Medical Scientists, Except Epidemiologists
In short: today's best AI models could do about 9% of this job's working time (projected from METR's April 2026 measurement and Epoch's November 2026 index) if the work were set up for them, and about 58% by the end of 2028 if progress keeps its long-run pace. The conversations, in-person and hands-on work, about 33% of the time, stays with people.
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.
172,340 people do this job in the US. $103,410 a year.
Reviewed on September 30, 2026: what each task is made of, and what handing it over would ask.
A typical 40-hour week
- AI could take on0 h
- A person checks AI's work3 h
- Desk work AI can't do yet24 h
- Stays with people13 h
Medical Scientists, Except Epidemiologists, now: of a 40-hour week, AI could take on 0 hours, 3 hours become checking its work, 24 hours are desk work it can't do yet, and 13 hours stay with people.
The largest pieces: Evaluate effects of drugs and organisms (about 2 h, a person checks); Prepare and analyze tissue samples (under an hour, a person checks); Study health and physiology (under an hour, a person checks).
Estimates for a typical week of this job, if progress keeps its long-run pace, counting work the best models finish four times in five. Every task is below.
of the working time. Now is projected from METR's April 2026 measurement and Epoch's November 2026 index.
- 67%
- 0%
- 5%
- 1%
- 26%
of the work: Anthropic's measure of what people already bring to Claude at work (2025). It counts help with part of a task, so it can be higher than the share within reach.
On the chosen pace and reliability. Estimate.
Share of the working time within reach, now to 2030
Change assumptionsHide assumptionsLong-run pace, four in five
What each choice means: , , , , . Every pace starts from .
At this date and pace, models finish tasks up to 2.4 work days long four times in five.
What handing it over would take, now
The 9% within reach splits by what a model would need. The dates, pace and reliability follow the controls above. Estimates.
of the working time. The material can be handed to a model and the result handed back.
of the working time. The model has to be connected to the system the work lives in.
is conversation short enough by length. This site leaves conversation with people, so it is not in the total.
How far this has come
With GPT-4, measured in March 2023, 0% of this job's working time was within reach at . With Claude 3.7 Sonnet, measured in February 2025, 0%. With Claude Mythos Preview (early), measured in April 2026, 1%. Today's estimate: 9%.
A backcast: today's task data with the best model METR had measured by each date. It shows how fast the models have moved, not what anyone forecast at the time.
Measured use over time
Of the Claude.ai conversations Anthropic sampled and tied to a work task, no reportable share went to this job's tasks in May 2026; 0 of its 14 tasks showed up. Within each of Anthropic's methods, never across a change: 0.028% to 0.055% (released February and March 2025); 0.042% to 0.022% (August 2025 to February 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | 0.028% | 377 of 708 | 6 of 14 |
| Released March 2025 | 0.055% | 251 of 703 | 7 of 14 |
| The week of Aug 4, 2025 (released September 2025) | 0.042% | 261 of 655 | 5 of 14 |
| The week of Nov 13, 2025 (released January 2026) | 0.023% | 367 of 672 | 6 of 14 |
| The week of Feb 5, 2026 (released March 2026) | 0.022% | 388 of 677 | 6 of 14 |
| April 2026 (released June 2026) | none | n/a | 0 of 14 |
| May 2026 (released June 2026) | none | n/a | 0 of 14 |
Measured, not estimated: Anthropic's samples of Claude.ai conversations, each tied by a model to an O*NET task, from the Anthropic Economic Index. It says where Claude's use goes, not how much of this job's working time it covers, so it is not on the same scale as the reach above. The releases differ in their sample, their classifier and the smallest share they report (the June 2026 file rounds each task to a hundredth of a percent, so a smaller task reads as none), which is why a change of method starts a new line. At least 96% of each older release's task-tied use matched this site's tasks, and all of the newest. Claude.ai only, so use through other tools and other companies' models is not in it. Every job ranked.
Robots and the physical work
Anthropic rates 54% of this job's working time as physical work. For 37% of the working time, a robot has been shown doing most of the task somewhere: 37% in a purpose-built cell. 0.37 of 3, the 358th highest of the 923 jobs.
- Not physical 46%
- No robot shown 17%
- In a purpose-built cell 37%
Anthropic's rating, not this site's estimate: Claude Opus 5, searching the web, rated each physical O*NET task by the kind of setting in which a robot it could cite (deployed, sold or demonstrated) does most of the task, weighted by its own estimate of each task's share of the time (What work can robots do?, September 30, 2026, data CC BY 4.0; every job ranked). A robot shown somewhere is not a robot in your building: cost, setup and safety decide that. The share within reach on this page still counts no body work. The robot behind each rated task is under the task below.
What changes for the people in this job
From the task-by-task review, by the end of 2028 on the long-run pace at four in five. It says which parts of the work stay with people and which change, not whether anyone keeps a job: that depends on decisions no data set can see.
