Editorial on Special Issue “Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care”.
Rajaraman S, Xue Z, Antani S
Diagnostics. 2024; 14(17):1984. https://doi.org/10.3390/diagnostics14171984.
Abstract:
In an era of rapid advancements in artificial intelligence (AI) technologies, particularly in medical imaging and natural language processing, strategic efforts to leverage AI’s capabilities in analyzing complex medical data and integrating it into clinical workflows have emerged as a key driver of innovation in healthcare. As the global healthcare landscape shifts towards precision medicine, the incorporation of AI into image-based screening, diagnostics, and clinical care is becoming increasingly crucial, offering significant opportunities to enhance patient outcomes and improve healthcare delivery. Advancements in AI, particularly in deep learning (DL) [1], a subset of machine learning (ML), have significantly advanced medical imaging, and accurate analyses. These developments represent a paradigm shift, where AI not only automates processes but also enhances precision in diagnostics, allowing for personalized interventions tailored to individual patient needs. However, integrating AI into clinical workflow presents several challenges that must be carefully addressed. The effectiveness and generalizability of AI-driven solutions can be adversely affected by the data quality, imbalance, and limited availability of well-annotated training data [2]. The imbalance in datasets [3] refers to pathological cases in various grades of severity that are significantly smaller compared to healthy controls. These factors complicate the development of robust, reliable, bias-free, and generalizable models and limit their use in real-world clinical environments. As Guest Editors of the Special Issue “Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care”, we present a collection of research findings addressing some of these challenges, showcasing cutting-edge advancements in AI applications in healthcare. This Special Issue not only highlights technological progress but also explores the practical implications of AI in clinical practice, offering insights that are critical for the ongoing evolution of the field. We believe that the contributions within this Special Issue will serve as a catalyst for future research and encourage the broader medical and scientific communities to fully explore AI’s potential in transforming patient care.
Rajaraman S, Xue Z, Antani S. Editorial on Special Issue “Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care”.
Diagnostics. 2024; 14(17):1984. https://doi.org/10.3390/diagnostics14171984.
PMID | PMCID