P., Suthanthira Devi and Revathy, G and P., Mohana Priya Priya (2025) Empowering Healthcare With AI-Driven Transformer Models:. In: Transformative Role of Transformer Models in Healthcare. IGI Global Scientific Publishing, pp. 343-358. ISBN 9798337320403
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Abstract
Suthanthira Devi P. SRM Institute of Science and Technology, India https://orcid.org/0000-0002-7386-821X G. Revathy Vels Institute of Science, Technology, and Advanced Studies, India Mohana Priya Priya P. Vels Institute of Science, Technology, and Advanced Studies, India Empowering Healthcare With AI-Driven Transformer Models
The BERT and GPT transformer models are leading the way in artificial intelligence (AI) in healthcare. Personalized medicine and clinical decision-making can be simplified with these models because they excel at handling natural language and unstructured data. Healthcare generates enormous amounts of data every day, including electronic health records (EHRs), imaging, and biomedical literature. It is difficult for clinicians and researchers to extract actionable insights from this unstructured information due to its unstructured nature. The transformer model can process and analyze this data efficiently because it has self-attention mechanisms and context understanding. In drug discovery, transformers analyze vast biomedical datasets to speed repurposing. They enable multi-modal assessments through text, image, and genomic data integration in diagnostics. Through AI-driven solutions, transformers will revolutionize healthcare by improving patient care, reducing workloads, and accelerating innovation.
chapter 12 8 29 2025 343 358 10.4018/979-8-3373-2038-0.ch012 20251002104114 https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/979-8-3373-2038-0.ch012 https://www.igi-global.com/viewtitle.aspx?TitleId=391129
| Item Type: | Book Section |
|---|---|
| Subjects: | Computer Science Engineering > Artificial Intelligence |
| Domains: | Computer Science Engineering |
| Depositing User: | Mr Sureshkumar A |
| Date Deposited: | 26 Dec 2025 08:23 |
| Last Modified: | 26 Dec 2025 08:23 |
| URI: | https://ir.vistas.ac.in/id/eprint/11895 |


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