Integrating Artificial Intelligence, Machine Learning, and IoT-Enabled Devices with Qualitative Research Methods in Ayurvedic Public Health: A Biostatistical Perspective on Modern Data Collection and Analysis
Keywords:
Ayurveda, Qualitative Research, Artificial Intelligence, Machine Learning, IoT, Biostatistics, AYUSH, Darshana Pariksha, Jihva Pariksha, Public Health, Prakriti, Mixed MethodsAbstract
One of the oldest and most philosophically comprehensive systems of medicine in the world, Ayurveda has been based on the multi-sensory and multi-dynamically diagnostic inquiry over the millenarian time. Its primordial examinations, Darshana Pariksha (visual examination), Nadi Pariksha (pulse diagnosis) and Jihva Pariksha (tongue examination) require contextual sensitivity and individualized analysis which have been historically elusive to the tools of standardized biomedical measurements. This paper also posits that the qualitative research methods, which have long been underestimated in Ayurvedic research, are the most epistemologically suitable methodology of studying Ayurvedic health phenomena, and that their effectiveness is exponential when combined with Artificial Intelligence (AI), Machine Learning (ML), and Internet of Things (IoT) technologies. Based on the integrative literature review framework provided by Snyder (2019), as well as the qualitative field research guidelines of Mack et al. (2005), and the biostatistical perspective informed by Kothari (2004), the paper will offer a three-phase methodological approach, which includes qualitative data collection, technology-facilitated analysis, and the biostatistical integration approach, which is appropriate to be presented in Scopus-indexed publication and applied to the AYUSH policy. The paradigm shows that the union of the traditional Ayurvedic and the contemporary data science is not only possible but very much necessary.
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Published on: 07-06-2026
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