Explainable Machine Intelligence in Healthcare / edited by Riccardo Ortale, Giovanni Costa, Agostino Forestiero.

Format:

Book

Contributor:

Ortale, Riccardo., Editor.

Costa, G. (Giovanni), Editor.

Forestiero, Agostino, Editor.

Language:

English

Subjects (All):

Medical informatics.

Artificial intelligence.

Medical sciences.

Health Informatics.

Artificial Intelligence.

Health Sciences.

Local Subjects:

Health Informatics.

Artificial Intelligence.

Health Sciences.

Physical Description:

1 online resource (XII, 200 p. 60 illus., 43 illus. in color.)

Edition:

1st ed. 2025.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2025.

Summary:

This book provides an in-depth analysis of cutting-edge research on healthcare applications of explainable machine intelligence. It showcases an array of the most recent findings, applications, and empirical results, utilizing advanced approaches for effective and trustworthy analysis and diagnosis across multiple medical fields. Key areas are reviewed, with further investigation into challenges, perspectives, emerging trends, and opportunities. The transformative potential of explainable machine intelligence is revealed in reshaping the landscape of healthcare through the implementation of transparent and trustworthy approaches. Explainable Machine Intelligence in Healthcare offers valuable insights into the design, development, integration, and deployment of machine intelligence in healthcare delivery and patient care. It caters to a broad readership encompassing diverse stakeholders, such as academicians, researchers, scholars, instructors, practitioners, students, industry professionals, public health agencies, and NGOs.

Contents:

Demystifying Healthcare AI: The Role of Explainable AI in Enhancing Clinical Decision Support

Supporting Healthcare Decision-Making: A Journey through CDSS, Explainability, and Bias Mitigation with Explainable AI

Designing a LLM-based Methodology for Assisting Physicians in Complex Clinical Data Interpretation

Enhancing Decision Support in Healthcare with Explainable AI Models

Improving Explanation Measures Inside the Decision-Making Process to Increase the Potential of AI Applications in Healthcare

Batch Integrated Gradients: A Library for Explaining Temporal Electronic Health Records

Embeddings and Domain Knowledge for Grounded Interpretable Sleep Staging

Explainability-Enhanced Neural Network for Thoracic Diagnosis Improvement

Decision Tree Models to Select High Risk Patients for Lung Cancer Screening and Model Interpretability

Interpreting Convolutional Neural Networks for Brain Tumors: A Transparent Box Approach

Explainable Artificial Intelligence for Melanoma Detection

Shedding Light Inside the Black Box: Techniques for Explainable Artificial Intelligence in Healthcare

Detection of Frontotemporal Dementia using an Interpretable Machine Learning Approach

Evaluation of Explainability Methods in Medical Image Analysis

Unveiling the Synergy of Explainable AI and Advanced Computational Techniques in Tuberculosis Detection

Explainable AI in Healthcare: A Pathway to Trust and Regulatory Compliance.

ISBN:

9783031913792