ArticleScientific reports2025
Evolution of AI enabled healthcare systems using textual data with a pretrained BERT deep learning model.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Structural characteristics and evolutionary trajectories of knowledge recombination in the field of AI-driven drug discovery.Scientific reports · 2026Article
- Scaling Biomedical Text-Mining: Transformers, GenAI, and Drug Discovery.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Anti-epileptic medication induced disturbed calcium-vitamin D metabolism: A behavioral analysis using association rule mining technique.World journal of experimental medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.