Saraboji, Kaaviyashri

Background/Objectives: Glucagon-like peptide-1 receptor agonists (GLP-1RAs) have emerged as a promising therapeutic option for women with polycystic ovary syndrome (PCOS). Clinical trials and data demonstrate significant improvements in body weight, insulin resistance, hyperandrogenism, and, in some studies, menstrual regularity and reproductive outcomes. However, the current evidence base remains disproportionately focused on short-term metabolic and anthropometric endpoints, while outcomes most relevant to patients—including treatment satisfaction, gastrointestinal tolerability, psychological well-being, body image, fertility planning, financial burden, and long-term adherence—remain inadequately characterized. Methods: We searched PubMed and Google Scholar for English-language literature published between 2021 and 2026, using structured keyword searches. This narrative review evaluates the literature on GLP-1RA use in PCOS beyond weight-focused outcomes, examining mechanisms of action, comparative tolerability across agents, and patient-centered domains including quality of life, eating behavior, and reproductive planning. Results: The published literature reveals a substantial gap between trial-reported completion rates and treatment success, and actual persistence and patient satisfaction. This discrepancy is particularly consequential in PCOS, where treatment decisions intersect with high baseline rates of depression, anxiety, and body image disturbance, and where ovulatory benefits must be weighed against mandatory discontinuation before conception. Conclusions: We identify a systematic absence of validated patient-reported outcome measures, standardized core outcome sets, and long-term follow-up data after treatment withdrawal. This review proposes a research agenda prioritizing patient-centered endpoints as co-primary outcomes, structured assessment of acceptability and persistence, qualitative and mixed-methods approaches, and a dedicated study of fertility planning and treatment interruption.

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.

Artificial intelligence (AI) is rapidly transforming healthcare through applications in clinical decision support, diagnostic imaging, population health management, and workflow optimization. Despite these advances, real-world deployment continues to expose critical challenges related to safety, bias, transparency, and integration into clinical workflows. Algorithmic bias can exacerbate health disparities, limited explainability may undermine clinician trust, and insufficient validation and post-deployment monitoring can compromise patient safety. Although the World Health Organization (WHO) has established six ethical principles for AI in health, including autonomy, well-being and safety, transparency, accountability, equity, and sustainability, translating these high-level principles into practical and enforceable governance mechanisms remains a persistent challenge. This narrative review synthesizes insights from bioethics, health policy, computer science, and clinical medicine to identify gaps in current AI governance approaches and proposes a lifecycle-aligned governance-by-design framework that operationalizes WHO ethical principles across key stages of the healthcare AI lifecycle, including data collection, model development, validation, deployment, and post-deployment monitoring. The framework integrates concrete governance mechanisms such as consent governance, fairness evaluation, external validation, explainability, clinician oversight, and continuous performance monitoring. Overall, this work advances a practical, lifecycle-integrated approach to AI governance and provides a structured foundation for developing safe, equitable, and trustworthy AI systems in healthcare.