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The review examines the role of explainable AI (XAI) in enhancing trust in lung cancer diagnoses using machine learning.

A critical review of explainable deep learning in lung cancer diagnosis

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The review examines the role of explainable AI (XAI) in enhancing trust in lung cancer diagnoses using machine learning.
Critical review of explainable AI applications in lung cancer diagnosis.
Evaluation of challenges related to trustworthiness and transparency in AI.
Analysis of weakly supervised lesion localization, prognostic models, and survival analysis.
XAI improves clinician understanding of machine learning outcomes.
Emerging trends in XAI are critical for ethical AI use in clinical practice.
Interobserver variability impacts the effectiveness of XAI in lung cancer modeling.

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This review highlights advancements in explainable AI for lung cancer diagnosis, indicating improved trustworthiness in machine learning outcomes.

Koutoulakis et al. (2025) studied this question.

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This review highlights advancements in explainable AI for lung cancer diagnosis, indicating improved trustworthiness in machine learning outcomes.