The integration of laboratory and radiology data: A holistic approach to patient diagnostics

Ola Yousef Fadan; Huda Obaid Alanzi; Abdullah Saad Abunaian; Mohammed Amaash Alanizi; Ahlam Nazeh Alenezi; Musab Abdulgader Alfares; Khalid Abdulrahman Alsharif; Motaeb Saqer Alenazi; Zainab Ali AlQarni; Ahmed Mufleh Alenazi; Abdulaziz Radi Alanazi; Mohammed Abdullah Shujaa

doi:10.53730/ijhs.v2nS1.15014

Authors

Ola Yousef Fadan KSA, National Guard Health Affairs
Huda Obaid Alanzi KSA, National Guard Health Affairs
Abdullah Saad Abunaian KSA, National Guard Health Affairs
Mohammed Amaash Alanizi KSA, National Guard Health Affairs
Ahlam Nazeh Alenezi KSA, National Guard Health Affairs
Musab Abdulgader Alfares KSA, National Guard Health Affairs
Khalid Abdulrahman Alsharif KSA, National Guard Health Affairs
Motaeb Saqer Alenazi KSA, National Guard Health Affairs
Zainab Ali AlQarni KSA, National Guard Health Affairs
Ahmed Mufleh Alenazi KSA, National Guard Health Affairs
Abdulaziz Radi Alanazi KSA, National Guard Health Affairs
Mohammed Abdullah Shujaa KSA, National Guard Health Affairs

Keywords:

Artificial intelligence, Radiology, Medical imaging, Machine learning, Deep learning

Abstract

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.

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