AYURVEDIC PHENOTYPING: A FRAMEWORK FOR INDIVIDUALIZED DISEASE DIAGNOSIS

Ayurveda is a holistic system of medicine that emphasizes individualized assessment of health and disease based on the unique constitution and clinical characteristics of an individual. The concept of Ayurvedic phenotype can be understood as the systematic representation of an individual's observable and clinically relevant characteristics through classical Ayurvedic parameters. These include Prakriti (innate constitution), Vikriti (current pathological state), Dosha (bio-humor), Dushya (vitiated tissue), Agni (digestive-metabolic fire), Ama (undigested metabolic by-product), Srotas (body channels), Koshtha (nature of bowel), Bala (strength), and other clinical features described in Ayurvedic diagnostic frameworks. Unlike a disease-centred approach, Ayurvedic assessment attempts to identify individual variations in disease manifestation and pathogenesis, thereby supporting personalized diagnosis and management. This paper proposes Ayurvedic Phenotype as a framework for individualized disease diagnosis, integrating classical Ayurvedic principles with a structured and systematic approach to clinical assessment. The framework aims to organize subjective and qualitative Ayurvedic observations into clearly defined and reproducible phenotypic features. Such standardization may facilitate comparison between individuals, identification of disease-specific patterns, and objective documentation of Ayurvedic clinical findings. Ayurvedic phenotyping may therefore serve as a bridge between traditional individualized diagnosis and contemporary approaches to personalized medicine. A standardized phenotypic framework has the potential to enhance diagnostic consistency, facilitate clinical research, and contribute to the wider scientific evaluation of personalized Ayurvedic healthcare. Further validation through larger, multicentric, and clinically diverse studies is required to establish its reliability, diagnostic utility, and applicability across different diseases and populations.