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024 7 _a10.1007/978-981-97-3705-5
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050 4 _aTA347.A78
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072 7 _aCOM004000
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245 1 0 _aExplainable AI in Health Informatics
_h[electronic resource] /
_cedited by Rajanikanth Aluvalu, Mayuri Mehta, Patrick Siarry.
250 _a1st ed. 2024.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2024.
300 _aXVII, 276 p. 77 illus., 64 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
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490 1 _aComputational Intelligence Methods and Applications,
_x2510-1773
505 0 _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.
520 _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.
650 0 _aArtificial intelligence.
_93407
650 0 _aMedical informatics.
_94729
650 0 _aBiomedical engineering.
_93292
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aHealth Informatics.
_931799
650 2 4 _aMedical and Health Technologies.
_939549
700 1 _aAluvalu, Rajanikanth.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_9105244
700 1 _aMehta, Mayuri.
_eeditor.
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_4http://id.loc.gov/vocabulary/relators/edt
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700 1 _aSiarry, Patrick.
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_4http://id.loc.gov/vocabulary/relators/edt
_9105247
710 2 _aSpringerLink (Online service)
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773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
_z9789819737079
830 0 _aComputational Intelligence Methods and Applications,
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_9105253
856 4 0 _uhttps://doi.org/10.1007/978-981-97-3705-5
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