Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care

Dear Colleagues,

In the realm of modern healthcare, the fusion of artificial intelligence (AI) with medical imaging has emerged as a cornerstone for revolutionizing screening, diagnostics, and clinical care across a spectrum of diseases. This evolution is particularly pivotal at a time when the global healthcare industry is striving to enhance accuracy, efficiency, and accessibility in patient care services.

The application of AI and machine learning (ML) in medical imaging extends beyond mere automation; it introduces a new era of precision medicine where diagnostics and treatment are highly tailored to individual patient profiles. Innovations in deep learning (DL) algorithms have shown exceptional promise in rapidly assessing complex imaging data, serving as an adjunct to expert clinical judgment, and significantly minimizing variability in diagnostic interpretations. These technological advancements have the potential not only to streamline workflow, but also to facilitate the early detection of conditions, thereby profoundly impacting treatment outcomes and patient prognosis.

However, the journey towards fully integrating AI into clinical settings is fraught with challenges. The effectiveness of these AI and ML methodologies heavily depends on the quality, volume, and annotation of training datasets. The scarcity of well-curated and annotated medical images, compounded by the inherent imbalance between cases and controls, poses a significant hurdle in model training and validation. Moreover, the presence of noisy and incomplete data in real-world medical datasets can lead to unreliable algorithm performance and potential biases.

The Special Issue entitled "Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care" endeavors to illuminate the groundbreaking advancements ushered in by AI/ML across various medical imaging modalities. This edition seeks to explore innovative AI/ML methodologies tailored for image-based screening, diagnostics, and clinical management, overcoming the barriers of limited and imperfect medical data. We are particularly interested in original research and comprehensive reviews that present novel methods for training ML/DL models capable of navigating the complexities of real-world medical imaging data. Through this compilation, we aim to share insights into state-of-the-art AI applications that hold the promise of making substantial contributions to overcoming global health challenges.

We warmly invite contributions from researchers and practitioners pioneering in this field to submit their novel and unpublished work in this area.

Dr. Sameer Antani
Dr. Zhiyun Xue
Dr. Sivaramakrishnan Rajaraman
Guest Editors

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