ARTIFICIAL INTELLIGENCE-ASSISTED PRECISION HOMOEOPATHY: A GLOBAL RESEARCH FRAMEWORK FOR INDIVIDUALISED PRESCRIBING, BIOMARKER INTEGRATION AND N-OF-1 EVIDENCE GENERATION

Abstract

Background: Homoeopathy has historically emphasized individualisation, totality of symptoms and selection of the similimum rather than treatment based solely on diagnostic labels. Contemporary biomedical research, however, increasingly demands reproducible phenotyping, objective outcome measurement, transparent reporting and biologically testable hypotheses. The future of homoeopathic research therefore requires methodologies capable of preserving individualised prescribing while introducing rigorous tools from modern clinical science.

Objective: To examine how artificial intelligence (AI), structured clinical phenotyping, biomarker research, systems biology and N-of-1 trial methodology could be integrated into a contemporary research architecture for individualised homoeopathy.

Methodology: A narrative, literature-informed review was undertaken using published literature on clinical research in homoeopathy, high-dilution/basic research, artificial intelligence in medicine, precision medicSine, N-of-1 methodology and international research-reporting standards. Global policy developments concerning evidence generation, safety, regulation, innovation and digital health in traditional, complementary and integrative medicine were also considered. The proposed framework was designed to remain compatible with individualised homoeopathic prescribing while improving reproducibility and scientific transparency.

Results: The current evidence base demonstrates a heterogeneous research landscape. Randomised trials and meta-analyses of individualised homoeopathy have produced findings that remain methodologically contested, with risk of bias and inconsistency limiting definitive conclusions. Basic research has reported physicochemical phenomena, including nanoparticulate findings in some highly diluted preparations, but such observations cannot by themselves establish clinical efficacy. Modern precision-medicine and AI methodologies offer an opportunity to transform the research question from a purely disease-centred model into a multidimensional investigation of patient phenotype, treatment selection, biological response and longitudinal outcome. A five-layer research architecture is proposed: structured digital case phenotyping; AI-assisted repertorial decision support; biomarker and systems-biology profiling; personalised N-of-1 evaluation; and federated real-world evidence generation.

Conclusion: The next phase of homoeopathic research should not depend on either uncritical acceptance or categorical dismissal. It should generate falsifiable, transparent and reproducible evidence while preserving the central research characteristic of individualised prescribing. AI should function as decision support rather than an autonomous prescriber. Biomarkers should be used to investigate patient stratification and response rather than to retrospectively justify treatment. N-of-1 methodology may provide an important bridge between individualisation and experimental rigor. This framework could position homoeopathic research within the broader global movement toward evidence-based, personalised and data-driven healthcare.