Description

Artificial intelligence (AI) is transforming medical imaging, introducing groundbreaking innovations that enhance diagnostic accuracy, streamline workflows, and enable truly personalized medicine. This special issue explores the latest AI-driven advancements in precision imaging, focusing on how intelligent algorithms improve prognostication, optimize decision-making, and unlock deeper insights into complex diseases.

With AI-powered imaging techniques, clinicians can extract quantitative data, refine disease prediction models, and tailor treatment strategies to individual patient profiles. From deep learning-based image segmentation to AI-driven radiomics and radiogenomics, these technologies are redefining clinical diagnostics and bringing us closer to a future where precision imaging plays a pivotal role in personalized healthcare.

This collection will present original research, technical innovations, clinical validations, and expert opinions on the role of AI in shaping modern diagnostics, fostering interdisciplinary collaboration among researchers, imaging specialists, healthcare professionals, and policymakers.

Aim and Scope

• Highlight cutting-edge AI applications in medical imaging across various clinical domains.

• Showcase innovative methodologies for image acquisition, processing, interpretation, and decision support.

• Explore AI's impact on precision diagnostics, prognostication, and personalized medicine.

• Discuss clinical validation, regulatory pathways, ethical considerations, and deployment challenges in AI-driven imaging.

• Strengthen interdisciplinary collaboration among AI researchers, radiologists, pathologists, nuclear medicine experts, clinicians, and healthcare policymakers.

• Provide forward-looking perspectives on AI-powered diagnostics and its evolving role in precision medicine.

Key Topics of Interest:

• AI-enhanced imaging techniques: MRI, CT, PET, ultrasound, and digital pathology.

• Deep learning models for automated image segmentation, detection, and classification.

• AI in radiomics and radiogenomics: Predictive modeling for personalized treatment.

• Explainable AI (XAI) to improve trustworthiness in clinical decision-making.

• Multimodal data fusion: Integrating imaging with genomic, clinical, and laboratory data.

• Automated image acquisition and reconstruction technologies.

• Real-world implementation studies and clinical validation of AI applications.

• Future perspectives on AI-driven precision medicine and personalized healthcare.