Doherty Rachael, Pracjek Parker, Luketic Christine D, Straiges Denise, Gray Alastair C
HOHM Foundation, Office of Research, Philadelphia, PA 19138, USA.
Healthcare (Basel). 2025 Aug 6;13(15):1923. doi: 10.3390/healthcare13151923.
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 ( = 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.
背景/目的:利用人工智能辅助医疗应用是一个新兴的研究和讨论领域。研究人员研究了人工智能(自动化)提供的顺势疗法指导与现场专业从业者提供的顺势疗法指导在治疗急性疾病方面是否存在差异。此外,该研究探讨了验证用于顺势疗法的人工智能工具所面临的实际挑战,并试图深入了解这些工具在管理急性健康问题方面的潜在价值和局限性。 方法:在一家顺势疗法教学诊所随机选择病例(n = 100),输入一款市售的顺势疗法药物查找器,以调查自动化推荐和现场推荐之间的一致性。比较了客户症状、医学免责声明、药物和剂量。本研究结果表明,专门构建的顺势疗法药物查找器不能一对一地替代现场从业者。 结果:在比较的100个病例中,自动化在线药物查找器针对每个病症提供了1至20个优先药物推荐,让用户根据每种潜在药物对其特征症状的覆盖程度做出最终的药物选择。在59%的病例中,现场从业者推荐的药物出现在自动化结果中,在37%的病例中出现在前三个结果中,在17%的病例中是最佳药物匹配。在现场临床环境中未发现有关处理药物反应的指导。 结论:本研究还强调了根据实际病例验证人工智能药物推荐的挑战和重要性。所使用的自动化药物查找器涵盖了74种急性病症。教学诊所的实际病例包括了74种病症中的22种。
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