Combining Artificial Intelligence with Ayurveda and Siddha Medicine: An Examination of Pulse-Based Disease Diagnosis Systems

Vollständiger Abstract

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Advancements in the fields of Ayurveda and Siddha worldwide have led to an increasing need for objective, noninvasive, technology-based diagnostic methods. Therefore, traditional pulse diagnosis has become a pivotal field in computational research to enhance diagnostic consistency. With the emergence of new biomedical sensing techniques and computational intelligence, automatic pulse diagnosis systems have been developed for pulse signal acquisition and analysis. This review paper introduces the technologies of pulse acquisition, signal preprocessing, feature extraction, machine learning, and deep learning-based methods to diagnose the disease by pulse in an automated system and also analyzed paradigms using sensors such as strain-gauge sensors, piezoelectric sensors, microelectromechanical systems, multichannel pulse sensors, photoplethysmography, piezoelectric wafer active sensors, Internet of Things-enabled devices, and artificial intelligence (AI)-assisted platforms. The preprocessing techniques, such as Butterworth, Chebyshev, Bessel, and Savitsky–Golay filters for enhancement of pulse waveform quality and feature reliability and assessment of the pulse parameters related to arterial stiffness and vascular function, along with K-Nearest Neighbors, support vector machines, Decision Trees, Random Forests, and artificial neural networks, were utilized for dosha classification and disease prediction. The review results show remarkable advances in sensing technology, signal analysis, and intelligent diagnostics algorithms. But there are hurdles, such as nonuniform datasets, explainability gaps, and clinical validation. For future studies, explainable AI, integration with wearables, standardized datasets, and widespread clinical testing are areas of interest to improve health assessment and early disease screening. Received: 30 November 2025 | Revised: 23 June 2026 | Accepted: 30 July 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Priyadharshini K: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Saravanan K: Conceptualization, Validation, Writing – review & editing, Supervision, Project administration.