What stays with people
32% of the working time: the conversation, in-person and hands-on parts of the tasks.
- Investigate diseases and parasites, the hands-on work, 6%
- Study health and physiology, the hands-on work, 5%
- Plan and direct studies, the conversation, 5%
- Evaluate effects of drugs and organisms, the hands-on work, 5%
- Develop methods and present findings, the hands-on work, 4%
- Prepare and analyze tissue samples, the hands-on work, 3%
What becomes checking the machine
45% of the working time: desk work a model could do by then, in tasks where a mistake costs something or a licensed person signs. A person checks it. Another 12% is desk work that is small if wrong, which can be handed over with lighter checks.
- Write grant applications, costly if wrong, 11%
- Study health and physiology, costly if wrong, 7%
- Standardize dosages and manufacturing procedures, severe if wrong, 6%
- Plan and direct studies, costly if wrong, 6%
- Evaluate effects of drugs and organisms, costly if wrong, 5%
- Investigate diseases and parasites, costly if wrong, 4%
What to learn
- Briefing a model and checking its drafts. About 67% of the time is documents and data, the part models reach first: writing a clear brief, giving the right material, and checking what comes back.
- Reviewing work you did not do yourself. The tasks under What becomes checking the machine, where a mistake costs something, stay a person's to check: spotting what a draft gets wrong is a skill of its own.
- The hands-on craft. About 26% of the time is hands-on work, which no model here does; robots are rated separately on this page.
- Judgment where errors are severe. Some tasks are severe if wrong. Knowing when not to trust an output matters more there than speed.
- Handling protected records. Much of the work touches protected personal records: knowing what may be shared with a model, and how, is part of the job.
Your own version of this job: read it against your job description. The headline is free; the full personal report, every task with its timeline and what to learn, is $19 (a sample).
The tasks
Sorted by . 14 tasks from .
| Task | ||||
|---|---|---|---|---|
| Study animal and human health and physiological processes.Reading and analysis, with experiments behind it.Done daily by most who do it.Robots: in a purpose-built cell, Ginkgo Bioworks autonomous lab (Reconfigurable Automation Carts) + OpenAI GPT-5 (sold). Anthropic's rating. | 12% | 2.1 work days3.3 h to 1.7 work weeks | Documents 60%, hands-on 40% | 9%Desk part half by Oct 2027 |
| Write and publish articles in scientific journals.Writing up the research for publication.Done yearly or less by most who do it. | 12% | 1 work week2.1 work days to 2.8 work weeks | Documents 100% | 4%Half by Oct 2027 |
| Investigate cause, progress, life cycle, or mode of transmission of diseases or parasites.Experiments and analysis of how a disease spreads and progresses.Done daily by most who do it.Robots: in a purpose-built cell, Adam (Robot Scientist) (Aberystwyth University / University of Cambridge) (demonstrated). Anthropic's rating. | 12% | 1 work week1 work day to 4.2 work weeks | Documents 50%, hands-on 50% | 2%Desk part half by Jun 2028 |
| Plan and direct studies to investigate human or animal disease, preventive methods, and treatments for disease.Designing the studies on paper and directing the team.Done daily by most who do it. | 12% | 1.7 work weeks2.1 work days to 4.2 work weeks | Documents 60%, conversation 40% | 1%Desk part half by May 2028 |
| Conduct research to develop methodologies, instrumentation, and procedures for medical application, analyzing data and presenting findings to the scientific audience and general public.Analysis and writing, with bench work and presentations.Done daily by most who do it. Robots: no robot shown. Anthropic's rating. | 12% | 3.3 work weeks4.2 work days to 2.4 work months | Documents 70%, hands-on 30% | 0%Desk part half by Jan 2029 |
| Write applications for research grants.Proposals written for the funder.Done more than yearly by most who do it. | 12% | 1 work week2.1 work days to 3.5 work weeks | Documents 100% | 4%Half by Dec 2027 |
| Standardize drug dosages, methods of immunization, and procedures for manufacture of drugs and medicinal compounds.Working out the standards on paper, with some bench work.Done more than monthly by most who do it. | 10% | 4.2 work days6.7 h to 3.3 work weeks | Documents 80%, hands-on 20% | 4%Desk part half by Apr 2028 |
| Evaluate effects of drugs, gases, pesticides, parasites, and microorganisms at various levels.Experiments at the bench and analysis of what they show.Done daily by most who do it. Robots: in a purpose-built cell, Tox21 robotic qHTS platform (NCATS/NIH) (deployed). Anthropic's rating. | 9% | 6.7 h3.3 h to 2.1 work days | Documents 50%, hands-on 50% | 43%Desk part half by now |
| Prepare and analyze organ, tissue, and cell samples to identify toxicity, bacteria, or microorganisms or to study cell structure.Bench work on the samples, with the analysis written up.Done more than weekly by most who do it. Robots: in a purpose-built cell, Maholo LabDroid (MOTOMAN-CSDA10F) (Robotic Biology Institute / Yaskawa) (deployed). Anthropic's rating. | 4% | 4.2 h1.7 h to 1.3 work days | Hands-on 70%, documents 30% | 28%Desk part half by now |
