Position Paper: Artificial Intelligence in Medical Image Analysis: Advances, Clinical Translation, and Emerging...

Area of Science:

• Medical Imaging and Artificial Intelligence
• Clinical Translation of AI Technologies
• Healthcare Workflow Integration

Background:

• AI adoption in medicine faces challenges.
• Recent AI advancements offer solutions for medical applications.
• The paper reviews AI progress in medical image and video analysis.

Purpose of the Study:

• To review recent advances in AI for medical imaging.
• To outline emerging AI paradigms for healthcare.
• To highlight pathways for successful clinical translation of AI.

Main Methods:

• Review of hybrid Convolutional Neural Network (CNN) Transformer architectures.
• Application of foundation and generative AI models for transfer learning.
• Implementation of federated learning for privacy-preserving collaboration.
• Adoption of explainable and trustworthy AI approaches.

Main Results:

• State-of-the-art results in segmentation, classification, reconstruction, synthesis, and registration.
• Enabling transfer learning for smaller datasets with limited ground truth.
• Supporting privacy-preserving multi-institutional collaboration.
• Fostering clinician trust and regulatory compliance through explainable AI.

Conclusions:

• AI developments are paving the way for integration into radiology, pathology, and healthcare.
• Hybrid CNN-Transformer models show significant promise.
• Foundation models and federated learning address key adoption barriers.
• Explainable AI is crucial for ethical and trusted deployment.