Abstract

Laboratory diagnostics serve as the backbone of clinical decision-making, yet traditional systems in hospital environments often suffer from inefficiencies, manual data entry errors, and integration challenges. This paper presents a novel AI-based automation framework designed to enhance the accuracy, speed, and reliability of laboratory diagnostics within hospital information management systems (HIMS). By embedding an artificial intelligence layer into the diagnostic workflow, the proposed system supports automated data ingestion, diagnostic inference, and real-time clinical alerts. The framework is architected with modular components including a lab interface, diagnostic inference engine, and alerting system, all compliant with HL7/FHIR standards to ensure interoperability and data privacy. Comprehensive evaluation using simulated and historical hospital datasets demonstrates significant improvements in diagnostic turnaround time, accuracy, and system uptime. Case studies such as automated blood panel interpretation and digital pathology further validate the framework’s utility and adaptability. While current limitations include scalability and ethical concerns related to AI transparency, the paper outlines future directions involving adaptive learning models, cloud-native deployment, and IoT biosensor integration. This work contributes a scalable, standards-aligned, and clinically relevant AI architecture capable of transforming diagnostic workflows in modern healthcare environments.