From Data to Diagnosis: The Integration of AI in Electronic Health Records
Schlagwörter:
Artificial Intelligence, Electronic Health Records, machine learning, diagnostic tools, predictive analytics, data privacy, algorithmic bias, decision support systems, healthcare automation, personalized medicine.Abstract
The integration of Artificial Intelligence (AI) in healthcare has led to significant advancements in the utilization of Electronic Health Records (EHRs) for clinical decision-making. This review explores the transformative impact of AI technologies on EHR systems, particularly in their role in improving the accuracy, efficiency, and personalization of diagnoses. AI algorithms, such as machine learning models and neural networks, have enhanced the ability to analyze vast datasets within EHRs, facilitating more precise diagnostic tools. These tools leverage historical patient data, clinical notes, lab results, and imaging data to predict outcomes, suggest treatments, and identify early signs of diseases that might otherwise remain undetected.
