The field of photoplethysmography (PPG)-based physiological monitoring is rapidly advancing, with a focus on improving the accuracy and robustness of PPG signals for various applications. Recent developments have centered around leveraging deep learning techniques, physics-informed models, and hybrid approaches to enhance the reliability and interpretability of PPG-based measurements. Notably, innovations in time-to-event modeling, patient-identity invariant features, and pseudo-lab alignment have improved the performance of PPG-only cardiac arrest prediction systems. Furthermore, the integration of 6G/WiFi integrated sensing and communication (ISAC) systems has enabled non-contact digital twin synthesis of PPG signals, while physics-grounded harmonic attention systems have enhanced remote PPG measurement accuracy. Additionally, advances in editing physiological signals in videos and translating wearable PPG to 12-lead ECG have expanded the possibilities for PPG-based monitoring. Some noteworthy papers in this area include: Wav2Arrest 2.0, which proposes three orthogonal improvements to improve PPG-only cardiac arrest systems. Radio-PPG, which introduces a novel non-contact method for digital twin PPG signal synthesis using 6G/WiFi ISAC signals. PHASE-Net, which presents a physics-informed rPPG paradigm derived from the Navier-Stokes equations of hemodynamics. Editing Physiological Signals in Videos, which proposes a learned framework for editing physiological signals in videos while preserving visual fidelity. Translation from Wearable PPG to 12-Lead ECG, which introduces a demographic-aware diffusion framework to generate clinically valid 12-lead ECG from PPG signals. Inferring Optical Tissue Properties from Photoplethysmography, which proposes a biophysical model relating PPG signals to interpretable physiological and optical parameters.