• Yash Mhatre
• Yogesh Mishra

The integration of Artificial Intelligence (AI) with wearable healthcare devices has revolutionized early disease detection, enabling continuous monitoring and realtime diagnostics. This paper explores the role of AI-powered wearables in identifying early symptoms of diseases such as cardiovascular disorders, diabetes, neurological conditions, and respiratory ailments. By leveraging machine learning algorithms, deep learning models, and predictive analytics, these devices analyze biometric data, detect anomalies, and provide proactive healthcare insights. The study discusses various AI techniques used in wearable technology, including real-time anomaly detection, personalized health monitoring, and AI-driven predictive models. Furthermore, it examines key challenges such as data privacy, accuracy, ethical concerns, and regulatory compliance in medical AI applications. With advancements in edge computing, IoT integration, and noninvasive biosensors, AI-powered wearables have the potential to transform preventive healthcare. This research highlights the impact of AI- driven wearable devices in reducing healthcare costs, improving disease management, and enhancing patient outcomes. The findings suggest that continued innovations in AI and wearable technology will play a crucial role in shaping the future of digital healthcare and early diagnosis.

AI-powered wearables, early disease detection, predictive healthcare, machine learning, real-time health monitoring, biometric data analytics.

"AI-Powered Wearable Devices for Early Disease Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.g68-g72, March-2025, Available :http://www.jetir.org/papers/JETIR2503608.pdf

"AI-Powered Wearable Devices for Early Disease Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppg68-g72, March-2025, Available at : http://www.jetir.org/papers/JETIR2503608.pdf