XAI-Based Clinical Decision Support Systems: A Systematic Review

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(15), P. 6638 - 6638

Published: July 30, 2024

With increasing electronic medical data and the development of artificial intelligence, clinical decision support systems (CDSSs) assist clinicians in diagnosis prescription. Traditional knowledge-based CDSSs follow an accumulated knowledgebase a predefined rule system, which clarifies decision-making process; however, maintenance cost issues exist quality control standardization processes. Non-knowledge-based utilize vast amounts algorithms to effectively make decisions; deep learning black-box problem causes unreliable results. EXplainable Artificial Intelligence (XAI)-based provide valid rationales explainable These ensure trustworthiness transparency by showing recommendation prediction result process using techniques. However, existing have limitations, such as scope utilization lack explanatory power AI models. This study proposes new XAI-based CDSS framework address these issues; introduces resources, datasets, models that can be utilized; provides foundation model various disease domains. Finally, we propose future directions for technology highlight societal need addressed emphasize potential future.

Language: Английский

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Advanced Deep Learning Techniques for Battery Thermal Management in New Energy Vehicles

Energies, Journal Year: 2024, Volume and Issue: 17(16), P. 4132 - 4132

Published: Aug. 19, 2024

In the current era of energy conservation and emission reduction, development electric other new vehicles is booming. With their various attributes, lithium batteries have become ideal power source for vehicles. However, lithium-ion are highly sensitive to temperature changes. Excessive temperatures, either high or low, can lead abnormal operation batteries, posing a threat safety entire vehicle. Therefore, developing reliable efficient Battery Thermal Management System (BTMS) that monitor battery status prevent thermal runaway becoming increasingly important. recent years, deep learning has gradually widely applied in fields as an method, it also been some extent BTMS. this work, we discuss basic principles related optimization elaborate on algorithmic principles, frameworks, applications advanced methods We several emerging algorithms proposed feasibility BTMS applications. Finally, obstacles faced by potential directions development, proposing ideas progress. This paper aims analyze technologies commonly used provide insights into combination technology trams assist

Language: Английский

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SCGAN: Semi-Centralized Generative Adversarial Network for image generation in distributed scenes

Information Fusion, Journal Year: 2024, Volume and Issue: 112, P. 102556 - 102556

Published: July 3, 2024

Language: Английский

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Generative Adversarial Network for Synthetic Image Generation Method: Review, Analysis, and Perspective

Springer eBooks, Journal Year: 2024, Volume and Issue: unknown, P. 91 - 116

Published: Jan. 1, 2024

Language: Английский

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Augmenting the diversity of imbalanced datasets via multi-vector stochastic exploration oversampling

Neurocomputing, Journal Year: 2024, Volume and Issue: 583, P. 127600 - 127600

Published: March 24, 2024

Language: Английский

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