The field of health monitoring and signal recognition is witnessing significant advancements with the integration of artificial intelligence (AI) and machine learning (ML) techniques. Researchers are exploring innovative approaches to improve the accuracy and efficiency of disease detection, air quality monitoring, and signal recognition. Notably, the development of AI-powered systems that can analyze audio and visual data is gaining traction, enabling early detection and prevention of diseases. Furthermore, the application of self-supervised learning and transfer learning is enhancing the performance of these systems, especially in cases where labeled data is scarce. The use of digital twins and robotic testbeds is also being explored to improve the efficacy of air purification systems and mitigate airborne pathogen risks. Overall, these advancements have the potential to revolutionize the field of health monitoring and signal recognition, enabling more accurate and efficient disease detection and prevention. Noteworthy papers include: DeepGB-TB, which proposes a risk-balanced cross-attention gradient-boosted convolutional network for rapid and interpretable tuberculosis screening. AeroSafe, which introduces a novel approach to enhancing indoor air purification systems using a robotic cough emulator testbed and digital-twins-based aerosol residence time analysis. CoughViT, which proposes a self-supervised vision transformer for cough audio representation learning to enhance diagnostic performance in tasks with limited data. A Foundation Model for DAS Signal Recognition, which presents a foundational model for DAS signal recognition based on a Masked Autoencoder and Visual Prompt Tuning for downstream tasks. Perch 2.0, which expands a pre-trained model for bioacoustics to a large multi-taxa dataset and obtains state-of-the-art performance on several benchmarks.