A Machine-Assisted Framework for Ontology Development and Standardization: Case Study in Digital Health Technologies

Digital health technologies (DHTs) are reshaping healthcare by enabling personalized care, improving patient outcomes, and accelerating clinical research. However, the surge in DHT-related literature creates new challenges in effectively organizing, retrieving, and applying the resulting knowledge. Ontologies, structured frameworks for categorizing and connecting concepts, are central to meeting these challenges. Traditional ontology development in digital health often depends on manual processes, limiting efficiency, scalability, and cross-disciplinary adaptability. Building on previous work categorizing DHTs, we propose a new framework combining DHT lexicon extraction, ontology enrichment, and human-in-the-loop validation. In this study, we illustrate how the concept of a "adaptive ontology," powered by large language models, can classify and enhance DHT ontologies systematically, yet semi-automatically. Thus, providing a practical path to managing the evolving landscape of digital health.