THE FUTURE OF PRECISION HOMOEOPATHY: A MULTIMODAL AGENTIC INTELLIGENCE ARCHITECTURE FOR GENERATIVE CASE RECONSTRUCTION, COUNTERFACTUAL REMEDY REASONING AND ADAPTIVE LONGITUDINAL INDIVIDUALISATION

Abstract

Background: Homoeopathic practice is built on individualisation, totality-of-symptoms case analysis, and cross-referencing of rubrics against repertory and materia medica sources — a demanding process that is, as a documented weakness of the field's evidence base, inconsistently recorded. Generative artificial intelligence (AI) — systems, typically built on large language models (LLMs), that produce novel text or hypotheses from a prompt — and agentic AI — systems that autonomously plan and execute multi-step tasks by invoking external tools or databases in sequence — are increasingly used as documentation and research aids across biomedicine, and are beginning to be explored, in a still-nascent literature, within AYUSH (Ayurveda, Yoga and Naturopathy, Unani, Siddha, Homoeopathy) systems.

Objective: To synthesise the current and plausible near-future intersection of generative and agentic AI with homoeopathic research, and propose a conceptual multi-agent architecture spanning case reconstruction, repertorisation support, remedy differentiation and longitudinal follow-up.

Methods: A narrative, non-systematic search of PubMed, Google Scholar, IEEE Xplore, Scopus, the Cochrane Library and the AYUSH Research Portal was conducted for literature on AI/machine learning in AYUSH systems, LLM agents, and computational tools in homoeopathy.

Findings: Computerised repertorisation software is a direct historical precursor to current tools. Peer-reviewed applications of generative or agentic AI to homoeopathy specifically remain few, concentrated in expert systems, information-extraction pilots and exploratory AI/machine-learning remedy-matching frameworks. Substantial opportunities exist for AI-assisted literature mining, review automation, and case-record standardisation, counterbalanced by tension between homoeopathy's individualising ethic and AI's generalising tendencies, by data scarcity and hallucination risk, by unresolved explainability, regulatory and liability questions, and by the risk that AI outputs reinforce unverified claims.

Conclusion: Generative and agentic AI hold genuine, presently realisable value for homoeopathic research and documentation, but clinical deployment ahead of prospective validation and regulatory clarity is not supported by current evidence. A human-in-the-loop, decision-support framing is essential throughout.