| Teach principles of medicine and medical and laboratory procedures to physicians, residents, students, and technicians.Teaching physicians, residents and students in person, with preparation.Done more than weekly by most who do it. | 2% | 2.5 h50 min to 6.7 h | In person 70%, documents 30% | 29%Desk part half by now |
| Use equipment such as atomic absorption spectrometers, electron microscopes, flow cytometers, or chromatography systems.Running spectrometers, microscopes and cytometers.Done more than weekly by most who do it.Robots: in a purpose-built cell, BD FACSDuet Premium Sample Preparation System + BD FACSLyric (sold). Anthropic's rating. | 1% | 2.5 h50 min to 1 work day | Hands-on 100% | Stays with people |
| Follow strict safety procedures when handling toxic materials to avoid contamination.Handling toxic materials in the lab by the rules.Done daily by most who do it. Robots: in a purpose-built cell, Mobile robotic chemist (KUKA KMR iiwa-based, Cooper group) (University of Liverpool) (demonstrated). Anthropic's rating. | 0.8% | 25 min13 min to 1.3 h | Hands-on 100% | Stays with people |
Within reach, per task: the share of the task's time whose instances are short enough for a model to finish at the chosen date, pace and reliability. Half by: the date half of that time comes within reach. Made of: how the task's time splits among five kinds of doing. Only the documents and systems parts can count as within reach, so a task that is 30% documents can be at most 30% within reach.
Jobs that share skills with this one
From O*NET's related occupations. Within reach now and by the end of 2028, long-run pace, four in five.
Where this job works
The industry groups that employ the most of it (BLS, May 2025).
Where the most of these jobs are
The metro areas with the most people in it (BLS, May 2025).
Questions about this job
- How much of the work of Medical Scientists, Except Epidemiologists is within reach of AI now?
- An estimated 9% of the working time is within reach of AI models now and 57% by the end of 2028, on METR's long-run pace at four in five (31% to 66% across the three paces the site shows). Within reach means a model could finish work that long at that reliability. It is not a forecast of jobs lost or of savings.
- Which tasks of Medical Scientists, Except Epidemiologists come within reach first?
- Weighted by the time each takes, the tasks with the most within reach now (projected from METR's April 2026 measurement and Epoch's November 2026 index) are: Evaluate effects of drugs and organisms (43% of the task's time now, 50% by the end of 2028); Prepare and analyze tissue samples (28% of the task's time now); and Study health and physiology (9% of the task's time now, 55% by the end of 2028).
- Which tasks of Medical Scientists, Except Epidemiologists stay with people?
- Tasks that are people work or body work are not counted as within reach at any date. The largest: Operate laboratory instruments; and Follow safety procedures with toxic materials. Together such tasks are about 2% of the working time, and the people and body parts of the other tasks stay with people too.
- What should Medical Scientists, Except Epidemiologists learn as AI takes on more of the work?
- By the job's own task-by-task review, the first things to learn are briefing a model and checking its drafts; reviewing work you did not do yourself; and the hands-on craft. By the end of 2028 on the long-run pace at four in five, about 32% of the working time stays with people, 45% becomes checking a model's work and 12% could be handed over with lighter checks. It says which parts of the work change, never whether anyone keeps a job.
- Do people already use AI for the work of Medical Scientists, Except Epidemiologists?
- In the Claude.ai conversations Anthropic sampled for May 2026, no reportable share went to this job's tasks. Anthropic's labor market measure puts the share of the job's tasks seen in real Claude use at work at 4%. These are measures of use, not of how much of the work AI could finish, and they cover Claude only.
- Can robots do the physical work of Medical Scientists, Except Epidemiologists?
- Anthropic's robot exposure rating (September 2026) finds 54% of the working time is physical work, and that a robot has been shown doing most of the task, somewhere, for 37% of the working time. Its index is 0.37 of 3. One example: Ginkgo Bioworks autonomous lab (Reconfigurable Automation Carts) + OpenAI GPT-5 for "study health and physiology". A robot shown somewhere is not one in every workplace; this site's own estimates still count no physical work as within reach.
- How is this estimated?
- From the 14 tasks O*NET lists for the occupation, each with an estimated length checked against GDPval and a share of the working time, reviewed one task at a time on September 30, 2026 for what each is made of (documents, business systems, conversation, in person, hands-on), and METR's measurements of how long a task the best AI models can finish, carried forward to the date from the latest, Claude Mythos Preview (early) in April 2026. Data checked November 10, 2026.
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