Artificial intelligence and digital technologies in homeopathy: Acomprehensive review of clinical decision support, digital caseanalysis, individualised remedy selection, research applications, andfuture perspectives

Abstract

Artificial intelligence (AI) and digital health technologies are increasingly being used to organise clinical information, analyse free-text records, support prediction, automate repetitive tasks, and extend access to healthcare. Homeopathy presents a distinctive digitalisation problem because its clinical workflow commonly depends on longitudinal narratives, symptom descriptors, individualisation, repertorisation, materia medica consultation, and repeated follow-up assessment. Digital tools can therefore be relevant to case documentation, structured symptom extraction, repertory search, clinical decision support, remote consultation, outcome monitoring, research data management, and evidence synthesis. At the same time, the existence of a technically feasible AI application should not be confused with proof of clinical efficacy of the underlying therapy. Contemporary reviews and evidence assessments continue to report limited or uncertain evidence for homeopathy as an effective treatment for specific health conditions, while individualised homeopathy research has produced heterogeneous findings and ongoing methodological debate. Consequently, AI should be evaluated as a technology for information processing and decision support rather than as evidence that validates homeopathic efficacy.

This review examines the potential and current state of digital technologies in homeopathy, including electronic case records, repertory software, natural language processing, machine learning, large language models, multimodal data capture, patientfacing applications, teleconsultation, research informatics, and AI-assisted evidence synthesis. Published research on repertory software demonstrates meaningful uptake among practitioners, while recent multicentre Indian clinical data work shows that internet-based systems can capture symptoms, diagnoses, prescriptions, and longitudinal outcomes at scale. A 2026 comparative retrospective study also illustrates a key limitation of generative AI in this domain: remedy recommendations from chatbots were not consistently aligned with live practitioners or stable across repeated inputs.

For safe implementation, homeopathic AI should therefore follow established health-AI principles: clinically meaningful problem definition, representative datasets, traceable model development, external validation, transparent reporting, cybersecurity, privacy protection, human oversight, monitoring after deployment, and explicit management of uncertainty. The most credible near-term role is as a human-in-the-loop infrastructure that improves information quality, reproducibility, research efficiency, and longitudinal record keeping while leaving accountable clinical decisions with qualified professionals and ensuring timely conventional assessment for urgent, serious, or diagnostically uncertain conditions. Future progress will depend less on simply adding AI features and more on building high-quality, standardised, auditable datasets and testing whether digital support produces measurable improvements in decision quality, patient safety, workflow, and patient-centred outcomes.