Comparative Evaluation of Diagnostic and Treatment Accuracy: A Five-Level Scale Analysis of Generative AI and Homoeopathic Approaches

Abstract

Background: Amid growing healthcare complexities, there is an increasing need to evaluate and integrate traditional, technological, and alternative medical systems. This study compares the diagnostic and treatment accuracy of three distinct approaches—general diagnostics, generative AI-driven care, and homoeopathic physician-led treatments—using a standardized, five-level evaluation scale.Objectives: The research aims to assess (1) diagnostic accuracy, (2) the degree of personalization in treatment planning, and (3) the long-term efficacy of each modality. It also explores the potential of a hybrid healthcare model that leverages both AI precision and classical homoeopathic depth.Methods: A qualitative five-point scale was developed to categorize healthcare performance from Level 1 (Poor) to Level 5 (Excellent). Each level defines specific criteria in diagnostic accuracy, personalization, and relapse rates. The study then mapped general diagnostics, AI systems, and homoeopathic treatments against these benchmarks.Results: General diagnostics typically performed at Level 2 (Fair) to Level 3 (Good), offering standardized but less personalized care. Generative AI demonstrated a wide range of performance, from Level 1 to Level 5, depending on data integration and algorithm maturity. Homoeopathic treatment outcomes ranged from Level 1 in inexperienced practice to Level 5 in expert-led, individualized care.Discussion: Findings reveal a strong correlation between personalization and therapeutic success. Advanced AI systems and experienced homoeopaths achieved the highest accuracy and patient satisfaction. The research underscores the potential of integrating AI’s analytical power with homoeopathy’s holistic framework to build a next-generation, patient-centric care model.Conclusion: This study introduces a novel evaluation scale that provides a measurable framework for comparative analysis and development across diagnostic and therapeutic systems. It offers valuable insights for clinicians, AI developers, educators, and policymakers, paving the way for future interdisciplinary innovation and ethically sound healthcare evolution.

Ähnliche Arbeiten