IJCA Vol 4 i1 2025 webmag - Flipbook - Page 7
2025 | Volume 4, Issue 1
7
Message from IJCA Executive
Editor’s Desk
The International Journal of Conformity Assessment (IJCA) editorial team
presents the current volume, which explores the impact of Artificial Intelligence
on conformity assessment through a series of articles representing diverse
disciplines and perspectives.
The articles in this issue address advances in Artificial Intelligence, quality management, and
standardization in laboratory and industrial contexts. Diego Uribe’s article, “Evaluation of the
Capability of Generative AI to Interpret and Provide Guidance on the Application of the ISO/IEC 17025
Standard,” evaluates the performance of generative AI models in interpreting the ISO/IEC 17025
standard, focusing on a customized ChatGPT-based model he names L-Squad. Using a 40-question
assessment covering literal, inferential, and criterial comprehension, Uribe tested L-Squad against
three other AI tools (Meta AI, ChatGPT 4.0 Free, and ChatGPTo1). L-Squad achieved the highest overall
score, with its custom configuration and training contributing to strong performance in criterial
reasoning.
The article by Kerstin Haeckel et al., “Systematic Technology Identification for the Digitalization of
the Conformity of Production in the Automotive Industry,” presents research conducted by the BMW
Group that addresses the growing complexity of verifying vehicle compliance in the automotive
industry's Conformity of Production (CoP) process. The study aims to identify and validate
technologies capable of automating component identification (CID) checks. Future proof-of-concept
studies will determine which technologies best align component IDs with regulatory standards and
improve both accuracy and efficiency.
In the article “Balancing Innovation and Openness: The Role of Artificial Intelligence in Conformity
Assessment,” Hodjat A. Bagheri highlights the significant opportunities AI presents for conformity
assessment, including greater objectivity, efficiency, and adaptability—particularly in regions with
complex regulatory demands such as MENA and CIS. He also explores the dual nature of AI: while it
enhances fairness and reliability, it simultaneously raises challenges related to ethics, transparency,
and trust.
Emil Hazarian’s article, “The AI Transformation in Metrology and Conformity Assurance,” asserts
that AI is revolutionizing metrology and conformity assurance by automating compliance, improving
measurement precision, and enabling predictive analytics. Technologies such as Digital Calibration
Certificates (DCCs) and machine learning-based assessments reduce manual intervention and
enhance quality control. However, Hazarian also notes that this transformation brings challenges,
including workforce displacement, data security, and governance. He emphasizes that these
challenges must be addressed through greater collaboration among industry, regulatory, and
accreditation bodies to ensure ethical, secure, and standardized AI adoption.
The article “The Evolution of Quality Management in Laboratory Services: Ensuring Accuracy, Safety,
and Efficiency” by Vikash Chandra Mishra et al., examines how quality management in laboratory
services has advanced to meet growing demands for accuracy and patient safety. The authors trace
this evolution from early quality practices to the adoption of comprehensive quality management
