# The Application of Artificial Intelligence in Acute Prescribing in Homeopathy: A Comparative Retrospective Study

## TL;DR

This study compares AI-generated homeopathy recommendations with those from live practitioners for acute illnesses and finds that AI is not a perfect substitute.

## Contribution

The study evaluates the practical challenges and limitations of AI in homeopathy for acute prescribing.

## Key findings

- The AI tool provided 1 to 20 remedy recommendations per case, with the practitioner's choice appearing in 59% of cases.
- Only 17% of practitioner-recommended remedies were top matches in the AI results.
- The AI tool covered 74 acute complaints, but only 22 were represented in the live cases studied.

## Abstract

Background/Objective: The use of artificial intelligence to assist in medical applications is an emerging area of investigation and discussion. The researchers studied whether there was a difference between homeopathy guidance provided by artificial intelligence (AI) (automated) and live professional practitioners (live) for acute illnesses. Additionally, the study explored the practical challenges associated with validating AI tools used for homeopathy and sought to generate insights on the potential value and limitations of these tools in the management of acute health complaints. Method: Randomly selected cases at a homeopathy teaching clinic (n = 100) were entered into a commercially available homeopathic remedy finder to investigate the consistency between automated and live recommendations. Client symptoms, medical disclaimers, remedies, and posology were compared. The findings of this study show that the purpose-built homeopathic remedy finder is not a one-to-one replacement for a live practitioner. Result: In the 100 cases compared, the automated online remedy finder provided between 1 and 20 prioritized remedy recommendations for each complaint, leaving the user to make the final remedy decision based on how well their characteristic symptoms were covered by each potential remedy. The live practitioner-recommended remedy was included somewhere among the auto-mated results in 59% of the cases, appeared in the top three results in 37% of the cases, and was a top remedy match in 17% of the cases. There was no guidance for managing remedy responses found in live clinical settings. Conclusion: This study also highlights the challenge and importance of validating AI remedy recommendations against real cases. The automated remedy finder used covered 74 acute complaints. The live cases from the teaching clinic included 22 of the 74 complaints.