Artificial Intelligence Powered Wearable Biosensors for Early Disease Detection: A Comprehensive Review

Shreyansh Goswami; Saavya Sharma

Artificial Intelligence Powered Wearable Biosensors for Early Disease Detection: A Comprehensive Review

Author(s)	Mr. Shreyansh Goswami, Mr. Saavya Sharma
Country	India
Abstract	Wearable biosensors integrated with artificial intelligence (AI) have emerged as a transformative technology in modern healthcare, enabling continuous physiological monitoring, early disease detection, and personalized clinical decision-making. The convergence of advances in sensor miniaturization, wireless communication, cloud computing, and machine learning has shifted healthcare paradigms from episodic, reactive treatment toward proactive and preventive medicine. AI-powered wearable devices are capable of continuously acquiring multimodal physiological and biochemical signals including electrocardiography (ECG), photoplethysmography (PPG), electroencephalography (EEG), blood oxygen saturation, body temperature and sweat biomarkers and converting these data into clinically actionable insights. The aim of this comprehensive review is to critically evaluate recent advancements in AI-enabled wearable biosensors, emphasizing their technological evolution, sensing mechanisms, AI methodologies, clinical applications, integration with digital health ecosystems, and emerging innovations. This review discusses conventional machine learning algorithms, deep learning architectures, edge AI, and TinyML frameworks. Furthermore, the review highlights the integration of wearable biosensors with the Internet of Medical Things (IoMT), cloud computing, smartphone platforms, telemedicine, and electronic health record systems to support remote patient monitoring and precision healthcare. Despite remarkable progress, several challenges continue to impede widespread clinical implementation, including sensor accuracy, motion artifacts, data heterogeneity, algorithmic bias, battery limitations, cybersecurity risks, ethical concerns and regulatory complexities. Emerging technologies such as electronic skin (e-skin), flexible electronics, digital twins, explainable artificial intelligence (XAI), multimodal sensor fusion, self-powered wearable systems, nanotechnology-based biosensors, and smart contact lenses are expected to substantially enhance the diagnostic capabilities and clinical utility of next-generation wearable healthcare systems. Overall, AI-powered wearable biosensors represent a rapidly evolving interdisciplinary field with significant potential to revolutionize disease prevention, early diagnosis, and personalized medicine. Future research should prioritize standardized datasets, robust clinical validation, transparent and interpretable AI models, federated learning frameworks, and harmonized regulatory policies to accelerate the safe and equitable deployment of intelligent wearable technologies in both clinical and community healthcare settings.
Keywords	Artificial Intelligence; Wearable Biosensors; Early Disease Detection; Machine Learning; Digital Health; Internet of Medical Things (IoMT); Precision Medicine; Remote Patient Monitoring
Published In	Volume 8, Issue 4, July-August 2026
Published On	2026-08-16

View / Download PDF File

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Artificial Intelligence Powered Wearable Biosensors for Early Disease Detection: A Comprehensive Review

Share this