Advances in Artificial Intelligence in Healthcare

Dear Colleagues,

In the era of precision medicine, digital radiology and AI-driven radiomics analysis are at the forefront of healthcare transformation. This convergence enables personalized preventive and therapeutic interventions tailored to individual patient characteristics, minimizing the costs and side effects. Medical imaging plays a pivotal role by facilitating screening, early diagnosis, response evaluation and recurrence assessment. Radiomics extracts mineable, high-dimensional data from routine medical images to create an 'imaging phenotype,' which categorizes disease severity, predicts therapy response and forecasts patient outcomes. The aim of this research proposal is to investigate and promote the integration of digital radiology and AI-driven radiomics for precision medicine in healthcare.

Recent advances in smart computer vision have spurred significant interest in AI applications within radiology and in healthcare. While some AI software applications have received clinical approval, numerous unexplored possibilities remain. AI-driven computer-assisted detection and diagnosis, utilizing deep neural networks, automates tasks such as image classification and object localization. AI also extends its potential to clinical decision support, protocol optimization and workflow improvement. This Special Issue explores various network architectures suitable for digitalization in radiology and will be able to advance healthcare based on AI. We seek the support and engagement of experts, researchers and authors in this exciting endeavor to advance healthcare through the fusion of radiology and AI.

The convergence of artificial intelligence and digital imaging offers a unique opportunity to shift from qualitative to quantitative data, fostering the development of clinical decision support systems. Radiomics and deep learning, as two prominent quantitative imaging techniques, promise efficiency, minimal invasiveness and high accuracy. Challenges such as model explainability, feature reproducibility and sensitivity to imaging variations must be addressed before clinical implementation. This narrative review assesses the status of quantitative medical image analysis, outlines challenges in the field, proposes a robust radiomics analysis framework and discusses future prospects. The Special Issue we aim to create will consolidate research findings on AI in digital radiology and inspire a new era of precision medicine and smart healthcare.

• Radiology and AI-driven radiomics as the key for precise, cost-effective and personalized medicine;
• Medical imaging as the pivot for screening, diagnosis and treatment monitoring in this convergence;
• AI's role in digital radiology, particularly in computer-assisted detection and diagnosis;
• The 'imaging phenotype' via radiomics for disease categorization and outcome prediction;
• Deep convolutional neural networks for radiology tasks;
• Seeking research community support to advance digital radiology and AI in healthcare;
• Quantitative image analysis potential and Special Issue for precision medicine.

Prof. Dr. Kelvin K.L. Wong
Prof. Dr. Dhanjoo N. Ghista
Guest Editors

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