Argumentation approaches for explanaible AI in medical informatics

Caroprese, Luciano; Vocaturo, Eugenio; Zumpano, Ester

doi:10.1016/j.iswa.2022.200109

Cited by 30 publications

(10 citation statements)

References 37 publications

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“…Similarly, Caroprese et al ( 2022 ) explores argumentation approaches in XAI, offering structured justifications for medical decisions, thereby improving explainability and transparency. Although XAI has empowered radiologists with better interpretative insights, it still faces challenges, such as the potential for misinterpretation and the need for improved methods to accurately reflect the underlying model logic.…”

Section: Literature Reviewmentioning

confidence: 99%

Toward explainable AI in radiology: Ensemble-CAM for effective thoracic disease localization in chest X-ray images using weak supervised learning

Aasem,

Javed Iqbal

2024

Front. Big Data

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Chest X-ray (CXR) imaging is widely employed by radiologists to diagnose thoracic diseases. Recently, many deep learning techniques have been proposed as computer-aided diagnostic (CAD) tools to assist radiologists in minimizing the risk of incorrect diagnosis. From an application perspective, these models have exhibited two major challenges: (1) They require large volumes of annotated data at the training stage and (2) They lack explainable factors to justify their outcomes at the prediction stage. In the present study, we developed a class activation mapping (CAM)-based ensemble model, called Ensemble-CAM, to address both of these challenges via weakly supervised learning by employing explainable AI (XAI) functions. Ensemble-CAM utilizes class labels to predict the location of disease in association with interpretable features. The proposed work leverages ensemble and transfer learning with class activation functions to achieve three objectives: (1) minimizing the dependency on strongly annotated data when locating thoracic diseases, (2) enhancing confidence in predicted outcomes by visualizing their interpretable features, and (3) optimizing cumulative performance via fusion functions. Ensemble-CAM was trained on three CXR image datasets and evaluated through qualitative and quantitative measures via heatmaps and Jaccard indices. The results reflect the enhanced performance and reliability in comparison to existing standalone and ensembled models.

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Section: Literature Reviewmentioning

confidence: 99%

Toward explainable AI in radiology: Ensemble-CAM for effective thoracic disease localization in chest X-ray images using weak supervised learning

Aasem,

Javed Iqbal

2024

Front. Big Data

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“…The need for Explainable AI (XAI) is recognized, particularly in the medical field where decisions impact lives. Caroprese et al ( 2022 ) examined the benefits of using logic approaches for XAI, focusing on argumentation theory in Medical Informatics. Three categories were identified: Argumentation for Medical Decision Making, Explanations, and Dialogues.…”

Section: Related Workmentioning

confidence: 99%

Exploring dermoscopic structures for melanoma lesions' classification

Malik,

Yousaf,

Sial

et al. 2024

Front. Big Data

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BackgroundMelanoma is one of the deadliest skin cancers that originate from melanocytes due to sun exposure, causing mutations. Early detection boosts the cure rate to 90%, but misclassification drops survival to 15–20%. Clinical variations challenge dermatologists in distinguishing benign nevi and melanomas. Current diagnostic methods, including visual analysis and dermoscopy, have limitations, emphasizing the need for Artificial Intelligence understanding in dermatology.ObjectivesIn this paper, we aim to explore dermoscopic structures for the classification of melanoma lesions. The training of AI models faces a challenge known as brittleness, where small changes in input images impact the classification. A study explored AI vulnerability in discerning melanoma from benign lesions using features of size, color, and shape. Tests with artificial and natural variations revealed a notable decline in accuracy, emphasizing the necessity for additional information, such as dermoscopic structures.MethodologyThe study utilizes datasets with clinically marked dermoscopic images examined by expert clinicians. Transformers and CNN-based models are employed to classify these images based on dermoscopic structures. Classification results are validated using feature visualization. To assess model susceptibility to image variations, classifiers are evaluated on test sets with original, duplicated, and digitally modified images. Additionally, testing is done on ISIC 2016 images. The study focuses on three dermoscopic structures crucial for melanoma detection: Blue-white veil, dots/globules, and streaks.ResultsIn evaluating model performance, adding convolutions to Vision Transformers proves highly effective for achieving up to 98% accuracy. CNN architectures like VGG-16 and DenseNet-121 reach 50–60% accuracy, performing best with features other than dermoscopic structures. Vision Transformers without convolutions exhibit reduced accuracy on diverse test sets, revealing their brittleness. OpenAI Clip, a pre-trained model, consistently performs well across various test sets. To address brittleness, a mitigation method involving extensive data augmentation during training and 23 transformed duplicates during test time, sustains accuracy.ConclusionsThis paper proposes a melanoma classification scheme utilizing three dermoscopic structures across Ph2 and Derm7pt datasets. The study addresses AI susceptibility to image variations. Despite a small dataset, future work suggests collecting more annotated datasets and automatic computation of dermoscopic structural features.

