Scand J Clin Lab Invest. 2026 Jun 5:1-11. doi: 10.1080/00365513.2026.2684025. Online ahead of print.
ABSTRACT
Standard operating procedures (SOPs) are foundational to quality assurance in ISO-accredited clinical laboratories, but manual SOP development is labor-intensive and prone to error. With the emergence of large language models (LLMs), such as ChatGPT-5, there is growing interest in their potential to generate compliant, context-specific laboratory documentation. To test a regulatory-aligned, clause-based evaluation framework for assessing whether ChatGPT-5 can generate context-aware, fit-for-purpose SOP drafts within a real ISO-aligned laboratory environment. In a single-site, paired proof-of-concept study, 10 high-priority SOPs were generated using ChatGPT-5 and compared with matched SOPs written by experienced laboratory professionals. AI prompts were enriched with laboratory-specific inputs (e.g. operator manuals, reagent inserts and LIS codes). SOPs were evaluated using a seven-domain ISO/CLSI-aligned rubric, a laboratory-specificity audit, clause-mapping checklists, content validity indexing and usability testing by junior staff. Inter-rater reliability and paired non-parametric statistics were applied. AI-assisted SOPs demonstrated higher median quality scores, more complete ISO clause referencing, improved traceability and stronger lifecycle conformity compared with manual SOPs. Drafting time was reduced by approximately 91%. Expert reviewers showed excellent agreement (ICC = 0.91), and content validity indices exceeded established thresholds. Junior staff rated AI-assisted SOPs as clearer and more independently usable. Context-anchored prompts improved laboratory-specific relevance. This study demonstrates a replicable, clause-based framework for evaluating AI-assisted SOP generation within a regulated laboratory context. While findings support the feasibility of AI as a documentation co-author under expert oversight, external multi-center validation is required before broader regulatory or clinical adoption.
PMID:42247578 | DOI:10.1080/00365513.2026.2684025
