The field of AI-driven healthcare and audio generation is witnessing significant advancements, with a focus on developing innovative solutions for real-world problems. Researchers are exploring new architectures and training methods to improve the performance of AI models in resource-constrained settings, such as in hospitals with limited data. The use of self-supervised learning and transfer learning is becoming increasingly popular, enabling models to learn from limited data and adapt to new tasks. Additionally, there is a growing interest in developing lightweight and efficient models that can be deployed in resource-limited settings. Noteworthy papers include: The Rhythm In Anything, which presents a novel approach to generating high-fidelity drum recordings from rhythmic sound gestures. The Scattering Transformer is also a notable work, introducing a training-free transformer architecture for heart murmur detection that achieves competitive performance with state-of-the-art methods.