Individualization is the cornerstone of homoeopathic practice; however, the process is highly subjective and varies between practitioners due to differences in observation, interpretation, and analysis of symptoms. Recent advancements in Artificial Intelligence (AI), multimodal data collection, and machine learning offer an unprecedented opportunity to enhance precision in homoeopathic case-taking and remedy selection. This conceptual research proposes an AI-Aided Homoeopathic Clinic model integrating 360° multisensory recording, natural language processing (NLP), emotion-tone analysis, gesture recognition, and a curated digital knowledge base of Materia Medica, Repertory, Organon, and clinical literature. The model aims to reduce human errors, increase reproducibility in remedy selection, and strengthen the validity and reliability of homoeopathic prescribing. The paper explores applicability, reliability, validity, limitations, ethical considerations, and future possibilities of AI in homoeopathic practice. This concept has the potential to become a milestone in homoeopathic clinical methodology, guiding the future of precision homoeopathy. Keywords: Artificial Intelligence, Homoeopathy, Individualisation, Repertory, Materia Medica, 360° Recording, NLP, Machine Learning, Futuristic Medicine, AI-Aided Homoeopathic Clinic.
Anpat et al. (Thu,) studied this question.
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