Towards Reliable and Explainable AI Model for Solid Pulmonary Nodule Diagnosis

Wang, Chenglong; Liu, Yun; Wang, Fen; Zhang, Chengxiu; Wang, Yida; Yuan, Ming; Yang, Guang

doi:10.48550/arxiv.2204.04219

Cited by 2 publications

(2 citation statements)

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“…Moreover, XAI has been rigorously evaluated in diagnostic scenarios involving breast cancer [25], lung nodules [26], and brain tumors [27], among others. Across these evaluations, a diversity of XAI methodologies-including saliency maps, decision trees, and attribution maps-have been leveraged to illuminate the decision-making mechanisms of AI models.…”

Section: Related Workmentioning

confidence: 99%

Advancing Dermatological Diagnostics: Interpretable AI for Enhanced Skin Lesion Classification

Metta,

Beretta,

Guidotti

et al. 2024

Diagnostics

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A crucial challenge in critical settings like medical diagnosis is making deep learning models used in decision-making systems interpretable. Efforts in Explainable Artificial Intelligence (XAI) are underway to address this challenge. Yet, many XAI methods are evaluated on broad classifiers and fail to address complex, real-world issues, such as medical diagnosis. In our study, we focus on enhancing user trust and confidence in automated AI decision-making systems, particularly for diagnosing skin lesions, by tailoring an XAI method to explain an AI model’s ability to identify various skin lesion types. We generate explanations using synthetic images of skin lesions as examples and counterexamples, offering a method for practitioners to pinpoint the critical features influencing the classification outcome. A validation survey involving domain experts, novices, and laypersons has demonstrated that explanations increase trust and confidence in the automated decision system. Furthermore, our exploration of the model’s latent space reveals clear separations among the most common skin lesion classes, a distinction that likely arises from the unique characteristics of each class and could assist in correcting frequent misdiagnoses by human professionals.

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Section: Related Workmentioning

confidence: 99%

Advancing Dermatological Diagnostics: Interpretable AI for Enhanced Skin Lesion Classification

Metta,

Beretta,

Guidotti

et al. 2024

Diagnostics

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show abstract

“…While global methods aim at explaining the general reasoning of an AI model, local methods have the goal of explaining why an AI model gave a certain output for a particular instance, i.e., the data of a particular patient or the diagnostic images belonging to a specific individual. The application of local XAI methods addresses the demand for precise, caseby-case explanations, which are paramount for clinical decision making, enhancing patient care, and fostering trust in AI systems among healthcare providers and patients [6][7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Towards Transparent Healthcare: Advancing Local Explanation Methods in Explainable Artificial Intelligence

Metta,

Beretta,

Pellungrini

et al. 2024

Bioengineering

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This paper focuses on the use of local Explainable Artificial Intelligence (XAI) methods, particularly the Local Rule-Based Explanations (LORE) technique, within healthcare and medical settings. It emphasizes the critical role of interpretability and transparency in AI systems for diagnosing diseases, predicting patient outcomes, and creating personalized treatment plans. While acknowledging the complexities and inherent trade-offs between interpretability and model performance, our work underscores the significance of local XAI methods in enhancing decision-making processes in healthcare. By providing granular, case-specific insights, local XAI methods like LORE enhance physicians’ and patients’ understanding of machine learning models and their outcome. Our paper reviews significant contributions to local XAI in healthcare, highlighting its potential to improve clinical decision making, ensure fairness, and comply with regulatory standards.

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Towards Reliable and Explainable AI Model for Solid Pulmonary Nodule Diagnosis

Cited by 2 publications

References 0 publications

Advancing Dermatological Diagnostics: Interpretable AI for Enhanced Skin Lesion Classification

Advancing Dermatological Diagnostics: Interpretable AI for Enhanced Skin Lesion Classification

Towards Transparent Healthcare: Advancing Local Explanation Methods in Explainable Artificial Intelligence

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