Will AI replace Medical Scientists, Except Epidemiologists?

Not as a job, but it is already doing parts of the work. Across the 14 official task statements scored for Medical Scientists, Except Epidemiologists (United States, SOC 19-1042), 9% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 28 out of 100 (range 23–34, band: low). 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 Scientists, Except Epidemiologists” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Write and publish articles in scientific journals” (65/100, high); “Write applications for research grants” (58/100, partial); “Consult with and advise physicians, educators, researchers, and others regarding medical applications of physics, biology, and chemistry” (40/100, partial). 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 Scientists, Except Epidemiologists” stay human?

About 79% 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: “Use equipment such as atomic absorption spectrometers, electron microscopes, flow cytometers, or chromatography systems” (0/100, minimal); “Follow strict safety procedures when handling toxic materials to avoid contamination” (0/100, minimal); “Prepare and analyze organ, tissue, and cell samples to identify toxicity, bacteria, or microorganisms or to study cell structure” (16/100, minimal). 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 Scientists, Except Epidemiologists” do about AI?

Start from the ledger rather than the headline: 9% of this job's weighted core work is exposed, and roughly 79% 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 Scientists, Except Epidemiologists 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 14 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.

US dataswitch to UK

Medical Scientists, Except Epidemiologists

following strict safety procedures when handling toxic materials to avoid contamination, standardizing drug dosages and conducting research to develop methodologies. If that's your week, this page is about your job.

The honest answer

AI changes the edges of this job, not the middle: following strict safety procedures when handling toxic materials to avoid contamination is work software can't reach.

Your move: what you can actually do about this ↓

Where the work sits, by task weight

shifting to AI: 9%
changing shape: 12%
staying human: 79%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

Whole-job exposure score 28 out of 100 (23–34 allowing for uncertainty): low exposure, across 14 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 scientists, except epidemiologists 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 2026-08-04. Read the full method.

What shifts is writing and publishing articles in scientific journals: the overhead at the edges, not the middle you trained for.

Your week, as this page understands it

Conduct research dealing with the understanding of human diseases and the improvement of human health. Engage in clinical investigation, research and development, or other related activities. The job title says “medical scientists” or “except epidemiologists”: officially one job, two names. The real job is the part underneath: following strict safety procedures when handling toxic materials to avoid contamination. 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 scientists, except epidemiologists is not one task. It is 14 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is following strict safety procedures when handling toxic materials to avoid contamination, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 9%
changing shape: 12%
staying human: 79%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

How we know this

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 2026-08-04. 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.

4 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
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

1 task

Tasks 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.

Writing and publishing articles in scientific journals
It is the same call made over and over on articles, with a right answer to check it against. That is what a model is trained on.
importance 4 · Core
Source: “Write and publish articles in scientific journals.” (O*NET task statement)
How this row was scored
Exposure score: 65 out of 100 (58–72 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Journal writing is pure text work with huge published precedent, but a usable paper still needs heavy author editing.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.

Changing shape

2 tasks

Tasks 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.

Writing applications for research grants
The software now makes the first pass at applications, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Write applications for research grants.” (O*NET task statement)
How this row was scored
Exposure score: 58 out of 100 (54–62 allowing for uncertainty): partial exposure, high confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Grant writing is document work AI drafts quickly, though tailoring it to a specific funder takes real rework.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Consulting with and advising physicians
The software now makes the first pass at and advising physicians, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.
importance 3 · Supplemental
Source: “Consult with and advise physicians, educators, researchers, and others regarding medical applications of physics, biology, and chemistry.” (O*NET task statement)
How this row was scored
Exposure score: 40 out of 100 (33–47 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Advice on scientific applications is knowledge work, though doctors and researchers want it from a colleague they know.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.

Staying human

11 tasks

Tasks 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.

