Advancing Artificial Intelligence: The Role of Machine Learning, Natural Language Processing, and Electronic Health Records

Abstract

Healthcare organizations can enhance EHR administration by implementing AI alongside ML coupled with NLP in their medical systems. Research reviews present-day AI technology implementations that combine ML with NLP to show their diagnostic and patient care development and EHR system management capabilities.

Methods:
The research study scanned articles from January 2018 until August 2024 through PubMed and IEEE Xplore and Scopus databases which dealt with stroke diagnosis and treatment and management. The review evaluated research papers about AI/ML applications for stroke diagnosis from treatment through management evaluation in terms of their ethical aspects and technical requirements and regulatory limitations.

Results: AI and ML technologies generate better predictive patient outcomes while enhancing both diagnosis accuracy and individualized therapeutic designs. Clinical notes represented by unstructured data become more accessible through NLP applications that run within EHRs thereby supporting clinical decision support systems.

Conclusion:  The innovations in AI and ML and NLP drive modern healthcare systems through improved medical diagnostics and enhanced patient care although these systems decrease financial expenses. The current healthcare information system integration and algorithmic bias as well as data privacy issues continue to present obstacles. Research in the future should address obstacles in making AI-driven solutions accessible throughout various health care settings across different environments.