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<p>Artificial intelligence (AI) is transforming the medical industry, with AI applications in healthcare expanding across clinical domains. By 2025, medical AI is expected to be adopted in 90% of hospitals to support doctors’ work. Although AI has demonstrated proven capabilities in enhancing medical diagnosis and treatment efficacy, there remains a lack of in-depth research on its impact on doctors’ work, particularly for doctors with average qualifications in small-scale hospitals. Through an analysis of chest CT diagnostic data from a local hospital in China, our analysis reveals that after the introduction of AI assistance, doctors’ work quality improved, as evidenced by a 2.8% increase in the length of report conclusions and a 1.0% increase in the description length. However, work efficiency declined, with the average number of chest CT reports processed daily reduced by 4.3% for the overall department and 2.8% per doctor. Notably, over a six-month period following the adoption of AI, this trade-off became increasingly significant. Understanding the impact of AI assistance on doctors’ work performance is crucial for optimizing healthcare resource allocation and management decisions, ultimately enhancing patient satisfaction and well-being. This study redirects attention from patient perceptions to clinician behaviors, offering actionable insights for AI implementation in small-scale hospitals.</p>

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The paradox of AI assistance: enhancing quality while hindering efficiency in local hospitals

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月8日

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  • Siqi Dai,
  • Zhiyi Xie,
  • Zheshuai Yang,
  • Wei Miao

摘要

Artificial intelligence (AI) is transforming the medical industry, with AI applications in healthcare expanding across clinical domains. By 2025, medical AI is expected to be adopted in 90% of hospitals to support doctors’ work. Although AI has demonstrated proven capabilities in enhancing medical diagnosis and treatment efficacy, there remains a lack of in-depth research on its impact on doctors’ work, particularly for doctors with average qualifications in small-scale hospitals. Through an analysis of chest CT diagnostic data from a local hospital in China, our analysis reveals that after the introduction of AI assistance, doctors’ work quality improved, as evidenced by a 2.8% increase in the length of report conclusions and a 1.0% increase in the description length. However, work efficiency declined, with the average number of chest CT reports processed daily reduced by 4.3% for the overall department and 2.8% per doctor. Notably, over a six-month period following the adoption of AI, this trade-off became increasingly significant. Understanding the impact of AI assistance on doctors’ work performance is crucial for optimizing healthcare resource allocation and management decisions, ultimately enhancing patient satisfaction and well-being. This study redirects attention from patient perceptions to clinician behaviors, offering actionable insights for AI implementation in small-scale hospitals.