Evolution of AI enabled healthcare systems using textual data with a pretrained BERT deep learning model
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View abstract on PubMed
Summary
This summary is machine-generated.This study uses deep learning and text mining to analyze AI in healthcare trends from 2018-2022. It identifies key areas like IoT, data security, and image processing for future healthcare innovation.
Area Of Science
- Medical Informatics
- Artificial Intelligence
- Health Services Research
Background
- Artificial Intelligence (AI) is transforming healthcare, improving diagnostics and services.
- Traditional trend analysis relies on expert judgment, lacking efficiency and accuracy.
- Identifying emerging AI trends is crucial for future healthcare development.
Purpose Of The Study
- To uncover emerging trends and future directions of AI in healthcare.
- To apply advanced text mining and deep learning for trend analysis.
- To provide an empirical basis for AI commercialization in healthcare.
Main Methods
- Utilized text mining on 1587 scientific papers (Web of Science) and 1314 patents (Derwent Innovations Index) from 2018-2022.
- Employed multiple deep learning algorithms, including a novel self-supervised approach leveraging BERT.
- Analyzed market trends, research hotspots, and technological advancements.
Main Results
- Identified key market trends: Internet of Things (IoT), data security, and image processing in AI healthcare.
- Revealed current research hotspots and technological trajectories.
- Provided empirical data on AI commercialization potential in healthcare.
Conclusions
- The study introduces a novel deep learning self-supervised model for AI in healthcare trend analysis, enhancing accuracy and efficiency.
- Findings offer valuable guidance for researchers, policymakers, and industry professionals.
- Highlights potential transformation directions for future healthcare services through AI commercialization.
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