SAFE
The core of this job is physically inseparable from the patient: pelvic exams, cesarean sections, hysterectomies, laparoscopy, forceps and vacuum deliveries, and managing a hemorrhage at 3 a.m. AI is already useful for documentation, ultrasound and cytology screening, risk stratification, and coding, which trims clerical load but not clinical work. State licensure plus malpractice exposure — obstetrics is among the most litigated specialties — makes a named human physician legally and personally accountable for every delivery and operative decision.
Headcount grew steadily across the period.
Median pay $239,200 → $292,910 -2.0% in real terms
This line is counted by the Bureau of Labor Statistics — the one figure on this page that isn't a judgement of ours. Headcount moves on demand, offshoring, demographics and the business cycle, and automation is one term among several, often not the loudest.
So a falling line is not evidence that AI did it, and a rising one is not evidence that it won't. Both happen in this register: some occupations resist automation and shrink anyway, others are highly automatable and keep growing. The marked year is 2020.
BLS projection, 2024–2034
+1.2% 21,500 → 21,700 on the projections basis
Hard to automate, and growing
The work resists current AI and the BLS projects +1.2% more of these jobs by 2034. Note that safe does not mean well paid — several of the fastest-growing resistant occupations are among the lowest paid on the register.
Different clocks. The score is what current AI could do to this work today. The projection is how many of these jobs will exist in 2034. Everything between the two — how fast employers actually adopt, whether demand grows in the meantime — is why they can point opposite ways without either being wrong.
~600 openings a year on average, including replacing people who leave.
DoctorPhysicianHospitalistOB (Obstetrician)GYN (Gynecologist)MD (Medical Doctor)Gynecologic OncologistGynecological OncologistReproductive EndocrinologistOB/GYN (Obstetrician Gynecologist)GYN Physician (Gynecology Physician)OB Specialist (Obstetrics Specialist)OBGYN (Obstetrician and Gynecologist)Physician OB (Physician Obstetrician)Physician GYN (Physician Gynecologist)OBGYN Doctor (Obstetrics and Gynecology Doctor)OBGYN MD (Obstetrics Gynecology Medical Doctor)Maternal-Fetal Medicine Physician (MFM Physician)OB/GYN Physician (Obstetrics Gynecology Physician)OBGYN Physician (Obstetrics and Gynecology Physician)OB/GYN Generalist (Obstetrics and Gynecology Generalist)OBGYN Physician (Obstetrician and Gynecologist Physician)OB/GYN Hospitalist (Obstetrics and Gynecology Hospitalist)
Holding it up: embodiment . Weakest point: task resistance .
Tasks largely resist digitisation Reading a fetal heart tracing and deciding to move to the OR, then getting the head out through a shoulder dystocia with the McRoberts and Woods maneuvers, are not documentable-and-delegated tasks; the 16 rather than 19 reflects that a real share of an OB/GYN's week — well-woman counseling, Pap and HPV result triage, contraceptive management, prenatal visit templates — is protocol-driven and already partly absorbed by nurse practitioners, midwives and algorithmic screening.
Hands-on in uncontrolled environments There is no version of a cesarean, a manual placenta extraction, a cerclage, a colposcopy-directed biopsy, or a bimanual uterine massage for atony that happens through a screen, and the environment is uncontrolled by definition: labor decompensates on its own schedule, the patient is bleeding, and you are the person with your hands inside a body cavity — that is the top of the band.
Licensed human required and personally liable State medical licensure plus DEA registration plus hospital privileges and board certification all attach to one named individual, and obstetrics carries among the highest malpractice premiums and longest tail liability in medicine because a birth injury claim can be filed until the child reaches majority — the physician who signed the delivery note is the defendant, not the software that flagged the tracing.
Exists to be accountable for ambiguous calls Deciding whether to induce at 39 weeks, when to abandon a trial of labor after cesarean, whether a previable pregnancy meets the state's maternal-life exception, or how to counsel a patient with an abnormal NIPT result are calls made under time pressure with incomplete data and two patients' interests to weigh — the 18 rather than 20 reflects genuine guardrails from ACOG practice bulletins and institutional protocols.
