Interdisciplinary Synergy Between Radiologist and ML Engineer for augmented Whole‐Body MRI Interpretation in the AI Era

The concept of an AI-assisted comprehensive, accessible, and safe diagnostic modality has gained significant attention in recent years, reflecting the evolving needs and expectations of contemporary society. Current advancements in medical imaging have enabled high-resolution, detailed, non-invasive whole body image reconstruction, allowing for more accurate lesion detection and characterization and, therefore, facilitating more effective, targeted treatment. Practical employment of such solutions can become feasible and clinically useful through several key technologies that leverage state of the art adaptive algorithms. From this perspective, we describe the theoretical principles of a conceptual diagnostic network for ΑΙ-supported whole-body (WB) MRI analysis. We propose a framework that integrates well-established and widely studied AI tools, including Convolutional Neural Networks for image analysis, Natural Language Generation for structured reporting and Federated Learning for decentralized model training, into a multimodal diagnostic tool. These elements are selected for their strong academic maturity, broad validation across medical imaging studies, and proven compatibility with modern WB-MRI scanners. The expert-in-the-loop precept places the radiologist at the helm of this system, where they maintain clinical oversight and accountability for all decision-making, while also contributing to the ongoing readjustment of the functional algorithm. Inherently dependent on actual data availability, the complexity of the process raises issues related to patient psychological burden, as well as legality, fairness, and ethical governance

Convolutional neural network · Deep learning · Leverage (statistics) · Medical imaging · Modalities · Modality (human–computer interaction) · Pixel · Precept · Artificial Intelligence in Healthcare and Education · COVID-19 diagnosis using AI · Radiomics and Machine Learning in Medical Imaging