The field of AI-driven diagnostic tools and training data attribution is experiencing significant advancements, driven by innovations in computer vision, machine learning, and edge-based technologies. Researchers are focusing on developing efficient, scalable, and energy-conscious solutions for real-time diagnosis and treatment, particularly in resource-constrained environments. Notable efforts include the use of pre-trained CNN models, transfer learning techniques, and XAI methods to enhance model interpretability and diagnostic reliability. Additionally, there is a growing emphasis on improving training data attribution methods, including the development of more accurate inverse Hessian-vector product approximations. These advancements have the potential to drive meaningful improvements in healthcare outcomes, especially in underserved regions. Noteworthy papers include:

• Better Training Data Attribution via Better Inverse Hessian-Vector Products, which introduces an algorithm for accurate inverse Hessian-vector product approximation.
• Computer Vision for Real-Time Monkeypox Diagnosis on Embedded Systems, which presents an AI-driven diagnostic tool optimized for deployment on embedded systems.