The verdict above describes this occupation as a whole. Almost nobody does the typical version of a job — tick what's actually in your week and see how your own mix sits.
Your task mix speaks to task resistance (16/20 here) — how much of the day's work current AI already does. That is the dimension the boxes above are about.
It cannot move the other three. Liability shield (20/20) is whether the law requires a licensed human to sign. Trust premium (17/20) is whether buyers specifically pay for a person. Judgment and accountability (18/20) is whether the role exists to own consequential calls. Those are facts about the occupation's standing, not about which tasks are in your week — a paralegal who does only trial exhibits still holds no licence. Together they are 55 of this occupation's 91 points (60%).
Embodiment (20/20) is also a property of the work rather than the worker, but we don't tag individual tasks as physical or not, so the picker can't tell you anything about it. That's a limit of this tool, not a claim.
Did we get the list right? Tell us what's missing — the tasks are written from the outside, and you're reading this from the inside.
The moves above are yours to make. This is the other half: what would have to change in the world for the occupation itself to score higher. None of it is in any one person's gift, but it is where the floor actually comes from. Scores here are not a one-way ratchet. Only two of the five dimensions — task resistance and embodiment — track what machines can do. The other three track law, what buyers will pay for, and who is answerable, and those move in both directions, often in response to the same pressure AI creates. If every lever below landed, this occupation would score around 97/100, still SAFE.
Task-mix shift: ambient documentation (Nuance DAX, Abridge), AI fetal-biometry and cytology triage, and autocoding absorb the clerical and screening tier, leaving operative management, shoulder dystocia and hemorrhage response, and ambiguous fetal-heart-tracing calls. The residual day is almost entirely the judgment tier.
Continued shift of low-risk births to midwife-led and freestanding birth-center care raises the OB's remaining case mix to high-acuity, and patients choosing hospital OB care are specifically buying named-physician continuity (e.g., laborist vs. private-practice 'my doctor delivers me' models marketed by practices, plus doula/patient advocacy pressure for a known attending). If payers begin covering continuity-of-provider arrangements explicitly, the willingness to pay for a specific human becomes contractual rather than preferential.
If ACOG/state perinatal quality collaboratives formalize a named-attending sign-off for AI-flagged fetal monitoring interpretation and for cesarean indication (mirroring existing NTSV cesarean-rate accountability reporting), the ambiguity calls become explicitly attributed rather than diffused across a care team.
The limit. embodiment and liability_shield are already at 20; the composite is within a few points of the maximum, so realistic movement is marginal. The meaningful risk to this occupation is scope reallocation to midwives and CNMs and rural unit closures cutting headcount, not AI substitution of the physician's task set.
| New York-Newark-Jersey City, NY-NJ | 2,470 | $329,750 +13% |
| Chicago-Naperville-Elgin, IL-IN | 1,120 | $208,000 -29% |
| Dallas-Fort Worth-Arlington, TX | 770 | $321,260 +10% |
| Detroit-Warren-Dearborn, MI | 550 | $209,550 -28% |
| Boston-Cambridge-Newton, MA-NH | 500 | $235,460 -20% |
| Columbus, OH | 490 | $179,810 -39% |
| Minneapolis-St. Paul-Bloomington, MN-WI | 490 | $306,770 +5% |
| Tampa-St. Petersburg-Clearwater, FL | 410 | — |
| Burlington-South Burlington, VT | 70 | $444,290 +52% |
| Portland-South Portland, ME | 40 | $438,700 +50% |
| Ogden, UT | 50 | $428,800 +46% |
We have no reported case of a named organisation automating this occupation. Not one deployment, not one announcement.
That is worth saying out loud next to a score of 91. The verdict above is about what the work exposes — what current AI could do to these tasks. It is not a claim that anyone has done it. For this occupation those two things have come apart completely: the capability argument is on this page, and the evidence column is empty.
Has AI actually changed your work? One tap, anonymous, and the running tally is public. Nothing else is asked of you.
Rather than check back: get the digest and we'll tell you what changed — or watch a single occupation from its own page.