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“…Moreover, there is a growing interest in solutions based on argumentation frameworks. Argumentation approaches provide a structured framework for reasoning and decision-making, allowing for the explicit representation of different perspectives, uncertainties, and reasoning paths involved in AI systems (Caroprese et al, 2022). There is a concern that if adopted, healthcare providers may become reliant on the use of such platforms which would result in induction of "automation bias" which may result in unintended incidence of medical error when clinical judgement and expertise is not prioritized.…”

Section: Advantages and Disadvantages Of ML Models Over Traditional S...mentioning

confidence: 99%

Application of machine learning for lung cancer survival prognostication—A systematic review and meta-analysis

Didier,

Nigro,

Noori

et al. 2024

Front. Artif. Intell.

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IntroductionMachine learning (ML) techniques have gained increasing attention in the field of healthcare, including predicting outcomes in patients with lung cancer. ML has the potential to enhance prognostication in lung cancer patients and improve clinical decision-making. In this systematic review and meta-analysis, we aimed to evaluate the performance of ML models compared to logistic regression (LR) models in predicting overall survival in patients with lung cancer.MethodsWe followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. A comprehensive search was conducted in Medline, Embase, and Cochrane databases using a predefined search query. Two independent reviewers screened abstracts and conflicts were resolved by a third reviewer. Inclusion and exclusion criteria were applied to select eligible studies. Risk of bias assessment was performed using predefined criteria. Data extraction was conducted using the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS) checklist. Meta-analytic analysis was performed to compare the discriminative ability of ML and LR models.ResultsThe literature search resulted in 3,635 studies, and 12 studies with a total of 211,068 patients were included in the analysis. Six studies reported confidence intervals and were included in the meta-analysis. The performance of ML models varied across studies, with C-statistics ranging from 0.60 to 0.85. The pooled analysis showed that ML models had higher discriminative ability compared to LR models, with a weighted average C-statistic of 0.78 for ML models compared to 0.70 for LR models.ConclusionMachine learning models show promise in predicting overall survival in patients with lung cancer, with superior discriminative ability compared to logistic regression models. However, further validation and standardization of ML models are needed before their widespread implementation in clinical practice. Future research should focus on addressing the limitations of the current literature, such as potential bias and heterogeneity among studies, to improve the accuracy and generalizability of ML models for predicting outcomes in patients with lung cancer. Further research and development of ML models in this field may lead to improved patient outcomes and personalized treatment strategies.

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Argumentation approaches for explanaible AI in medical informatics

Cited by 30 publications

References 37 publications

Toward explainable AI in radiology: Ensemble-CAM for effective thoracic disease localization in chest X-ray images using weak supervised learning

Toward explainable AI in radiology: Ensemble-CAM for effective thoracic disease localization in chest X-ray images using weak supervised learning

Exploring dermoscopic structures for melanoma lesions' classification

Application of machine learning for lung cancer survival prognostication—A systematic review and meta-analysis

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