AI in Medical Imaging Informatics: Current Challenges and Future Directions
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JBHI.2020.2991043, IEEE Journal of Biomedical and Health Informatics > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 1 AI in Medical Imaging Informatics: Current Challenges and Future Directions A. S. Panayides, Senior Member, IEEE, A. Amini, Fellow, IEEE, N.D. Filipovic, IEEE, A. Sharma, IEEE, S. A. Tsaftaris, Senior Member, IEEE, A. Young, IEEE, D. Foran, IEEE, N. Do, S. Golemati, Member, IEEE, T. Kurc, K. Huang, IEEE, K. S. Nikita, Fellow, IEEE, B.P. Veasey, IEEE Student Member, M. Zervakis, Senior Member, IEEE, J.H. Saltz, Senior Member, IEEE, C.S. Pattichis, Fellow, IEEE Abstract—This paper reviews state-of-the-art research I. INTRODUCTION solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice. More specifically, it summarizes EDICAL imaging informatics covers the application of advances in medical imaging acquisition technologies for different M modalities, highlighting the necessity for efficient medical data information and communication technologies (ICT) to medical management strategies in the context of AI in big healthcare data imaging for the provision of healthcare services. A wide- analytics. It then provides a synopsis of contemporary and spectrum of multi-disciplinary medical imaging services have emerging algorithmic methods for disease classification and evolved over the past 30 years ranging from routine clinical organ/ tissue segmentation, focusing on AI and deep learning practice to advanced human physiology and pathophysiology.
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