Scholarlink

<p>In the current research landscape, there is a plethora of artificial intelligence methods for medical image analysis, improving diagnostic accuracy; however, AI introduces challenges related to trustworthiness and transparency of decisions. Clinicians and medical experts often find it difficult to comprehend the process by which machine learning models arrive at specific outcomes. This has the potential to hinder the ethical use of AI in a clinical setting. Explainable AI (XAI) enables clinicians to interpret and consequently improve trust for outcomes predicted by ML models. This review critically examines emerging trends in XAI applied to lung cancer modeling. Novel XAI implementations in tasks like weakly supervised lesion localization, prognostic models, and survival analysis are highlighted. Furthermore, this study explores the extend of clinician contributions in the development of XAI, the impact of interobserver variability, the evaluation and scoring of explanation maps, the adaptation of XAI methods to medical imaging, and lung-specific attributes that may influence XAI. Novel extensions to the current state-of-the-art are also discussed critically throughout this study.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A critical review of explainable deep learning in lung cancer diagnosis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月9日

查看全文
  • Emmanouil Koutoulakis,
  • Eleftherios Trivizakis,
  • Emmanouil Markodimitrakis,
  • Sophia Agelaki,
  • Manolis Tsiknakis,
  • Kostas Marias

摘要

In the current research landscape, there is a plethora of artificial intelligence methods for medical image analysis, improving diagnostic accuracy; however, AI introduces challenges related to trustworthiness and transparency of decisions. Clinicians and medical experts often find it difficult to comprehend the process by which machine learning models arrive at specific outcomes. This has the potential to hinder the ethical use of AI in a clinical setting. Explainable AI (XAI) enables clinicians to interpret and consequently improve trust for outcomes predicted by ML models. This review critically examines emerging trends in XAI applied to lung cancer modeling. Novel XAI implementations in tasks like weakly supervised lesion localization, prognostic models, and survival analysis are highlighted. Furthermore, this study explores the extend of clinician contributions in the development of XAI, the impact of interobserver variability, the evaluation and scoring of explanation maps, the adaptation of XAI methods to medical imaging, and lung-specific attributes that may influence XAI. Novel extensions to the current state-of-the-art are also discussed critically throughout this study.