Machine Learning and Artificial Intelligence in Clinical Medicine

The Machine Learning and Artificial Intelligence in Clinical Medicine section publishes rigorously reviewed original research articles, comprehensive reviews, and informed perspectives focused on the application of advanced computational techniques within clinical practice. This section provides a multidisciplinary forum that connects clinicians, data scientists, and engineers to foster the responsible and effective integration of machine learning (ML) and artificial intelligence (AI), with the aim of enhancing patient outcomes and advancing medical knowledge.

In summary, this section aims to foster the translation of cutting-edge ML and AI research into tangible clinical benefits, ensuring that technological innovation aligns with the principles of patient safety, ethical integrity, and scientific rigor.

Subject Areas

This section invites manuscripts covering, but not limited to, the following topics:

• Clinical Applications of AI and ML: Creating and validating algorithms for diagnosing diseases, predicting outcomes, and developing treatment strategies across medical fields.
• Predictive and Personalized Medicine: Utilizing AI-based tools for risk assessment, patient-specific modelling, and targeted therapies.
• Medical Imaging and Signal Processing: Applying deep learning and other computational methods to image segmentation, classification, and analysis in areas such as radiology, pathology, and cardiology.
• Clinical Decision Support Systems: Incorporating AI models into clinical practices, with attention to real-world implementation and their effects on patient care.
• Digital Health and Wearable Technologies: AI solutions for remote monitoring, telemedicine, and ongoing health assessments.