Artificial Intelligence in Radiology: Current Applications, Ethical Challenges, and Future Directions

Abstract

Artificial Intelligence is changing radiology by offering novel approaches to enhance diagnostic
accuracy and operational efficiency; however, this transformative potential also introduces significant
ethical considerations regarding accountability, potential biases, and the prudent deployment of
autonomous systems in diverse clinical environments. This chapter brings the narrative review of AI
application across various medical imaging modalities such as Diagnostic radiology, Computed
tomography, Magnetic resonance imaging, Mammography to examin the role and importance of deep
learning in image reconstruction, image analysis, pathology identification, nodule detection, leasion
detection, tumor idnetification, automated workflow priortization. Sensitivity of detection of various
anomilies such as pneumonia, breast cancer, intercranical pathologies has significantly increased in
recent years.
However along with these advantages, the widespread integration of AI in clinical radiology presents
intricate challenges, including data privacy concerns, the potential for systemic errors, and the
imperative for extensive research to guide optimal deployment in varied clinical settings . Practical
barriers such as implementation cost, integration with current technology, limited generalizability also
affect adoptation of AI driven technology in medical imaging field. AI should be used as decision
support tools, augmenting human expertise rather than replacing it, to ensure ethical and clinically
sound diagnostic processes under government regulatories to ensure ethical, safe usage.