CNN (88.3% accuracy) and XGBoost (86%) beat zero-shot LLMs (about 46%) on NMPA medical device classification, with no single model winning on accuracy, interpretability, and cost simultaneously.
Uncovering Regulatory Affairs Complexity in Medical Products: A Qualitative Assessment Utilizing Open Coding and Natural Language Processing (NLP)
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
This study investigates the complexity of regulatory affairs in the medical device industry, a critical factor influencing market access and patient care. Through qualitative research, we sought expert insights to understand the factors contributing to this complexity. The study involved semi-structured interviews with 28 professionals from medical device companies, specializing in various aspects of regulatory affairs. These interviews were analyzed using open coding and Natural Language Processing (NLP) techniques. The findings reveal key sources of complexity within the regulatory landscape, divided into five domains: (A) Regulatory language complexity, (B) Intricacies within the regulatory process, (C) Global-level complexities, (D) Database-related considerations, and (E) Product-level issues. The participants highlighted the need for strategies to streamline regulatory compliance, enhance interactions between regulatory bodies and industry players, and develop adaptable frameworks for rapid technological advancements. Emphasizing interdisciplinary collaboration and increased transparency, the study concludes that these elements are vital for establishing coherent and effective regulatory procedures in the medical device sector.
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AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification
CNN (88.3% accuracy) and XGBoost (86%) beat zero-shot LLMs (about 46%) on NMPA medical device classification, with no single model winning on accuracy, interpretability, and cost simultaneously.