Shringagrahika Nyaya and Artificial Intelligence: A Conceptual Framework for Feature Identification and Knowledge Interpretation

Abstract

Background: Ayurvedic classical literature preserves complex scientific knowledge through concise and aphoristic expressions, the interpretation of which frequently requires contextual reasoning and identification of representative features. Śṛṅgagrahika Nyāya, an interpretative principle employed by Chakrapāṇi Datta in the Āyurveda Dīpikā commentary on the Charaka Saṃhitā, illustrates the identification of a particular entity through a distinguishing or representative characteristic. Contemporary Artificial Intelligence (AI) similarly employs selective information processing, feature identification, pattern recognition, contextual interpretation, and explainable decision-making. Objective: To explore the conceptual relationship between Śṛṅgagrahika Nyāya and contemporary AI approaches and to propose a conceptual framework for its potential application in Ayurveda-oriented AI systems. Materials and Methods: A narrative conceptual review was undertaken through analysis of classical Ayurvedic literature, particularly the Charaka Saṃhitā with Chakrapāṇi Datta’s Āyurveda Dīpikā commentary, along with contemporary literature related to feature selection, pattern recognition, attention mechanisms, knowledge representation, Natural Language Processing, clinical decision support systems and Explainable Artificial Intelligence. Classical applications of Śṛṅgagrahika Nyāya were examined and conceptually mapped with relevant AI methodologies. Results: Analysis of twelve classical applications demonstrated four major epistemological dimensions of Śṛṅgagrahika Nyāya: representative knowledge representation, knowledge compression with contextual inference, feature-based identification and prioritization and adaptive or individualized interpretation. These dimensions demonstrated conceptual correspondence with feature selection, pattern recognition, attention mechanisms, knowledge representation, context-aware processing and Explainable Artificial Intelligence. Based on these correspondences, potential applications were identified in AI-assisted interpretation of Ayurvedic classical texts, Ayurveda knowledge graphs and ontologies, clinical decision-support systems, Ayurvedic pharmacological knowledge analysis, and explainable Ayurveda-oriented AI. Conclusion: Śṛṅgagrahika Nyāya may be understood as a classical Ayurvedic epistemological framework emphasizing selective identification, representative reasoning, contextual interpretation, and logical explanation. Its conceptual correspondence with contemporary AI provides a novel interdisciplinary perspective for developing transparent, context-sensitive, and explainable AI applications in Ayurveda. However, the proposed relationship is conceptual rather than historical or technological, and computational implementation and empirical validation are required to establish its practical applicability.

Keywords: Śṛṅgagrahika Nyāya, Ayurveda, Artificial Intelligence, Feature Selection, Pattern Recognition, Explainable Artificial Intelligence, Knowledge Representation, Ayurvedic Epistemology