Clin Chem Lab Med. 2026 Sep 7. doi: 10.1515/cclm-2026-0887. Online ahead of print.
ABSTRACT
The boom of generative artificial intelligence (AI) is empowering laboratory medicine specialists to create clinical software through conversational prompts without formal programming training, a practice referred to as “vibe coding”. Although vibe coding provides unprecedented potential to address workflow inefficiencies, it also introduces significant patient safety risks when AI-generated code is incorporated into lab processes without appropriate validation. A key issue is that laboratory specialists using these AI tools often lack the software engineering background to spot critical flaws in the code. This illusion of competence is exacerbated by the fact that individuals with limited expertise most severely overestimate their abilities, especially when AI tools generate syntactically correct code that looks professional. The distinction between “code that runs” and “code that is safe for clinical use” is significant. Current regulatory frameworks demand a broad set of requirements, such as risk management, extensive validation and verification, rigorous documentation, and quality assurance. Unreviewed and unvalidated vibe-coded software cannot meet these standards. Laboratory medicine is a particularly vulnerable discipline, as even minor errors can impact hundreds or thousands of patients due to the high volume of processed specimens. Nevertheless, vibe coding offers genuine value for rapid prototyping and proof-of-concept development when employed responsibly. An effective approach is that laboratory specialists use generative AI to prototype clinical concepts in a sandbox environment, followed by collaboration with qualified software developers and regulatory experts who translate them into production-grade systems. However, addressing the vibe coding risk necessitates institutional governance to fulfill the innovative potential while preserving the quality systems that laboratory medicine has established over decades.
PMID:42695169 | DOI:10.1515/cclm-2026-0887