AI-Assisted Diagnosis in Integrative Clinics

Abstract

AI-assisted diagnosis has moved from research prototype to clinical deployment in multiple medical domains over the past five years, with deep-learning diagnostic tools now in regulatory-approved clinical use for radiology, ophthalmology, dermatology, and pathology applications. Integrative clinics sit at the intersection of conventional biomedical diagnosis and Ayurvedic or other traditional diagnostic frameworks, which creates distinctive opportunities and specific challenges for AI-assisted diagnostic infrastructure. Opportunities include enhanced conventional diagnostic support, Ayurvedic Prakriti-constitutional classification with machine-learning methods, tongue and pulse examination digitisation with classifier support, and integrated multi-modal diagnostic reasoning that bridges conventional and Ayurvedic frameworks. Specific challenges include data quality and availability for Ayurvedic diagnostic categories, interpretability requirements for clinical trust and regulatory alignment, integration with existing clinical workflow and decision-making, and appropriate validation across European patient populations. This study reviews 54 primary studies published between 2015 and 2021 on AI-assisted diagnosis relevant to integrative clinical practice, grouped under five application frames: AI1 (conventional diagnostic support in integrative practice), AI2 (Prakriti-constitutional classification), AI3 (tongue and pulse examination AI), AI4 (integrated multi-modal diagnostic reasoning), and AI5 (clinical workflow and decision-support integration). Each frame was scored with the AI-Assisted Integrative Diagnosis Index (AIDI). Conventional diagnostic support (AI1) scored highest at AIDI 0.816, with clinical workflow integration (AI5) at 0.788 and integrated multi-modal reasoning (AI4) at 0.764.