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Purpose <p>This systematic review aims to examine the literature of artificial intelligence (AI) algorithms in the diagnosis of hepatocellular carcinoma (HCC) among focal liver lesions compared to radiologists on multiphase CT images, focusing on performance metrics that include sensitivity and specificity as a minimum.</p> Methods <p>We searched Embase, PubMed and Web of Science for studies published from January 2018 to May 2024. Eligible studies evaluated AI algorithms for diagnosing HCC using multiphase CT, with radiologist interpretation as a comparator. The performance of AI models and radiologists was recorded using sensitivity and specificity from each study. TRIPOD + AI was used for quality appraisal and PROBAST was used to assess the risk of bias.</p> Results <p>Seven studies out of the 3532 reviewed were included in the review. All seven studies analysed the performance of AI models and radiologists. Two studies additionally assessed performance with and without supplementary clinical information to assist the AI model in diagnosis. Three studies additionally evaluated the performance of radiologists with assistance of the AI algorithm in diagnosis. The AI algorithms demonstrated a sensitivity ranging from 63.0 to 98.6% and a specificity of 82.0–98.6%. In comparison, junior radiologists (with less than 10&#xa0;years of experience) exhibited a sensitivity of 41.2–92.0% and a specificity of 72.2–100%, while senior radiologists (with more than 10&#xa0;years of experience) achieved a sensitivity between 63.9% and 93.7% and a specificity ranging from 71.9 to 99.9%.</p> Conclusion <p>AI algorithms demonstrate adequate performance in the diagnosis of HCC from focal liver lesions on multiphase CT images. Across geographic settings, AI could help streamline workflows and improve access to timely diagnosis. However, thoughtful implementation strategies are still needed to mitigate bias and overreliance.</p>

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A systematic review of comparisons of AI and radiologists in the diagnosis of HCC in multiphase CT: implications for practice

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月18日

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  • Jarrod Younger,
  • Emily Morris,
  • Nicholas Arnold,
  • Chanchala Athulathmudali,
  • Janani Pinidiyapathirage,
  • William MacAskill

摘要

Purpose

This systematic review aims to examine the literature of artificial intelligence (AI) algorithms in the diagnosis of hepatocellular carcinoma (HCC) among focal liver lesions compared to radiologists on multiphase CT images, focusing on performance metrics that include sensitivity and specificity as a minimum.

Methods

We searched Embase, PubMed and Web of Science for studies published from January 2018 to May 2024. Eligible studies evaluated AI algorithms for diagnosing HCC using multiphase CT, with radiologist interpretation as a comparator. The performance of AI models and radiologists was recorded using sensitivity and specificity from each study. TRIPOD + AI was used for quality appraisal and PROBAST was used to assess the risk of bias.

Results

Seven studies out of the 3532 reviewed were included in the review. All seven studies analysed the performance of AI models and radiologists. Two studies additionally assessed performance with and without supplementary clinical information to assist the AI model in diagnosis. Three studies additionally evaluated the performance of radiologists with assistance of the AI algorithm in diagnosis. The AI algorithms demonstrated a sensitivity ranging from 63.0 to 98.6% and a specificity of 82.0–98.6%. In comparison, junior radiologists (with less than 10 years of experience) exhibited a sensitivity of 41.2–92.0% and a specificity of 72.2–100%, while senior radiologists (with more than 10 years of experience) achieved a sensitivity between 63.9% and 93.7% and a specificity ranging from 71.9 to 99.9%.

Conclusion

AI algorithms demonstrate adequate performance in the diagnosis of HCC from focal liver lesions on multiphase CT images. Across geographic settings, AI could help streamline workflows and improve access to timely diagnosis. However, thoughtful implementation strategies are still needed to mitigate bias and overreliance.