Core Viewpoint - The article discusses the urgent need for real-time health monitoring technologies in light of the alarming statistic that nearly 18 million people die from cardiovascular diseases each year, accounting for 32% of global deaths. It highlights the challenges faced by traditional cardiovascular signal monitoring, particularly the trade-off between signal quality and patient comfort [2]. Group 1: Need for AI Completion Technology - Cardiovascular signals, such as PPG, ECG, and BP, are inherently interconnected and complementary, reflecting the health status of the cardiovascular system. However, obtaining complete and high-quality multimodal signals during monitoring is rare [6]. - Wearable devices are prone to interference from motion artifacts, power line disturbances, and muscle contractions, while clinical monitoring is hindered by the high cost of equipment and patient discomfort, making long-term use difficult [6]. Group 2: Breakthrough with UniCardio - UniCardio, a multimodal diffusion transformer model, is developed to "complete" missing or low-quality cardiovascular signals. Its generated signals perform comparably to real signals in detecting abnormal health conditions and assessing vital signs, while ensuring interpretability for human experts [3][8]. - The core innovation of UniCardio lies in unifying the generation tasks of various cardiovascular signals into a single framework, utilizing advanced conditional diffusion models to iteratively reconstruct the required signals [8]. Group 3: Performance of Generated Signals - UniCardio was pre-trained on a dataset containing 339 hours of multimodal recordings and evaluated across various generation tasks, outperforming specialized baseline models in denoising, interpolation, and conversion tasks [12]. - The generated signals exhibit excellent performance in waveform morphology, spectral features, and clinical interpretability, particularly excelling in challenging tasks such as PPG interpolation and ECG conversion [12]. Group 4: Practical Applications in Medical Diagnosis - The reliability of AI-generated signals for health monitoring and medical diagnosis is affirmed through evaluations in real scenarios, showing that denoised signals achieve accuracy, sensitivity, and specificity levels comparable to real signals [14]. - UniCardio significantly improves heart rate estimation and blood pressure assessment, demonstrating its clinical effectiveness and interpretability through the generation of typical abnormal diagnostic features [14]. Group 5: Future Implications of AI-Generated Signals - The emergence of UniCardio signifies a paradigm shift in cardiovascular signal processing, providing a universal and scalable framework for multimodal physiological signal generation [16]. - UniCardio is expected to enhance personalized health monitoring by enabling accurate data collection from wearable signals and synthesizing cardiovascular signals that cannot be captured by wearable sensors [18]. - The technology has broader applications beyond cardiovascular health, potentially impacting psychological and cognitive science research, where physiological signals are used for stress and emotion assessment [18].