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<p>Lesion localization and medical report generation are two fundamental yet complementary tasks for modern healthcare systems, jointly underpinning accurate diagnosis and effective clinical decision-making. Although both tasks have been separately reviewed in the literature, their interconnection is not well studied. The advent of generative artificial intelligence (AI) offers transformative potential for linking both tasks. In this review, we conduct a comprehensive survey of the recent advances in lesion localization and automatic report generation. For lesion localization, we examine the evolution from non-generative approaches to state-of-the-art generative foundation models. For report generation, we focus on lesion-aware report generation and encapsulate the methodologies spanning knowledge injection, grounding, and reasoning. We further summarize the widely used datasets and evaluation metrics, and highlight the key challenges alongside potential research directions. This review offers an integrated perspective by framing lesion localization and report generation as interdependent tasks within the framework of generative AI. Future directions should integrate both tasks in one unified system for more reliable and interpretable clinical usage.</p> Graphical Abstract <p></p>

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Review of generative AI for lesion localization and automatic report generation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月2日

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  • Kaicong Sun,
  • Yulin Wang,
  • Shijie Huang,
  • Yuxiao Liu,
  • Haoyuan Chen,
  • Zifeng Lian,
  • Minhui Tan,
  • Xin Lin,
  • Weilin Zhou,
  • Zhaoyu Qiu,
  • Xiaoye Li,
  • Mingyang Yu,
  • Dinggang Shen

摘要

Lesion localization and medical report generation are two fundamental yet complementary tasks for modern healthcare systems, jointly underpinning accurate diagnosis and effective clinical decision-making. Although both tasks have been separately reviewed in the literature, their interconnection is not well studied. The advent of generative artificial intelligence (AI) offers transformative potential for linking both tasks. In this review, we conduct a comprehensive survey of the recent advances in lesion localization and automatic report generation. For lesion localization, we examine the evolution from non-generative approaches to state-of-the-art generative foundation models. For report generation, we focus on lesion-aware report generation and encapsulate the methodologies spanning knowledge injection, grounding, and reasoning. We further summarize the widely used datasets and evaluation metrics, and highlight the key challenges alongside potential research directions. This review offers an integrated perspective by framing lesion localization and report generation as interdependent tasks within the framework of generative AI. Future directions should integrate both tasks in one unified system for more reliable and interpretable clinical usage.

Graphical Abstract