AI-Based Symptom Pattern Recognition in Ayurveda

Abstract

Ayurvedic diagnostic practice leans heavily on multi-modal symptom pattern recognition: the practitioner integrates pulse characteristics, tongue appearance, stool and urine observations, voice qualities, touch feedback, visual observation, general appearance and structured case history into a constitutional (prakriti) and dynamic (vikriti) clinical picture. The cognitive work is substantial and has historically been the bottleneck for both practitioner training and inter-rater reliability. Artificial intelligence has been applied to parts of this pattern-recognition task since the late 2000s, covering rule-based expert systems, classical machine learning classifiers, tongue and iris image analysis, pulse waveform signal processing, and more recently multi-modal deep learning systems. The evidence is scattered across engineering and clinical venues, and the question of which AI approaches are actually converging toward clinical utility has not been systematically addressed. This study reviews 58 primary studies published between 2008 and 2021 on AI-based symptom pattern recognition in Ayurveda, groups them under five methodological frames (A1: rule-based / expert systems; A2: classical ML classifiers; A3: image analysis; A4: pulse and signal processing; A5: deep learning and integrated multimodal AI), and scores each frame using the AI-Ayurveda Symptom Integration Score (ASI). Integrated multimodal deep learning (A5) produced the highest ASI at 0.866, with tongue image analysis (A3) the strongest single-modality frame at 0.798. Interpretability remains the dominant unsolved problem across frames, and clinical deployment beyond pilot settings is almost entirely absent. AI is closer to useful decision support than to autonomous diagnosis in Ayurveda, and the next empirical investments should prioritise interpretable multi-modal systems with practitioner-centred validation.