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.
Abstract
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.
Vibe coding is a workflow in which users describe the desired software behavior in natural language, following which large language models (LLMs) generate the code. It is increasingly being used by non-developer clinicians without any formal programming training to create tools that support the clinical workflow. This...
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A codepath-aware governance framework for AI-assisted engineering in regulated codebases, with emphasis on financial services, payments, healthcare, and other domains where software changes may affect legal, operational, privacy, and audit obligations is developed.
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In this single-centre proof-of-concept, aggregate accuracy was insufficient for safety characterisation: models with indistinguishable mean accuracy carried different catastrophic-failure tiers and reproducibility profiles.
Fang-Yan Liu, Zhi Liu, Xiaolu Fei et al.· Journal of medical systems· 0 citations
Generative artificial intelligence (AI) is dramatically changing the division of labor in digital health innovation. Until recently, a frontline health professional, educator or researcher who identified a workflow problem typically needed a software developer to translate that problem into a functioning application. D...
TRACE (Tracking Reliability of AI-generated Conversational Evidence), a practitioner-audit framework for evaluating the downstream workflow reliability of conversational AI, is presented, suggesting taxonomy legibility under standardized conditions even where human judgment diverged.
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These findings support independent, domain-specific assurance of both implementation and the clinical expectations and non-functional requirements encoded in clinician-led AI-assisted clinical software.
A. Munuera, M. A. Arrabal Polo, Clemente García Hidalgo et al.· International Journal of Med...· 0 citations
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