Purpose
Nanomedicine has significant potential to revolutionize biomedicine and healthcare through innovations in diagnostics, therapeutics, and regenerative medicine. This study aims to develop a novel framework that integrates advanced natural language processing, noise-free topic modeling, and multidimensional bibliometrics to systematically identify emerging nanomedicine technology topics from scientific literature.
Design/methodology/approach
The framework involves collecting full-text articles from PubMed Central and nanomedicine-related metrics from the Web of Science for the period 2013–2023. A fine-tuned BERT model is employed to extract key informative sentences. Noiseless Latent Dirichlet Allocation (NLDA) is applied to model interpretable topics from the cleaned corpus. Additionally, we develop and apply metrics for novelty, innovation, growth, impact, and intensity to quantify the emergence of novel technological topics.
Findings
By applying this methodology to nanomedical publications, we identify an increasing emphasis on research aligned with global health priorities, particularly inflammation and biomaterial interactions in disease research. This methodology provides deeper insights through full-text analysis and leading to a more robust discovery of emerging technologies.
Research limitations
One limitation of this study is its reliance on the existing scientific literature, which may introduce publication biases and language constraints. Additionally, manual annotation of the dataset, while thorough, is subject to subjectivity and can be time-consuming. Future research could address these limitations by incorporating more diverse data sources, and automating the annotation process.
Practical implications
The methodology presented can be adapted to explore emerging technologies in other scientific domains. It allows for tailored assessment criteria based on specific contexts and objectives, enabling more precise analysis and decision-making in various fields.
Originality/value
This study offers a comprehensive framework for identifying emerging technologies in nanomedicine, combining theoretical insights and practical applications. Its potential for adaptation across scientific disciplines enhances its value for future research and decision-making in technology discovery.
