Revolutionizing Healthcare Through AI: Harnessing Machine Learning, Natural Language Processing, and EHRs for Smarter Patient Care and Efficient Medical Systems

Abstract

Objective: Healthcare organizations 
have the opportunity to enhance EHR 
management by incorporating AI 
alongside machine learning and natural language processing in their 
healthcare systems. Research showcases existing uses of AI that 
combine machine learning with natural language processing, 
illustrating their success in diagnostics, improving patient care, and 
managing EHR systems.
Methods: The research investigation analyzed publications 
from January 2018 to August 2024 found in the PubMed, IEEE 
Xplore, and Scopus databases that dealt with the diagnosis, 
treatment, and management of stroke. The review evaluated 
academic articles regarding the application of AI/ML in the 
diagnosis, treatment, and assessment of stroke management, 
with an emphasis on ethical implications, technical details, and 
regulatory limitations.
Results: AI and machine learning advancements enhance 
patient outcomes by improving predictions, refining 
diagnostic accuracy, and customizing treatment plans to meet 
individual requirements. Natural language processing tools 
incorporated into electronic health records make clinical notes, 
typically unstructured, easier to understand, thereby 
supporting clinical decision-making systems.
Conclusion: The progress in AI, machine learning, and natural 
language processing significantly impacts modern healthcare 
by improving diagnostic precision and patient services, all 
while lowering expenses. Nonetheless, issues like the 
integration of health information systems, bias in algorithms, 
and data privacy concerns continue to exist. Future studies 
should aim to address these problems to make certain that AIbased solutions are accessible in various healthcare settings.