(untitled) (International Journal of Environmental Sciences, 2025)
Ayurveda, an ancient system of holistic medicine, emphasizes individualized healthcare through the assessment of Prakriti (constitutional typology), traditionally determined via subjective questionnaires and clinical observations. However, the lack of standardization and inherent subjectivity in these methods compromises diagnostic reproducibility. This research paper explores the integration of artificial intelligence (AI) to establish an objective, scalable framework for Prakriti assessment. We review AI methodologies—including machine learning (ML), natural language processing (NLP), and multimodal data fusion—applied to phenotypic, genetic, and questionnaire-derived datasets for Prakriti classification. Our analysis highlights how AI algorithms enhance diagnostic accuracy by identifying subtle patterns beyond human perceptual thresholds, while addressing biases in training data and model interpretability. Preliminary studies demonstrate AI-driven tools achieving >85% agreement with expert assessments, validating their potential as clinical adjuncts. We further discuss ethical considerations, such as data privacy and algorithmic Transparency, and propose a hybrid AI-human validation pipeline to preserve Ayurvedic principles. This synthesis of AI and Ayurveda not only modernizes Prakriti assessment but also paves the way for predictive, personalized wellness strategies rooted in evidence-based science.