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AI exposure report

Will AI replace Biostatisticians?

Many of your tasks are within reach of AI. The role will change, so it pays to act now.

Task exposure to today's AI

61/100

High exposure

This is the occupation's typical task mix. Your own score can be up to 30 points higher or lower depending on how you work: routine share, people contact, judgment and how much you already use AI.

Get your personal score

Will AI replace Biostatisticians?

Not entirely. Today's AI tools can take on about 61% of the typical work in this occupation, mostly: write program code to analyze data with statistical analysis software. The parts that stay human include: Provide biostatistical consultation to clients or colleagues.; Analyze clinical or survey data, using statistical approaches such as longitudinal analysis, mixed-effect modeling, logistic regression analyses, and model-building techniques.; Write detailed analysis plans and descriptions of analyses and findings for research protocols or reports..

People who learn to use AI for the automatable parts keep the edge. The 3-phase plan below shows where to start.

Which tasks AI can take on

The largest tasks in this occupation, their share of the working week, and how much of each today's AI tools can do.

TaskAutomatable
Write program code to analyze data with statistical analysis software. 78%
Analyze clinical or survey data, using statistical approaches such as longitudinal analysis, mixed-effect modeling, logistic regression analyses, and model-building techniques. 70%
Write detailed analysis plans and descriptions of analyses and findings for research protocols or reports.AI already used 70%
Prepare tables and graphs to present clinical data or results. 65%
Draw conclusions or make predictions, based on data summaries or statistical analyses. 62%
Provide biostatistical consultation to clients or colleagues. 20%

+ 19 more tasks (47% of the week) in the full test.

Your human edge

  • Provide biostatistical consultation to clients or colleagues.
  • Analyze clinical or survey data, using statistical approaches such as longitudinal analysis, mixed-effect modeling, logistic regression analyses, and model-building techniques.
  • Write detailed analysis plans and descriptions of analyses and findings for research protocols or reports.

AI tools to try

Coding assistants (GitHub Copilot, Claude Code, Cursor)ChatGPT / Claude for design and debuggingAI test and documentation generators

Related roles

A 3-phase AI learning plan

Phase 1

Pair programming with AI

  • Use a coding assistant for boilerplate, tests and refactoring.
  • Give it context: project rules, examples, small steps.
  • Review AI code like a colleague's pull request.

Project

Use a coding assistant to rewrite one legacy SAS or R analysis script for a mixed-effects model, adding unit tests on simulated data, and log hours saved versus your last rewrite.

Phase 2

AI in your systems

  • Call an LLM API with structured output.
  • Retrieval over your own documents or data.
  • Evaluate quality, cost and latency before shipping.

Project

Build a tool that calls an LLM API with a fixed JSON schema to draft statistical analysis plan sections from retrieval over your own de-identified past protocols, and benchmark accuracy, cost and latency on 10 studies.

Phase 3

Production-grade AI

  • Evals and regression tests for prompts.
  • Security: prompt injection, data leaks, permissions.
  • Monitoring and cost control.

Project

Add a 30-case eval suite plus regression tests, prompt-injection checks and a token-cost dashboard to your SAP drafting tool, and report pass rate and cost per draft before and after.
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