Artificial intelligence vs human clinicians: a comparative analysis of complex medical query handling across the USA and Australia

Abstract

PURPOSE: This study sought to explore the practical application and effectiveness of AI-generated responses in healthcare and compared these with human clinician responses to complex medical queries in the USA and Australia. The study identifies strengths and limitations of AI in clinical settings and offers insights into its potential to enhance healthcare delivery. DESIGN/METHODOLOGY/APPROACH: A comparative analysis used a dataset of 7,165 medical queries to assess AI-generated responses versus human clinicians on accuracy, professionalism and real-time performance using machine learning algorithms and various tests. The study evaluated AI and human responses across the diverse healthcare systems of the United States and Australia, broadening the findings' applicability. FINDINGS: The results show that AI-generated responses were generally more accurate and professional than human responses, suggesting potential benefits like increased efficiency, lower costs and enhanced patient satisfaction. However, significant concerns such as AI's lack of emotional depth, data bias and the risk of displacing human clinicians must be addressed to fully utilize AI in clinical settings. ORIGINALITY/VALUE: This study contributes to the ongoing discourse on AI in healthcare by empirically testing AI's capability to handle complex medical queries compared to human clinicians. It provides a comprehensive analysis that not only underscores AI's potential to transform healthcare practices but also highlights critical areas where further refinement is necessary. The comparative analysis between two major healthcare systems adds to its originality, offering a nuanced understanding of AI's role in global health contexts.

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