03923nam a22005295i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001300172050001400185072001600199072002300215072001500238245012800253250001800381264007200399300006600471336002600537337002600563338003600589347002400625490006800649505079900717520109101516650003602607650003202643650002802675650003602703650002402739650003702763700009002800700008302890700008502973710003403058773002603092776003603118776003603154776003603190830007503226856004603301912001403347912001503361999001703376978-981-97-3705-5DE-He21320251030112619.0cr nn 008mamaa240708s2024    si |    s    |||| 0|eng d  a97898197370559978-981-97-3705-57 a10.1007/978-981-97-3705-52doi 4aQ334-342 4aTA347.A78 7aUYQ2bicssc 7aCOM0040002bisacsh 7aUYQ2thema10aExplainable AI in Health Informaticsh[electronic resource] /cedited by Rajanikanth Aluvalu, Mayuri Mehta, Patrick Siarry.  a1st ed. 2024. 1aSingapore :bSpringer Nature Singapore :bImprint: Springer,c2024.  aXVII, 276 p. 77 illus., 64 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aComputational Intelligence Methods and Applications,x2510-17730 aChapter 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.  aThis 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. 0aArtificial intelligence.958441 0aMedical informatics.960693 0aBiomedical engineering.14aArtificial Intelligence.95844124aHealth Informatics.24aMedical and Health Technologies.1 aAluvalu, Rajanikanth.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9802051 aMehta, Mayuri.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9764151 aSiarry, Patrick.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9756332 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978981973704808iPrinted edition:z978981973706208iPrinted edition:z9789819737079 0aComputational Intelligence Methods and Applications,x2510-177397058340uhttps://doi.org/10.1007/978-981-97-3705-5  aZDB-2-SCS  aZDB-2-SXCS  c61433d61433