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<p>This study explored medical students’ perceptions, educational needs, and cognitive biases toward Artificial Intelligence-assisted (AI-assisted) renal cell carcinoma (RCC) diagnosis, using the World Health Organization (WHO) 2022 classification. A cross-sectional survey of 249 students examined their familiarity with AI tools, trust in AI-based pathology, and understanding of diagnostic criteria. While most students acknowledged AI’s potential to boost diagnostic accuracy, especially for subtyping, over half voiced significant concerns about risks such as over-reliance and automation bias. A key finding was the knowledge gap around the WHO 2022 standards; urology students had a 66.7% self-reported awareness compared to just 7.7% among other majors’ students. Nearly 70% of the participants called for mandatory AI training in curricula, with a focus on the rigorous clinical validation of AI tools, supported by over 70% of the pathology students. These results highlight the need to integrate AI innovations with stronger validation protocols and curricular updates to address challenges such as technical limitations, data standardization, and ethical issues. Future studies should align AI development with educational reforms and ethical frameworks to advance precise RCC diagnosis.</p>

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AI-assisted diagnosis of renal cell carcinoma: educational needs and cognitive assessment based on the WHO classification 2022

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月2日

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  • Yuxi He,
  • Xiaoxue Yin,
  • Xu Shi,
  • Qian Wang,
  • Xingming Zhang

摘要

This study explored medical students’ perceptions, educational needs, and cognitive biases toward Artificial Intelligence-assisted (AI-assisted) renal cell carcinoma (RCC) diagnosis, using the World Health Organization (WHO) 2022 classification. A cross-sectional survey of 249 students examined their familiarity with AI tools, trust in AI-based pathology, and understanding of diagnostic criteria. While most students acknowledged AI’s potential to boost diagnostic accuracy, especially for subtyping, over half voiced significant concerns about risks such as over-reliance and automation bias. A key finding was the knowledge gap around the WHO 2022 standards; urology students had a 66.7% self-reported awareness compared to just 7.7% among other majors’ students. Nearly 70% of the participants called for mandatory AI training in curricula, with a focus on the rigorous clinical validation of AI tools, supported by over 70% of the pathology students. These results highlight the need to integrate AI innovations with stronger validation protocols and curricular updates to address challenges such as technical limitations, data standardization, and ethical issues. Future studies should align AI development with educational reforms and ethical frameworks to advance precise RCC diagnosis.