Divya Dinesh Joshi, Kaaviyashri Saraboji, Asiya Tasleema Shaik, Krishna Sai Kiran Sakalabaktula, Shreya Purohit, Swathi Godugu, Jasmine Nirmal, Riya Kayarkar, Bernardo Henrique Mendes Correa, Namratha Gangidi, Pratibha Yadav, Jyoti Yadav, Farshi Farook, Jieun Lee, Mohammed Naveed Shariff, Divaakar Siva Baala Sundaram, Gayathri Yerrapragada, Poonguzhali Elangovan, Thangeswaran Natarajan, Jayarajasekaran Janarthanan, Sancia Mary Jerold Wilson, Shiva Sankari Karuppiah, Zoma Abbasi, Swetha Rapolu, Simardeep Kaur Bumrah, Rashi Bilgaiyan, Chandra Rupini Premkumar, Anmolpreet Kaur, Lakshmi Sree Pugalenthi, Divyanshi Sood, Suganti Shivaram, Vivek N Iyer, Vidhya C M, Simin Masihi, Scott A Helgeson, Shivaram P Arunachalam
Photoplethysmography (PPG) is a non-invasive optical technique commonly used to measure heart rate and oxygen saturation, but its waveform contains additional physiological information that can be analyzed using artificial intelligence (AI). This narrative review summarizes the emerging applications of AI-based PPG in cardiovascular, respiratory, sleep, hemodynamic, pregnancy-related, and portal-hypertension assessment, with the aim of evaluating its potential beyond conventional monitoring and identifying barriers to clinical translation. The literature search was conducted using PubMed, Google Scholar, IEEE Xplore, ScienceDirect, and SpringerLink. Additional relevant studies were identified through screening the reference lists of included articles. Studies published between 2002 and 2026 were identified to capture the development of PPG from conventional monitoring to newer AI-based applications. Human studies were prioritized, while relevant computational, simulated, synthetic, ex vivo, and technical studies were also included. Studies unrelated to PPG, duplicates, and studies with limited relevance were excluded. A total of 96 references were included, covering AI approaches such as convolutional and deep neural networks, ensemble methods, transfer learning, U-Net, generative adversarial networks, and Transformer-based models. Overall, the reviewed evidence suggests that AI-based PPG may support blood pressure estimation, atrial fibrillation detection, sleep and respiratory monitoring, vascular aging assessment, pulmonary hypertension screening, preeclampsia assessment, volume-status and compensatory-reserve assessment, and exploratory assessment related to portal hypertension. However, clinical translation remains limited by motion artifacts, sensor and measurement-site variability, skin-pigmentation-related bias, physiological and environmental influences, heterogeneous methods, limited external validation, and inconsistent clinical and regulatory standards.