Evolution of AI enabled healthcare systems using textual data with a pretrained BERT deep learning model

In the rapidly evolving field of healthcare, Artificial Intelligence (AI) is increasingly driving the promotion of the transformation of traditional healthcare and improving medical diagnostic decisions. The overall goal is to uncover emerging trends and potential future paths of AI in healthcare by applying text mining to collect scientific papers and patent information. This study, using advanced text mining and multiple deep learning algorithms, utilized the Web of Science for scientific papers (1587) and the Derwent innovations index for patents (1314) from 2018 to 2022 to study future trends of emerging AI in healthcare. A novel self-supervised text mining approach, leveraging bidirectional encoder representations from transformers (BERT), is introduced to explore AI trends in healthcare. The findings point out the market trends of the Internet of Things, data security and image processing. This study not only reveals current research hotspots and technological trends in AI for healthcare but also proposes an advanced research method. Moreover, by analysing patent data, this study provides an empirical basis for exploring the commercialisation of AI technology, indicating the potential transformation directions for future healthcare services. Early technology trend analysis relied heavily on expert judgment. This study is the first to introduce a deep learning self-supervised model to the field of AI in healthcare, effectively improving the accuracy and efficiency of the analysis. These findings provide valuable guidance for researchers, policymakers and industry professionals, enabling more informed decisions.

Text 118403.pdf - Published Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (2MB) Official URL or Download Paper: https://www.nature.com/articles/s41598-025-91622-8...

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