AI-Driven Applications for Diagnostic, Clinical Decision Support and Prevention in Cardiology, Orthopedics, and Oncology

Artificial Intelligence (AI) is rapidly transforming the healthcare landscape, enabling smarter diagnostics, continuous monitoring, prevention and advanced clinical decision support. This paper presents a unified overview of several AI-driven research initiatives carried out within the SoWide/Ibis Lab targeting critical areas in medicine, including cardiology, orthopedics, and oncology. We describe lightweight machine learning and deep learning models for real-time noise filtering in ECG signals, enhancing the diagnostic capabilities of wearable devices. We then present a novel pipeline for detecting and classifying periprosthetic fractures in hip arthroplasty using automated Gruen zone classification. Ongoing efforts include the development of an AI-powered chatbot for interpreting lung cancer screening results and a computer vision-based approach for assessing lymphadenectomy performance via anatomical landmark detection. These projects exemplify practical, efficient, and scalable AI solutions tailored for both clinical environments and real-world settings, contributing to improved patient outcomes and reduced healthcare workload.