AI in Medical Imaging Informatics: Current Challenges and Future Directions

TLDR

Advances in medical imaging acquisition across modalities necessitate efficient data management strategies for AI‑driven big healthcare analytics. The paper reviews state‑of‑the‑art solutions in medical imaging informatics, discusses clinical translation, and outlines future directions for practice. It surveys imaging acquisition advances, data management needs, and AI/deep‑learning methods for disease classification and segmentation. In‑silico modeling, 3D reconstruction, and integrative analytics are shown to enhance diagnosis, prognosis, and treatment planning, advancing precision medicine in radiology and digital pathology.

Abstract

This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice. More specifically, it summarizes advances in medical imaging acquisition technologies for different modalities, highlighting the necessity for efficient medical data management strategies in the context of AI in big healthcare data analytics. It then provides a synopsis of contemporary and emerging algorithmic methods for disease classification and organ/ tissue segmentation, focusing on AI and deep learning architectures that have already become the de facto approach. The clinical benefits of in-silico modelling advances linked with evolving 3D reconstruction and visualization applications are further documented. Concluding, integrative analytics approaches driven by associate research branches highlighted in this study promise to revolutionize imaging informatics as known today across the healthcare continuum for both radiology and digital pathology applications. The latter, is projected to enable informed, more accurate diagnosis, timely prognosis, and effective treatment planning, underpinning precision medicine.