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This paper presents a comprehensive study on the Clinical Readiness Score (CRS), a structured evaluation metric for assessing AI models used in lung cancer diagnosis. The CRS incorporates multiple criteria such as interpretability, efficiency, clinical validation, and accuracy, employing the Analytic Hierarchy Process (AHP) for weight assignments. This study discusses the methodology behind CRS, validates its consistency, and explores its practical implications. Additionally, graphical representations of AHP weight distribution, sensitivity analysis, and CRS factor contributions are provided for better comprehension.

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Improving Lung Cancer Prognosis Through Data Science

  • 会议论文

  • 发表日期：

    2026年

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  • Shiva Jyoti,
  • Samriddhi Ganguly,
  • B. Sri Soumya,
  • S. Nachiyappan

摘要

This paper presents a comprehensive study on the Clinical Readiness Score (CRS), a structured evaluation metric for assessing AI models used in lung cancer diagnosis. The CRS incorporates multiple criteria such as interpretability, efficiency, clinical validation, and accuracy, employing the Analytic Hierarchy Process (AHP) for weight assignments. This study discusses the methodology behind CRS, validates its consistency, and explores its practical implications. Additionally, graphical representations of AHP weight distribution, sensitivity analysis, and CRS factor contributions are provided for better comprehension.