Artificial Intelligence-Driven Radiomics in the Diagnosis and Prognosis of Cancer

Dear Colleagues,

The convergence of Artificial Intelligence (AI) and medical imaging signifies a paradigm shift in healthcare diagnostics. Traditional diagnostic methodologies, reliant on the human eye and expertise, possess inherent limitations such as inter-observer variability. AI, especially deep learning architectures like Convolutional Neural Networks (CNNs), offers a remedy. These algorithms are adept at pattern recognition, extracting intricate features from images often imperceptible to clinicians. As a result, AI-driven models have shown remarkable proficiency in tasks ranging from tumour detection in radiographs to retinal disease classification in ophthalmic images. Moreover, AI's potential extends beyond mere diagnosis. Predictive modelling, image reconstruction, and workflow optimisation are facets undergoing rapid transformation under AI's influence. However, the marriage of AI and medical imaging is not without challenges: data privacy, algorithmic transparency, and clinical integration pose pertinent questions. This Special Issue delves deep into these advancements and hurdles, providing a holistic perspective on the current state and future trajectory of AI in the medical imaging domain. It underscores the pivotal role AI is positioned to play in shaping the next frontier of diagnostic medicine.

Dr. Shara WY Lee
Guest Editor

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