Harnessing Ai for Enhanced Laboratory Diagnostics: A Critical Examination

The integration of artificial intelligence (AI) into laboratory diagnostics is revolutionizing healthcare by enhancing diagnostic accuracy, efficiency, and patient outcomes. This critical examination explores the current state of AI applications in laboratory diagnostics, focusing on significant advancements in machine learning (ML), deep learning (DL), and natural language processing (NLP). The review highlights AI's role in various diagnostic fields, including pathology, radiology, and laboratory medicine, emphasizing its potential to automate routine tasks, improve diagnostic precision, and facilitate personalized medicine. Despite the promising benefits, challenges such as data quality, ethical considerations, and regulatory barriers remain significant. Addressing these challenges requires interdisciplinary collaboration, standardization of AI protocols, and robust regulatory frameworks. This review aims to provide a comprehensive understanding of AI's transformative impact on laboratory diagnostics, identify critical areas for improvement, and propose future research directions. By examining both the opportunities and limitations, this review contributes to the ongoing dialogue on AI's role in advancing healthcare diagnostics and improving patient care.