Integration of Laboratory and Radiology Data

Background _ Artificial intelligence (AI), particularly deep learning, has the potential to significantly transform the field of radiology. While there has been considerable attention in popular literature on the possible applications of AI, such as autonomous vehicles, it is suggested that the healthcare sector would experience the earliest and most significant impact from AI. Aim of Work – This paper aims to provide an overview of the current state of AI in radiology, including its potential benefits and challenges, and to discuss the ethical, technical, and practical considerations for its implementation in clinical practice. Methods – A comprehensive literature review was conducted to identify relevant studies and articles on AI in radiology. The review included scientific publications, conference proceedings, and reports from reputable organizations and institutions. Results – The findings of the literature review suggest that AI has the potential to enhance the efficiency, accuracy, and precision of medical image analysis and interpretation. AI algorithms can assist radiologists in tasks such as image segmentation, lesion detection, and disease classification. However, several challenges need to be addressed before AI can be widely adopted in clinical practice

Applications of artificial intelligence · Clinical Practice · Data science · Deep learning · Field (mathematics · Health care · Medical imaging · Medical physics · Patient care · Radiology · Artificial Intelligence in Healthcare and Education · Computer Science · Medical Imaging and Analysis · Medicine · Radiomics and Machine Learning in Medical Imaging · Artificial Intelligence