DEVELOPMENT OF AN INFORMATION SYSTEM ARCHITECTURE FOR HEALTHCARE INSTITUTIONS USING ARTIFICIAL INTELLIGENCE

The integration of artificial intelligence (AI) into medicine has significantly advanced diagnostics and treatment, particularly through the automated analysis of medical images such as magnetic resonance imaging (MRI), computed tomography (CT) scans, and X-rays. AI enhances diagnostic precision by identifying anomalies that may be overlooked by human interpretation. However, implementing AI in clinical workflows remains challenging, requiring alignment with existing systems and active physician involvement in decision-making. This study presents an information model for a medical system that integrates AI for image processing and interacts seamlessly with electronic medical records. The proposed architecture includes essential components such as medical imaging devices, cloud computing platforms, structured databases, and advanced AI algorithms, while maintaining the physician’s pivotal role throughout the diagnostic process. The findings reveal that the integration of AI significantly improves diagnostic accuracy and efficiency without compromising the physician’s role. The modular and scalable design supports adaptability to diverse medical needs, ensuring secure handling of patient data in compliance with international standards. This research concludes that AI-enabled systems streamline diagnostic workflows, facilitate precise and timely decision-making, and enhance the overall quality and safety of patient care. The proposed framework offers a blueprint for the effective integration of AI into clinical practice, paving the way for more accessible, efficient, and reliable healthcare delivery.