Twelve quick tips for AI-assisted coding in science.

Clinical Snapshot

Key Findings

• Effect Size: Not applicable — no empirical effect size reported; this is a qualitative guidance document

• Primary Outcome: Twelve structured practical recommendations for AI-assisted coding in scientific research, organised across four thematic domains: (1) problem preparation and understanding, (2) managing context and interaction, (3) testing and validation, and (4) code quality assurance and iterative improvement

• Nnt Or Sensitivity: Not applicable — no diagnostic, therapeutic, or prognostic metrics reported; the paper does not provide NNT, sensitivity, specificity, or hazard ratio estimates

• Confidence Interval: Not applicable — no quantitative analyses performed

Clinical Application

The tips are presented as immediately actionable with no requirement for additional resources, training infrastructure, or institutional approval. Feasibility is high for individual researchers. However, implementation fidelity across teams or institutions would require additional scaffolding such as code review policies, documentation standards, and training programmes not described in the paper. Australian researchers are increasingly using AI coding tools across NHMRC-funded projects, clinical trials units, and university-based computational health research. The Australian Code for the Responsible Conduct of Research (2018) and institutional research integrity frameworks already require reproducibility and transparency in computational methods — the tips in this paper are broadly consistent with these obligations. The Australian Research Data Commons (ARDC) and relevant discipline-specific bodies (e.g., AMSI, SSA) may find this guidance useful as a starting point for developing institution-level AI coding policies. There are no PBS or TGA implications. The RACGP does not directly govern computational research practice, though general practitioners engaged in clinical research or quality improvement projects using data analysis tools would benefit from the validation and reproducibility principles outlined. Australian universities and the ARC may wish to develop more formal, evidence-based guidance building on this foundation. Researchers conducting computational analyses in any scientific discipline, including clinical researchers, biostatisticians, health data scientists, and clinician-scientists who write or review code for data analysis, modelling, or software development. Particularly relevant for early- to mid-career researchers integrating AI coding assistants into their workflows.

Abstract

While AI coding tools have demonstrated potential to accelerate software development, their use in scientific computing raises critical questions about code quality and scientific validity. In this paper, we provide twelve practical tips for AI-assisted coding that balance the capabilities of AI with the demands of scientific and methodological rigor. We address how AI can be leveraged strategically throughout the development cycle with four key themes: problem preparation and understanding, managing context and interaction, testing and validation, and code quality assurance and iterative improvement. These principles serve to emphasize maintaining human agency in coding decisions, establishing robust validation procedures, and preserving the domain expertise essential for methodologically sound research. These tips are intended to help researchers harness AI's transformative potential for faster software development while ensuring that their code meets the standards of reliability, reproducibility, and scientific validity that research integrity demands.

References

1. 1.Bridgeford, E. W., Campbell, I. D., Chen, Z., Lin, Z., Ritz, H., Vandekerckhove, J., & Poldrack, R. A. (2026). Twelve quick tips for AI-assisted coding in science. PLoS Computational Biology. https://doi.org/10.1371/journal.pcbi.1014428