in “Medical Transcriptionists” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Transcribe dictation for a variety of medical reports, such as patient histories, physical examinations, emergency room visits, operations, chart reviews, co…” (88/100, very high); “Review and edit transcribed reports or dictated material for spelling, grammar, clarity, consistency, and proper medical terminology” (88/100, very high); “Distinguish between homonyms and recognize inconsistencies and mistakes in medical terms, referring to dictionaries, drug references, and other sources on an…” (88/100, very high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
- Which tasks in “Medical Transcriptionists” stay human?
- About 17% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Receive and screen telephone calls and visitors” (32/100, low); “Receive patients, schedule appointments, and maintain patient records” (32/100, low); “Perform a variety of clerical and office tasks, such as handling incoming and outgoing mail, completing and submitting insurance claims, typing, filing, or o…” (38/100, low). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.
- What should someone working in “Medical Transcriptionists” do about AI?
- Start from the ledger rather than the headline: 79% of this job's weighted core work is exposed, and roughly 17% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.
- How is the AI exposure score for Medical Transcriptionists calculated?
- Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 15 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.
Where these numbers come from
Worth knowing about these figures
- The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
- Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
- Task statements
- onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
- Task weights
- onet-db (im-rt)
- Scores
- Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-05.
- Pay and employment
- bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))
Figures on this page come from release 2026-q4.1, published 2026-08-05. Every release keeps its own permanent address, so a figure you cite in March is still there, unchanged, in November.
The plain-English wording on this page is assembled directly from the task statements and the published ratings, not written by hand for this occupation. That is why it is specific, and it is also why we say so.
The routes and free resources further up are today’s, not the release’s (last reviewed 2026-08-05). A route is an offer, not a historical fact, so it moves on its own clock.
Using these figures?
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Plain text
Collab365 (2026). Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1 (methodVersion 2.0.0, promptVersion task_scoring_v1.0). https://futureproof.collab365.com/data/2026-q4.1. Licensed CC BY 4.0. Built with O*NET data (USDOL/ETA, CC BY 4.0); ONS data (Open Government Licence v3.0); GAISI task framework (arXiv:2507.22748, MIT); BLS data (public domain).
BibTeX
@misc{collab365futureproof2026q41,
title = {Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1},
author = {{Collab365}},
year = {2026},
url = {https://futureproof.collab365.com/data/2026-q4.1},
note = {Release 2026-q4.1, methodVersion 2.0.0, promptVersion task_scoring_v1.0, CC BY 4.0}
}Data as of release 2026-q4.1, published . Releases never change after publication; when the figures move, a new dated release is published beside this one and this one stays exactly where it is.