Influence of progress in artificial intelligence on radiology's future: A two-fold view on advantages and challenge

Niusha Zare 1, *, Marzieh Mirzaie Loor 2, Sarina Badakhshan 3, Zahra Ghoncheh 4 and Niloofar Ghadimi 5

1 Post Graduate Student, Department of Oral and Maxillofacial Radiology, Azad University of Tehran Dental Branch, Tehran, Iran.
2 MSc in Orthodontics, Faculty of Dentistry, School of Dentistry, Urmia University of Medical Sciences, Urmia, Iran.
3 research assistant, University of California Los Angeles, CA, USA.
4 Department of Oral and Maxillofacial Radiology, International Campus, School of Dentistry, Tehran University of Medical University, Tehran, Iran.
5 MSc in Oral and Maxillofacial Radiology, Department of Oral and Maxillofacial Radiology, Dental School, Islamic Azad University of Medical Sciences, Tehran, Iran.
 
Review
World Journal of Biology Pharmacy and Health Sciences, 2024, 17(01), 215–219
Article DOI: 10.30574/wjbphs.2024.17.1.0013
Publication history: 
Received on 02 December 2023; revised on 17 January 2024; accepted on 20 January 2024
 
Abstract: 
The assimilation of Artificial Intelligence (AI) into radiology marks a significant milestone in medical diagnostic procedures. This article examines the varied effects of AI developments in radiology, considering both the advantageous prospects and the possible challenges. AI's application in radiology, primarily through machine learning and deep learning techniques, offers unprecedented improvements in diagnostic accuracy, efficiency, and patient care. However, these advancements also bring forth significant challenges, including ethical dilemmas, potential job displacement, and data security concerns. Through a balanced examination of current literature and case studies, this editorial aims to provide a comprehensive understanding of AI's role in reshaping radiology. It discusses how AI can revolutionize diagnostic practices while addressing the critical issues accompanying its implementation. The goal is to present a nuanced perspective, acknowledging AI's potential to enhance radiology, alongside the importance of addressing the complexities of this technological evolution.
 
Keywords: 
Artificial intelligence; Radiology; Diagnosis
 
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