A new article titled “AI and Medical Imaging Informatics: Current Challenges and Future Directions” has been published in the IEEE Journal of Biomedical and Health Informatics
A new article by A. Panayides, A. Amini, A. Sharma, S. Tsaftaris, A. Young, D. Foran, N. Do, S. Golemati, T. Kurc, K. Huang, K. Nikita, B. Veasey, M. Zervakis, J. Saltz, C. Pattichis, titled “AI and Medical Imaging Informatics: Current Challenges and Future Directions”, has been published in the IEEE Journal of Biomedical and Health Informatics.
This paper reviews state-of-the-art research 18 solutions across the spectrum of medical imaging informatics, 19 discusses clinical translation, and provides future directions for 20 advancing clinical practice. More specifically, it summarizes 21 advances in medical imaging acquisition technologies for different 22 modalities, highlighting the necessity for efficient medical data 23 management strategies in the context of AI in big healthcare data analytics. It then provides a synopsis of contemporary and 24 emerging algorithmic methods for disease classification and 25 organ/ tissue segmentation, focusing on AI and deep learning 26 architectures that have already become the de facto approach. The 27 clinical benefits of in-silico modelling advances linked with 28 evolving 3D reconstruction and visualization applications are 29 further documented. Concluding, integrative analytics approaches driven by associate research branches highlighted in this study 30 promise to revolutionize imaging informatics as known today 31 across the healthcare continuum for both radiology and digital 32 pathology applications. The latter, is projected to enable informed, 33 more accurate diagnosis, timely prognosis, and effective treatment 34 planning, underpinning precision medicine.