AI in Medical Imaging Informatics: Current Challenges and Future Directions.

Area of Science:

• Medical Imaging Informatics
• Artificial Intelligence in Healthcare
• Radiology and Digital Pathology

Background:

• The rapid growth of medical imaging data necessitates advanced informatics solutions.
• Artificial intelligence (AI) and deep learning are transforming medical data analysis.
• Integrating diverse data sources is crucial for comprehensive healthcare analytics.

Purpose of the Study:

• To review state-of-the-art research in medical imaging informatics.
• To discuss clinical translation and future directions for AI in healthcare.
• To highlight advances in disease classification, segmentation, and in-silico modeling.

Main Methods:

• Review of contemporary and emerging algorithmic methods, focusing on AI and deep learning.
• Analysis of medical imaging acquisition technologies and data management strategies.
• Documentation of clinical benefits from 3D reconstruction and in-silico modeling.

Main Results:

• AI and deep learning are the de facto approaches for disease classification and segmentation.
• Efficient medical data management is essential for big healthcare data analytics.
• Advances in 3D reconstruction and visualization offer clinical benefits.

Conclusions:

• Integrative analytics approaches will revolutionize imaging informatics across healthcare.
• AI-driven insights promise more accurate diagnosis and prognosis in radiology and digital pathology.
• These advancements support precision medicine through improved treatment planning.