“Artificial Intelligence in Homoeopathy: Current Applications and Future Directions”

Background: Artificial Intelligence (AI) is revolutionizing healthcare by enhancing diagnostics, decision-making, and personalized medicine. Homoeopathy, being an individualized system of medicine, can benefit significantly from AI-driven innovations for case-taking, repertorization, drug proving, and clinical decision support.
Objective: This narrative review aims to explore the applications of AI in homoeopathy, summarize current developments, and highlight future directions for integrative digital healthcare.
Methods: A literature search was conducted in PubMed, Scopus, Google Scholar, and AYUSH research databases for studies, reports, and conceptual papers on AI and homoeopathy (2000–2025). In addition, grey literature, conference proceedings, and digital health projects were screened. Relevant examples from mainstream AI in healthcare were extrapolated to homoeopathy.
Results: Current applications of AI in homoeopathy include:
• AI-based case-taking and symptom analysis using Natural Language Processing (NLP).
• Machine learning algorithms for repertorization and individualized prescription support.
• Data mining techniques in materia medica and drug proving validation.
• AI-assisted systematic reviews and evidence synthesis.
• Mobile health applications for patient monitoring, compliance, and outcome tracking.
Future possibilities involve precision homoeopathy through integration of patient clinical data, biomarkers, and AI-driven predictive modelling. Challenges include lack of standardized datasets, need for robust validation, and ethical issues related to patient privacy.
Conclusion: AI has immense potential to modernize homoeopathy by improving accuracy, efficiency, and evidence generation. Collaborative efforts between homoeopathic practitioners, data scientists, and policymakers are needed to create reliable, validated, and clinically applicable AI models.

Homoeopathy, Artificial Intelligence, Machine Learning

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