AI in Predictive Healthcare: How Artificial Intelligence and Wearable Devices are Transforming Early Disease Detection | A Systematic Literature Review (2026) | Ruchir Ganatra

This research paper was prepared by Ruchir Ganatra as an independent research study exploring Artificial Intelligence in Predictive Healthcare. The paper provides a comprehensive review of AI-powered wearable devices, machine learning algorithms, digital health technologies, Internet of Medical Things (IoMT), and early disease detection using peer-reviewed scientific literature published between 2022 and 2026. The review compares leading AI techniques including CNN, LSTM, Random Forest, Federated Learning, and Explainable AI while discussing clinical applications, ethical considerations, privacy, algorithmic bias, and future research directions. This paper is intended to support researchers, students, healthcare professionals, and technology enthusiasts interested in the future of AI-driven healthcare.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-06-28
DOI
https://doi.org/10.5281/zenodo.20997942
Primary Topic
Machine Learning in Healthcare
Type
preprint
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preprint

AI in Predictive Healthcare: How Artificial Intelligence and Wearable Devices are Transforming Early Disease Detection | A Systematic Literature Review (2026) | Ruchir Ganatra

Ruchir Ganatra
Zenodo (CERN European Organization for Nuclear Research)
Machine Learning in Healthcare
preprint

AI in Predictive Healthcare: How Artificial Intelligence and Wearable Devices are Transforming Early Disease Detection | A Systematic Literature Review (2026) | Ruchir Ganatra

Ruchir Ganatra
preprint en

Abstract

This research paper was prepared by Ruchir Ganatra as an independent research study exploring Artificial Intelligence in Predictive Healthcare. The paper provides a comprehensive review of AI-powered wearable devices, machine learning algorithms, digital health technologies, Internet of Medical Things (IoMT), and early disease detection using peer-reviewed scientific literature published between 2022 and 2026. The review compares leading AI techniques including CNN, LSTM, Random Forest, Federated Learning, and Explainable AI while discussing clinical applications, ethical considerations, privacy, algorithmic bias, and future research directions. This paper is intended to support researchers, students, healthcare professionals, and technology enthusiasts interested in the future of AI-driven healthcare.

Zenodo (CERN European Organization for Nuclear Research)
Machine Learning in Healthcare
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