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The integration of Electronic Health Records (EHR) and Artificial Intelligence (AI) represents a pivotal advancement in modern healthcare delivery. Originating in the mid -20th century, EHRs have evolved from simple digital platforms to comprehensive repositories of patient health data. In parallel, AI technologies have emerged as transformative tools for analyzing vast datasets and improving clinical decision -making processes. AI integration into EHR systems offers numerous benefits, including efficient diagnosis and treatment planning, predictive analysis for risk stratification, enhanced data management, streamlined workflows, reduced physician burnout, and improved clinical trial matching. However, challenges such as data quality, privacy concerns, interoperability issues, cost considerations, transparency, and user acceptance must be addressed for successful implementation. Despite these challenges, the integration of AI into EHR systems holds immense potential to revolutionize healthcare delivery, improve patient outcomes, and reduce healthcare disparities. Responsible implementation, ongoing research, and collaboration between stakeholders are essential to maximize the benefits of AI -powered EHR systems while mitigating risks.
S. Saranya (Fri,) studied this question.
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