Biostatisticians

Draw conclusions or make predictions based on data summaries or statistical analyses.

AI: Fully automatable - AI systems routinely draw conclusions and make predictions from summaries and statistical analyses and can do so end‑to‑end in many practical settings.

Analyze clinical or survey data using statistical approaches such as longitudinal analysis, mixed effect modeling, logistic regression analyses, and model building techniques.

AI: Fully automatable - AI tools can perform complex analyses—longitudinal models, mixed effects, logistic regression and model building—generate code, fits and diagnostics end‑to‑end in many applied contexts.

Calculate sample size requirements for clinical studies.

AI: Fully automatable - Sample size calculation is a well‑specified quantitative task that AI can accurately perform given explicit design assumptions and inputs.

Prepare tables and graphs to present clinical data or results.

AI: Fully automatable - Preparing publication‑quality tables and graphics from cleaned clinical data is a routine, automatable task that AI tools can perform reliably.

Prepare statistical data for inclusion in reports to data monitoring committees, federal regulatory agencies, managers, or clients.

AI: Fully automatable - Given clean data and templates, AI can accurately compute, format, and prepare statistical outputs for inclusion in reports to committees, regulators, managers, or clients.

Write program code to analyze data using statistical analysis software.

AI: Fully automatable - AI can generate, test, and adapt statistical analysis code (R, Python, SAS, etc.) given data and instructions, automating most coding tasks.

Design or maintain databases of biological data.

AI: Fully automatable - AI can design database schemas, generate ETL and maintenance scripts, and automate routine upkeep, enabling reliable full automation of database design and maintenance tasks.

Develop or use mathematical models to track changes in biological phenomena such as the spread of infectious diseases.

AI: Fully automatable - AI can build, fit, and run mathematical and epidemiological models and automate simulation and scenario analysis for tracking biological phenomena like infectious disease spread.

Design research studies in collaboration with physicians, life scientists, or other professionals.

AI: Partial - AI can propose and optimize study designs and assist in protocol drafting, but effective collaboration with clinicians, ethical review navigation, and domain judgment require human leadership.

Provide biostatistical consultation to clients or colleagues.

AI: Partial - AI can provide evidence-based recommendations and draft consults but lacks full contextual understanding, professional judgment, and accountability required for high‑stakes biostatistical consultation.

Write research proposals or grant applications for submission to external bodies.

AI: Partial - AI can generate strong drafts of proposals, aims, methods, and budgets, but cannot fully ensure institutional alignment, funding-specific nuance, or authorship/credential requirements for submission without human oversight.

Prepare articles for publication or presentation at professional conferences.

AI: Partial - AI can draft manuscripts, produce figures and slides, and perform routine analyses, but final scientific interpretation, novelty assessment, and authorship responsibility require human judgment.

Write detailed analysis plans and descriptions of analyses and findings for research protocols or reports.

AI: Partial - AI can produce detailed analysis plans from templates and study inputs, but tailoring to protocol‑specific nuances, regulatory conventions, and final sign‑off still requires expert human review.

Monitor clinical trials or experiments to ensure adherence to established procedures or to verify the quality of data collected.

AI: Partial - Automated monitoring and data‑quality checks are feasible, yet ensuring procedural adherence, on‑site verification, and complex corrective decisions cannot be fully automated.

Review clinical or other medical research protocols and recommend appropriate statistical analyses.

AI: Partial - AI can review protocols and recommend standard statistical approaches and flag issues, but nuanced protocol review and bespoke statistical judgment remain dependent on human expertise.

Develop or implement data analysis algorithms.

AI: Partial - AI can implement and prototype many data‑analysis algorithms and generate production code, but inventing novel methods and validating their statistical properties typically requires human statisticians.

Read current literature, attend meetings or conferences, and talk with colleagues to keep abreast of methodological or conceptual developments in fields such as biostatistics, pharmacology, life sciences, and social sciences.

AI: Partial - AI can rapidly read, synthesize, and summarize literature but cannot fully replicate in-person meetings, nuanced networking, or the sociopolitical aspects of staying abreast of developments.

Assign work to biostatistical assistants or programmers.

AI: Partial - AI can propose and optimize work assignments and schedules, but lacks the authority and nuanced personnel judgment required for full managerial responsibility.

Teach graduate or continuing education courses or seminars in biostatistics.

AI: Partial - AI can create course materials, deliver lectures and provide tutoring, but cannot fully replace the mentorship, real-time classroom leadership, and accreditation responsibilities of human instructors.

Plan or direct research studies related to life sciences.

AI: Partial - AI can design study protocols and suggest analyses, yet cannot assume legal/ethical responsibility or lead complex interdisciplinary coordination required to direct research.

Determine project plans, timelines, or technical objectives for statistical aspects of biological research studies.

AI: Partial - AI can draft project plans, timelines, and technical objectives and optimize schedules, but human leaders are needed for final decisions and adapting to unforeseen constraints.

Apply research or simulation results to extend biological theory or recommend new research projects.

AI: Partial - AI can analyze results and generate hypotheses or project recommendations, but translating these into validated theoretical advances requires human scientific judgment and experimental validation.

Collect data through surveys or experimentation.

AI: Partial - AI can run and manage surveys and coordinate experimental workflows, but cannot generally perform physical sample collection or fully oversee complex lab experimentation.

Analyze archival data such as birth, death, and disease records.

AI: Partial - AI can perform cleaning and statistical analyses of archival birth/death/disease records but still requires human epidemiological oversight for study design, confounding control, and interpretation.

Design surveys to assess health issues.

AI: Partial - AI can draft survey instruments and recommend sampling strategies, but human experts must refine wording, pilot instruments, and address ethical and contextual considerations.