A Retrospective Comparative Study of Concordance Between Artificial Intelligence Remedy Suggestion and Synthesis Repertory Results in Urinary Tract Infection Cases

Background: Homeopathic prescribing traditionally relies on detailed case taking, repertorization, and consultation of the materia medica to identify the most appropriate simillimum. With the emergence of artificial intelligence (AI)-based tools, it has become possible to obtain rapid remedy suggestions based on symptom input, potentially bypassing classical repertorization steps. The extent to which such AI-generated suggestions align with classical repertorial outcomes remains insufficiently explored. Objective: To determine the concordance between remedies suggested by an AI-based tool and those obtained through classical repertorization using the Synthesis Repertory in cases of urinary tract infection (UTI). Methods: A retrospective comparative study was conducted on 34 well-documented UTI cases. Each case was repertorized using the Synthesis Repertory via Radar software, and the first 10 remedies were recorded. The same symptom totality was entered into an AI-based tool (HomeoXpert by Nambisons), accessed through ChatGPT, to obtain a single remedy suggestion. Concordance was classified as an exact match (same first remedy), within the first 10 repertorial remedies, or absent. Results: Exact concordance between the AI-suggested remedy and the first repertorial remedy was observed in three cases (8.8%). In 18 cases (53%), the AI-suggested remedy appeared within the first 10 repertorial remedies. In 13 cases (38.2%), the AI-suggested remedy did not appear among the first 10 repertorial remedies. Conclusion: AI-based remedy suggestions showed partial concordance with classical repertorial outcomes, most commonly appearing within the first 10 remedies rather than as an exact match. These findings suggest that AI tools may serve as supportive aids in the preliminary stages of remedy consideration but cannot replace classical repertorization and clinical judgment in homeopathic prescribing.

Authors

Institutions

Publication Details

Journal
Homeopathy
Published
2026-08-27
DOI
https://doi.org/10.1055/a-2836-9686
Primary Topic
Complementary and Alternative Medicine Studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Retrospective Comparative Study of Concordance Between Artificial Intelligence Remedy Suggestion and Synthesis Repertory Results in Urinary Tract Infection Cases

Manish Kumar Tiwari, Anita Lobo, Akshara Thayyil, Sandhya Kondalakadu
Homeopathy
Complementary and Alternative Medicine Studies
article

A Retrospective Comparative Study of Concordance Between Artificial Intelligence Remedy Suggestion and Synthesis Repertory Results in Urinary Tract Infection Cases

Manish Kumar Tiwari, Anita Lobo, Akshara Thayyil, Sandhya Kondalakadu
article en

Abstract

Background: Homeopathic prescribing traditionally relies on detailed case taking, repertorization, and consultation of the materia medica to identify the most appropriate simillimum. With the emergence of artificial intelligence (AI)-based tools, it has become possible to obtain rapid remedy suggestions based on symptom input, potentially bypassing classical repertorization steps. The extent to which such AI-generated suggestions align with classical repertorial outcomes remains insufficiently explored. Objective: To determine the concordance between remedies suggested by an AI-based tool and those obtained through classical repertorization using the Synthesis Repertory in cases of urinary tract infection (UTI). Methods: A retrospective comparative study was conducted on 34 well-documented UTI cases. Each case was repertorized using the Synthesis Repertory via Radar software, and the first 10 remedies were recorded. The same symptom totality was entered into an AI-based tool (HomeoXpert by Nambisons), accessed through ChatGPT, to obtain a single remedy suggestion. Concordance was classified as an exact match (same first remedy), within the first 10 repertorial remedies, or absent. Results: Exact concordance between the AI-suggested remedy and the first repertorial remedy was observed in three cases (8.8%). In 18 cases (53%), the AI-suggested remedy appeared within the first 10 repertorial remedies. In 13 cases (38.2%), the AI-suggested remedy did not appear among the first 10 repertorial remedies. Conclusion: AI-based remedy suggestions showed partial concordance with classical repertorial outcomes, most commonly appearing within the first 10 remedies rather than as an exact match. These findings suggest that AI tools may serve as supportive aids in the preliminary stages of remedy consideration but cannot replace classical repertorization and clinical judgment in homeopathic prescribing.

Homeopathy
Father Muller Homoeopathic Medical College (IN)
Openalex Percentile: Top 6%
Complementary and Alternative Medicine Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

A Retrospective Comparative Study of Concordance Between Artificial Intelligence Remedy Suggestion and Synthesis Repertory Results in Urinary Tract Infection Cases — Manish Kumar Tiwari, Anita Lobo, et al. · Homeopathy (2026) | TGRS Research Map | TGRS