Discover the essential insights and practical applications of explainable AI in healthcare that will empower professionals and enhance patient trust with Explainable AI in the Healthcare Industry, a must-have resource. Explainable AI (XAI) has significant implications for the healthcare industry, where trust, accountability, and interpretability are crucial factors for the adoption of artificial intelligence. XAI techniques in healthcare aim to provide clear and understandable explanations for AI-driven decisions, helping healthcare professionals, patients, and regulatory bodies to better…mehr
Discover the essential insights and practical applications of explainable AI in healthcare that will empower professionals and enhance patient trust with Explainable AI in the Healthcare Industry, a must-have resource. Explainable AI (XAI) has significant implications for the healthcare industry, where trust, accountability, and interpretability are crucial factors for the adoption of artificial intelligence. XAI techniques in healthcare aim to provide clear and understandable explanations for AI-driven decisions, helping healthcare professionals, patients, and regulatory bodies to better comprehend and trust the AI models' outputs. Explainable AI in the Healthcare Industry presents a comprehensive exploration of the critical role of explainable AI in revolutionizing the healthcare industry. With the rapid integration of AI-driven solutions in medical practice, understanding how these models arrive at their decisions is of paramount importance. The book delves into the principles, methodologies, and practical applications of XAI techniques specifically tailored for healthcare settings.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Abhishek Kumar, PhD, is an Assistant Director and associate professor in the Computer Science and Engineering Department at Chandigarh University, India with over 11 years of teaching experience. He has over 100 publications in reputed, peer-reviewed national and international journals, books, and conferences, six internationally published book, and has edited over 27 books. Additionally, he has been a session chair and keynote speaker for many international conferences and webinars in India and abroad. T. Ananth Kumar, PhD, is an associate professor at the Indo-French Educational Trust College of Engineering. He has presented papers in various national and international conferences and journals, as well as published many book chapters. His fields of interest include networks on chips, computer architecture, and application-specific integrated circuit design. Prasenjit Das, PhD, is a professor in the Department of Computer Science and Engineering at Chandigarh University, Punja, India with over 19 years of experience in academics and the IT industry. He has more than 20 research papers and two books to his credit and has filed more than 25 patents, three of which have been granted. Apart from data mining, his other areas of research include machine learning, image processing, and natural language processing. Chetan Sharma is a program manager at the upGrad Campus, upGradEducation Private Limited, India with more than 15 years of experience in academics, administration, and EdTech. He has published more than 42 research manuscripts in various national and international journals and conferences and presented papers at several national and international conferences. In addition to this, he serves as a reviewer for various journals and conferences and filed more than 30 patents, eight of which have been granted by the Indian Patent Office. Ashutosh Kumar Dubey, PhD, is an associate professor in the Department of Computer Science, School of Engineering and Technology, Chitkara University, India with more than 14 years of teaching experience. He is also a postdoctoral fellow for the Ingenium Research Group Lab, Universidad de Castilla-La Mancha, Ciudad Real, Spain. He has authored and edited ten books and published over 50 articles in peer-reviewed international journals and conference proceedings. Additionally, he is a senior member of the Institute for Electronics and Electrical Engineers and Association for Computing Machinery, as well as an editor editorial board member, and reviewer for many peer-reviewed journals.
