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

Bioengineering, Journal Year: 2024, Volume and Issue: 11(4), P. 369 - 369

Published: April 12, 2024

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

Language: Английский

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Transforming Dermatopathology With AI: Addressing Bias, Enhancing Interpretability, and Shaping Future Diagnostics

Dermatological Reviews, Journal Year: 2025, Volume and Issue: 6(1)

Published: Jan. 17, 2025

ABSTRACT Background Artificial intelligence (AI) is transforming dermatopathology by enhancing diagnostic accuracy, efficiency, and precision medicine. Despite its promise, challenges such as dataset biases, underrepresentation of diverse populations, limited transparency hinder widespread adoption. Addressing these gaps can set a new standard for equitable patient‐centered care. To evaluate how AI mitigates improves interpretability, promotes inclusivity in while highlighting novel technologies like multimodal models explainable (XAI). Results AI‐driven tools demonstrate significant improvements precision, particularly through that integrate histological, genetic, clinical data. Inclusive frameworks, the Monk scale, advanced segmentation methods effectively address biases. However, “black box” nature AI, ethical concerns about data privacy, access to low‐resource settings remain. Conclusion offers transformative potential dermatopathology, enabling equitable, innovative diagnostics. Overcoming persistent will require collaboration among dermatopathologists, developers, policymakers. By prioritizing inclusivity, transparency, interdisciplinary efforts, redefine global standards foster

Language: Английский

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Interpretable Machine Learning for Oral Lesion Diagnosis Through Prototypical Instances Identification

Lecture notes in computer science, Journal Year: 2025, Volume and Issue: unknown, P. 316 - 331

Published: Jan. 1, 2025

Language: Английский

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Enhanced Skin Disease Classification via Dataset Refinement and Attention-Based Vision Approach

Bioengineering, Journal Year: 2025, Volume and Issue: 12(3), P. 275 - 275

Published: March 11, 2025

Skin diseases are listed among the most frequently encountered diseases. such as eczema, melanoma, and others necessitate early diagnosis to avoid further complications. This study aims enhance of skin disease by utilizing advanced image processing techniques an attention-based vision approach support dermatologists in solving classification problems. Initially, is being passed through various steps quality dataset. These adaptive histogram equalization, binary cross-entropy with implicit averaging, gamma correction, contrast stretching. Afterwards, enhanced images for performing which based on encoder part transformers multi-head attention. Extensive experimentation performed collect results two publicly available datasets show robustness proposed approach. The evaluation shows competitive compared a state-of-the-art

Language: Английский

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Assessing Feature Importance in Eye-Tracking Data within Virtual Reality Using Explainable Artificial Intelligence Techniques

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(14), P. 6042 - 6042

Published: July 11, 2024

Our research systematically investigates the cognitive and emotional processes revealed through eye movements within context of virtual reality (VR) environments. We assess utility eye-tracking data for predicting states in VR, employing explainable artificial intelligence (XAI) to advance interpretability transparency our findings. Utilizing VR Eyes: Emotions dataset (VREED) alongside an extra trees classifier enhanced by SHapley Additive ExPlanations (SHAP) local interpretable model agnostic explanations (LIME), we rigorously evaluate importance various metrics. results identify significant correlations between metrics such as saccades, micro-saccades, blinks, fixations specific states. The application SHAP LIME elucidates these relationships, providing deeper insights into responses triggered VR. These findings suggest that variations feature patterns serve indicators heightened arousal. Not only do understanding affective computing but they also highlight potential developing more responsive systems capable adapting user emotions real-time. This contributes significantly fields human-computer interaction psychological research, showcasing how XAI can bridge gap complex machine-learning models practical applications, thereby facilitating creation reliable, user-sensitive experiences. Future may explore integration multiple physiological signals enhance emotion detection interactive dynamics

Language: Английский

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Identifying diagnostic biomarkers for Erythemato-Squamous diseases using explainable machine learning

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 100, P. 107101 - 107101

Published: Oct. 24, 2024

Language: Английский

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