04118nam a22005775i 4500 978-981-97-3705-5 DE-He213 20251030112619.0 cr nn 008mamaa 240708s2024 si | s |||| 0|eng d 9789819737055 978-981-97-3705-5 10.1007/978-981-97-3705-5 doi Q334-342 TA347.A78 UYQ bicssc COM004000 bisacsh UYQ thema Explainable AI in Health Informatics [electronic resource] / edited by Rajanikanth Aluvalu, Mayuri Mehta, Patrick Siarry. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. XVII, 276 p. 77 illus., 64 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Computational Intelligence Methods and Applications, 2510-1773 Chapter 1. Introduction to Explainable AI -- Chapter 2. Explainable AI Methods and Applications -- Chapter 3. Unveil the Black Box Model for Healthcare Explainable AI -- Chapter 4. Explainable AI: Methods, Frameworks, and Tools for Healthcare 5.0 -- Chapter 5. Explainable AI in Disease Diagnosis -- Chapter 6. Explainable Artificial Intelligence in Drug Discovery -- Chapter 7. Explainable AI for Big Data Control -- Chapter 8. Patient Data Analytics using XAI- Existing Tools & Case Studies -- Chapter 9. Enhancing Diagnosis of Kidney Ailments from CT Scan with Explainable AI -- Chapter 10. Explainable AI for Colorectal Cancer Classification -- Chapter 11. Explainable AI (XAI)-based Robot-Assisted Surgical classification Procedure -- Chapter 12. Explainable AI Case Studies in Healthcare. This book provides a comprehensive review of the latest research in the area of explainable artificial intelligence (XAI) in health informatics. It focuses on how explainable AI models can work together with humans to assist them in decision-making, leading to improved diagnosis and prognosis in healthcare. This book includes a collection of techniques and systems of XAI in health informatics and gives a wider perspective about the impact created by them. The book covers the different aspects, such as robotics, informatics, drugs, patients, etc., related to XAI in healthcare. The book is suitable for both beginners and advanced AI practitioners, including students, academicians, researchers, and industry professionals. It serves as an excellent reference for undergraduate and graduate-level courses on AI for medicine/healthcare or XAI for medicine/healthcare. Medical institutions can also utilize this book as reference material and provide tutorials to medical professionals on how the XAI techniques can contribute to trustworthy diagnosis and prediction of the diseases. Artificial intelligence. 58441 Medical informatics. 60693 Biomedical engineering. Artificial Intelligence. 58441 Health Informatics. Medical and Health Technologies. Aluvalu, Rajanikanth. editor. edt http://id.loc.gov/vocabulary/relators/edt 80205 Mehta, Mayuri. editor. edt http://id.loc.gov/vocabulary/relators/edt 76415 Siarry, Patrick. editor. edt http://id.loc.gov/vocabulary/relators/edt 75633 SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819737048 Printed edition: 9789819737062 Printed edition: 9789819737079 Computational Intelligence Methods and Applications, 2510-1773 70583 https://doi.org/10.1007/978-981-97-3705-5 ZDB-2-SCS ZDB-2-SXCS 61433 61433