Healthcare

General Practitioner

Based on 36 assessments

20% Low risk

Average realistic automation risk across all General Practitioner profiles in the dataset.

Raw potential
31%
Realistic risk
20%
Research benchmark ?
29%

Raw potential = I/O automation ceiling. Realistic risk = adjusted for informal knowledge and social context. Research benchmark: Eloundou et al. (2023)

Distribution across 36 profiles. Middle half of General Practitioners score between 17% and 23%.

0% 50% 100%
p10 · 14%
25% · p90
On-screen work 16%

Done entirely on a computer. High AI exposure — these tasks are already in the automation zone.

In-person + screen 10%

Physical sensing, digital output — e.g. interviewing someone then writing a report. Partially protected.

Computer + action 21%

Computer input, real-world output — needs someone to act on it, not just software.

Fully in-person 53%

No computer required. Furthest from automation — the strongest human advantage.

3 synthetic profiles for a General Practitioner, ordered by automation exposure. Tab between them to see how task mix drives the score difference.

Task Time Type Exposure
Conducting patient consultations to diagnose illnesses, discuss symptoms, and recommend treatments or further tests
deep expertise
40% AA 7%
Coordinating with specialists, nurses, or other healthcare providers to manage patient referrals or follow-up care
some context needed
20% AA 3%
Performing minor medical procedures (e.g., wound suturing, vaccinations, or minor lesion removals)
16% AA 9%
Prescribing medications, therapies, or lifestyle changes and explaining their use or benefits to patients
13% DA 18%
Providing preventive care advice, such as screenings, vaccinations, or health education (e.g., diet, exercise, smoking cessation)
deep expertise
4% DA 7%
Reviewing and interpreting lab results, imaging reports, or specialist letters to inform patient care plans
3% DD 56%
Documenting patient visits, updating medical records, and coding diagnoses for billing or administrative purposes
1% AD 46%

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