Following strict safety procedures when handling toxic materials to avoid contamination
This work happens in the physical world: strict safety procedures, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Follow strict safety procedures when handling toxic materials to avoid contamination.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Handling toxic materials safely is careful physical work in protective kit.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Evaluating effects of drugs, gases, pesticides, parasites and microorganisms at various levels
This work happens in the physical world: effects of drugs, gases, pesticides, parasites and microorganisms, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Evaluate effects of drugs, gases, pesticides, parasites, and microorganisms at various levels.” (O*NET task statement)
How this row was scored
Exposure score: 25 out of 100 (18–32 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Software can analyze the numbers, but the exposures and effects still have to be produced in a real lab.
The five ratings: output a model can produce 2/4 · 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 2/4.
Planning and directing studies to investigate human or animal disease
The ratings behind this row put studies well outside what today's tools can do on their own.
importance 4 · Core
Source: “Plan and direct studies to investigate human or animal disease, preventive methods, and treatments for disease.” (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.
The rating behind it: AI drafts study designs well, yet a named researcher still has to run the study and answer for it.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Conducting research to develop methodologies
This work happens in the physical world: research, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Conduct research to develop methodologies, instrumentation, and procedures for medical application, analyzing data and presenting findings to the scientific audience and general public.” (O*NET task statement)
How this row was scored
Exposure score: 24 out of 100 (17–31 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: Data analysis and write-up are strong AI ground, but building methods and instruments needs people in the lab.
The five ratings: output a model can produce 2/4 · 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.
Preparing and analyzing organ, tissue and cell samples to identify toxicity, bacteria or microorganisms or to study cell structure
This work happens in the physical world: organ, tissue and cell samples, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Prepare and analyze organ, tissue, and cell samples to identify toxicity, bacteria, or microorganisms or to study cell structure.” (O*NET task statement)
How this row was scored
Exposure score: 16 out of 100 (12–20 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Preparing tissue and cell samples is bench work with hands and instruments; only the interpretation is deskbound.
The five ratings: output a model can produce 1/4 · 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 2/4.
Teaching principles of medicine and medical and laboratory procedures
The value here is that a specific person handles principles of medicine and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Teach principles of medicine and medical and laboratory procedures to physicians, residents, students, and technicians.” (O*NET task statement)
How this row was scored
Exposure score: 30 out of 100 (23–37 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: AI can build the teaching material, though residents and technicians still learn procedures from a person in the room.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Studying animal and human health and physiological processes
This work happens in the physical world: animal, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Study animal and human health and physiological processes.” (O*NET task statement)
How this row was scored
Exposure score: 29 out of 100 (22–36 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: Much of the literature work is deskbound, yet studying live physiology still means experiments someone has to run.
The five ratings: output a model can produce 2/4 · 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.

Show the other 4 tasks

Conferring with health departments, industry personnel, physicians and others to develop health safety standards and public health improvement programs
staying human
The value here is that a specific person handles health departments, industry personnel, physicians and stands behind it. That is earned, not computed.
importance 4 · Supplemental
Source: “Confer with health departments, industry personnel, physicians, and others to develop health safety standards and public health improvement programs.” (O*NET task statement)
How this row was scored
Exposure score: 30 out of 100 (23–37 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: AI can draft safety standards, but agreeing them takes live discussion between health departments, industry and doctors.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Investigating cause, progress, life cycle or mode of transmission of diseases or parasites
staying human
This work happens in the physical world: cause, progress, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Investigate cause, progress, life cycle, or mode of transmission of diseases or parasites.” (O*NET task statement)
How this row was scored
Exposure score: 29 out of 100 (22–36 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: Tracing how a disease spreads mixes well-documented reasoning with samples and fieldwork that need people present.
The five ratings: output a model can produce 2/4 · 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.
Standardizing drug dosages, methods of immunization and procedures for manufacture of drugs and medicinal compounds
staying human
The rules require a named, qualified person to answer for drug dosages, methods of immunization and procedures, and that person cannot be a piece of software.
importance 4 · Supplemental
Source: “Standardize drug dosages, methods of immunization, and procedures for manufacture of drugs and medicinal compounds.” (O*NET task statement)
How this row was scored
Exposure score: 28 out of 100 (21–35 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: someone qualified has to answer for it.
The rating behind it: Dosage and manufacturing standards are technical writing, but a qualified scientist has to stand behind each figure.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Using equipment, atomic absorption spectrometers, electron microscopes, flow cytometers or chromatography systems
staying human
This work happens in the physical world: equipment, atomic absorption spectrometers, electron microscopes, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Use equipment such as atomic absorption spectrometers, electron microscopes, flow cytometers, or chromatography systems.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Someone has to load samples into the microscope or spectrometer and run it; that is entirely hands-on.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.

What this job pays, and how many people do it

Median pay: $103,410a 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: 172,340in 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 the same call made over and over on articles, where the right answer is already known. The rows above are exactly that shape: writing and publishing articles in scientific journals and writing applications for research grants. What it cannot do is be there in the room, and that is still where strict safety procedures 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

Start with what does not change: following strict safety procedures when handling toxic materials to avoid contamination is the middle of this job, and the evidence on this page says it stays with a person.

So, given all that: 9% of this job's task weight sits in rows the software is already learning, 12% in rows that change shape rather than disappear, and 79% in rows it is nowhere near. That is the position, measured across 14 scored tasks. It is not a forecast about you.

So the thing worth your attention is not the job going away. It is the layer around it. Writing and publishing articles in scientific journals is the part turning into software, and being the person who understands that layer is worth money.

This week: one thing

Ask the one question. Find whoever is bringing new software into your workplace (the manager, the office, whoever runs the system) and ask them what it is meant to do to articles, and what it is not meant to touch. Ten minutes, this week, before anyone decides it for you.

What you end up holding: a straight answer about what is actually being rolled out, and when
How long it takes: ten minutes

If there’s nobody obvious to ask, or you’d rather not ask your manager: Put the same question to your union rep, your shift lead or the person who has been there longest, in person, over a break. Same ten minutes, same answer, and you will usually get a straighter one. Write down what they say. The note is the artifact, and it tells you whether strict safety procedures are in scope or not. Nothing to log into, no license needed.

Over the next 90 days

Get inside the tool rollout rather than waiting for it. Over the next ninety days, ask to be in the group that tests, checks or signs off whatever new system arrives near writing applications for research grants. It is usually an unglamorous seat that nobody fights for, and it is the one that decides how the software is used on your job rather than to it.

