Explainable Machine Intelligence in Healthcare

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

List of 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.

About the author

Riccardo Ortale is currently a researcher at ICAR-CNR. He received his Ph.D. in Systems and Computer Engineering and graduated summa cum laude in Computer Science Engineering, both from the University of Calabria. He holds a Master's degree in Internet Software Design from Cefriel (Politecnico di Milano) and Siemens. His research interests span explainable/interpretable machine intelligence, knowledge discovery and data mining, intelligent information systems, recommender systems, text analysis, and social network/media analysis. He has published various scientific papers in international journals, conference proceedings, and volumes from renowned publishers. He has served as a program committee member for premier international conferences, including IEEE ICDM, IJCAI, ACM CIKM, SIAM SDM, and PAKDD. He has served as an associate/guest editor for numerous esteemed international journals. His service as a reviewer has been acknowledged on the corresponding WoS/ORCID profiles by several prestigious international journals. He has organized and co-chaired international workshops at premier international conferences.    
Gianni Costa is currently a researcher at the Institute of High Performance Computing and Networks (ICAR-CNR) of the National Research Council of Italy. He graduated summa cum laude in computer science engineering in 2003 and received Ph.D. in systems and computer engineering in 2007 from the University of Calabria, Italy. His research interests include data mining and knowledge discovery, recommender systems, social network analysis, explainable AI, and topic modeling. He published several scientific papers in international conferences and journals. He serves as a program committee member for various international conferences, as an associate editor for different international journal and as Guest Editor for diverse Special Issues among which: “Explainable Artificial Intelligence for Medical Applications” – Neural Computing and Applications, “Recent Applications of Machine Learning and Data Mining in Bioinformatics” – Applied Sciences, “Automatic Disinformation Detection on Social Media Platforms” – Data, “Role-Aware Analysis of Complex Networks” – Entropy.    
Agostino Forestiero is Senior Researcher at the Institute for High Performance Computing and Networking of Italian National Research Council (CNR). He received the National Scientific Habilitation as full professor of Information Processing Systems (09/H1).  He is adjunct professor of Medical Informatics at University of Calabria. He published more than 150 papers on international conferences and journals. His research interests include Artificial Intelligence, eHealth, Internet of Things and Cybersecurity. He co-founded the company eco4cloud srl, spin-off of the University of Calabria and CNR, and he filed six patents. He is Associate Editor of IEEE Access, Expert Systems with Applications (Elsevier), Frontiers in Big Data (Frontiers), and Editorial Board Member of Neural Computing and Applications (Springer), Engineering Application of Artificial Intelligence and Computer Communication (Elsevier), Computational Intelligence and Neuroscience, and Journal of Healthcare Engineering (Hindawi). He edited the book “Integrating Artificial Intelligence and IoT for Advanced Health Informatics”, Springer Series.

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

Product details

• Assisted by: Giovanni Costa (Editor), Agostino Forestiero (Editor), Riccardo Ortale (Editor)
• Publisher: Springer, Berlin
• Languages: English
• Product format: Hardback
• Release: 11.09.2025
• EAN: 9783031913785
• ISBN: 978-3-0-3191378-5
• No. of pages: 250
• Illustrations: Approx. 250 p.
• Subjects: Natural sciences, medicine, IT, technology > Medicine > General; Künstliche Intelligenz, Medizin, allgemein, Artificial Intelligence, Health Informatics, Health Sciences, Computational Healthcare Intelligence, Transparent Predictive Diagnostics, Comprehensible Healthcare Data Analysis