Over the next 12 months

On this evidence I would not retrain out of this job, and I will say that plainly rather than hedge it. The task list here is dominated by work that stays with a person. What I would do with a year is get formally recognised for the layer around it (the systems, the compliance, the planning), so you are the one who understands the software instead of the one it is done to. 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 scientists, except epidemiologists (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was epidemiologists: it pays 15.7% less and there are far fewer of those jobs than of yours. And on the numbers you do not need one. This job scores 28/100 here, with only 9% of the task list in the top band, and “follow strict safety procedures when handling toxic materials to avoid contamination” is not work that hands over cleanly. None of them beats deepening what you already have.

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.

Epidemiologists
Why it looked obvious: It came up as a near neighbour because one of your tasks is on their list in the same words: “plan and direct studies to investigate human or animal disease, preventive methods, and treatments for disease”. Across the whole of both lists that adds up to about 42% of the work in that job the software is not taking.
Why I am not recommending it: It is a pay cut, in those words: $87,220 against your $103,410, 15.7% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice. And it is a narrow door: about 12,090 of those jobs against 172,340 of yours (OEWS May 2025), 7% as many seats.
Look at that job’s page anyway →
Biochemists and Biophysicists
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already write applications for research grants, and their equivalent is to write grant proposals to obtain funding for research. Across both published task lists that is about 6% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 6% of the durable side of that job. That is a different job, not a next step. And it is a narrow door: about 33,830 of those jobs against 172,340 of yours (OEWS May 2025), 20% as many seats.
Look at that job’s page anyway →
Physicists
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already write and publish articles in scientific journals, and their equivalent is to report experimental results by writing papers for scientific journals or by presenting information…. Across both published task lists that is about 5% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 5% of the durable side of that job. That is a different job, not a next step. And it is a narrow door: about 20,430 of those jobs against 172,340 of yours (OEWS May 2025), 12% as many seats.
Look at that job’s page anyway →

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: 9% of its task weight, across 14 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
The headlines about your trade disappearing
They are usually about the technology, not the timetable. Changes to work like following strict safety procedures when handling toxic materials to avoid contamination arrive through rules, insurance and money, slowly and visibly. This page tracks the task evidence and will move when it moves.
Retraining out of a job that is holding up
On this evidence I would not spend money leaving. Spend it on the layer around the job instead: the tools, the paperwork, the planning. That is where the change actually is.
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 Biochemists and biomedical scientists 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 Biochemists and biomedical scientists, Natural and social science professionals n.e.c., Other researchers, unspecified discipline and Biological scientists. 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:
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No Space for this job, but one for what is happening to it

Nothing Collab365 runs is built for medical scientists / except epidemiologists, 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 9% of the work on this page is already inside what they can do.

Try The AI Authority free →

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 scientists / except epidemiologists. 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 scientists / except epidemiologists launches. Nothing else.

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No Space for medical scientists / except epidemiologists yet. Should there be one?

Collab365 Spaces is built by the same people as this site. We find the problems that AI and automation are creating inside one kind of work, then solve them as short courses, briefings and Blueprints. Each Space is the community too, so the research and the people doing your job are in the same place.

What a Space actually is, in full

Collab365 launches new communities where the need is real. If one for medical scientists / except epidemiologists existed, with researched problems, courses and people in the same boat, would you want in?

Your email

We use your email address for one thing: to tell you if a Space for medical scientists / except epidemiologists launches. We never sell it, never use it for unrelated marketing, and every email has a one-click unsubscribe. Our privacy policy has the full version.

This unlocks nothing. Every figure, every row and every step on this page is already yours, whether you fill this in or not.

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 Scientists, Except Epidemiologists?: Not as a job, but it is already doing parts of the work. Across the 14 official task statements scored for Medical Scientists, Except Epidemiologists (United States, SOC 19-1042), 9% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 28 out of 100 (range 23–34, band: low). 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 Scientists, Except Epidemiologists” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Write and publish articles in scientific journals” (65/100, high); “Write applications for research grants” (58/100, partial); “Consult with and advise physicians, educators, researchers, and others regarding medical applications of physics, biology, and chemistry” (40/100, partial). 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 Scientists, Except Epidemiologists” stay human?: About 79% 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: “Use equipment such as atomic absorption spectrometers, electron microscopes, flow cytometers, or chromatography systems” (0/100, minimal); “Follow strict safety procedures when handling toxic materials to avoid contamination” (0/100, minimal); “Prepare and analyze organ, tissue, and cell samples to identify toxicity, bacteria, or microorganisms or to study cell structure” (16/100, minimal). 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 Scientists, Except Epidemiologists” do about AI?: Start from the ledger rather than the headline: 9% of this job's weighted core work is exposed, and roughly 79% 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 Scientists, Except Epidemiologists 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 14 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.
4 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
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.

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Everything on this site is published under CC BY 4.0. Quote it, chart it, sell something built on it. Just say where it came from, and cite the dated release rather than the site, so the figure you quote stays checkable.

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}
}

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https://futureproof.collab365.com/data/2026-q4.1

Data as of release 2026-q4.1, published 2026-08-05. 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.