Inhaltsangabe
Preface xxix 1 A Review on Explainable Artificial Intelligence for Healthcare 1 Rakhi Chauhan 2 Explainable Artificial Intelligence (XAI) in Healthcare: Fostering Transparency, Accountability, and Responsible AI Deployment 17 Asha S. Manek, Shruti Vashist, Geeta Tripathi and Savita Sindhu 3 Illuminating the Diagnostic Path: Unveiling Explainability in Medical Imaging 39 Sivanantham S., Anwar Basha H., Thanuja K., Shafiya Banu M., Maithili K. and AnilKumar Ambore 4 HealsHealthAI: Unveiling Personalized Healthcare Insights with Open Source Fine-Tuned LLM 67 Lavan J. V. and Lakshmi Sangeetha 5 Introduction to Explainable AI in EEG Signal Processing: A Review 79 Parag Puranik and Rahul Pethe 6 Transparency in Disease Diagnosis: Leveraging Interpretable Machine Learning in Healthcare 105 Inam Ul Haq, Adil Husain Rather, Syed Zoofa Rufai, Ahmad Shah, Sheetal and Akib Mohi Ud Din Khanday 7 Transparency in Text: Unraveling Explainability in Healthcare Natural Language Processing 131 Madhan Veeramani, Karthick P., S. Venkateswaran, Sriman B., Shaik Thasleem Bhanu and V. Seedha Devi 8 Introduction to Explainable AI in Healthcare: Enhancing Transparency and Trust 161 Karthik Srinivasan, Chaithanya Kumar Viralam Ramamurthy, Saravanan Matheswaran and Shermin Shamsudheen 9 Interpretable Machine Learning Techniques 185 V. Kavitha, K. Suresh, G. Priyadharshini, Shaik Rasheeda Begum and R. Vidhya 10 Interpretable Machine Learning Techniques in AI 209 Shavez, Poornima, Kanu Goyal, Shweta Sharma and Parul Sharma 11 Interpretable Machine Learning Techniques in Medical System-The Role of Data Analytics and Machine Learning 233 Venkataraman P., Sunantha D. and Lakshmi S. 12 Interpretable AI: Shedding Light on Medical Image Analysis Using Machine Learning Techniques 257 S. Bashyam, P. Supraja and Prithiviraj Rajalingam 13 Exploring the Role of Explainable AI in Women's Health: Challenges and Solutions 283 Inam Ul Haq and Akib Mohi Ud Din Khanday 14 Explainable AI in Healthcare: Introduction 307 Amandeep Kaur and Sonali Goyal 15 Ethical Implications of Emotion Recognition Technology in Mental Healthcare: Navigating Privacy, Bias, and Therapeutic Boundaries 325 R. Ravi, V. Jeya Ramya, B. Prameela Rani, Srikanth Nalluri and M. Jenath 16 Bridging the Gap: Clinical Adoption and User Perspectives of Explainable AI in Healthcare 349 Shaik Masood Ahamed and J. Jabez 17 Application of AI-Based Technologies in the Healthcare Sector: Opportunities, Challenges, and Its Impact-Review 375 G. Jegadeeswari and B. Kirubadurai 18 A Complete Road Map for Interpretable Machine Learning Techniques Harnessing Various Real-Time Applications 393 A. Pandian, V. V. Ramalingam, J. Venkata Subramanian, K. Pradeep Mohan Kumar and S. Padmini 19 Future Research Directions: Explainable Artificial Intelligence in Healthcare Industry 423 Shamneesh Sharma, Neha Kumra, Meghna Luthra, Vikas Verma and Komal Sharma 20 Real-World Applications of Explainable AI in Healthcare 451 Urvi, Parul Sharma, Kanu Goyal and Shweta Sharma 21 Explainable AI in Medical Imaging, Personalized Medicine, and Bias Reduction: A New Era in Healthcare 467 Komal, Ganesh K. Sethi, Shamneesh Sharma and Rajender Kumar 22 Understanding Explainability in Medical Imaging 493 Annie Silviya S. H., R. Tamizh Kuzhali, Akshaya V., Lakshmi Prabha T. S., Immanuvel Arokia James K. and B. Sriman 23 Explainability and Regulatory Compliance in Healthcare: Bridging the Gap for Ethical XAI Implementation 521 Uma Maheswari Kalia Moorthy, Asthampatti Marimuthu Jayapalan Muthukumaran, Vijayalakshmi Kaliyaperumal, Shobana Jayakumar and Kalpana Ayanellore Vijayaraghavan 24 Envisioning Explainable AI: Significance, Real-Time Applications, and Challenges in Healthcare 563 Kannan Chakrapani, Mohamed Iqubal Safa, Saranya Gangadhara Moorthy, Meenakshi Kumaraswamy and George Parimala 25 Enlightened XAI: Illuminating Ethics and Equitable Explainability 593 Hemalatha P., Manikandan J., B. Balaji and V. Sujitha 26 Enhancing Trust and Collaboration Using Explainability in Natural Language Processing for AI-Driven Healthcare 619 A. Pandian, K. Pradeep Mohankumar, S. Padmini, Sibi Amaran and K. Sreekumar About the Editors 651 Index 653
Preface xxix 1 A Review on Explainable Artificial Intelligence for Healthcare 1 Rakhi Chauhan 2 Explainable Artificial Intelligence (XAI) in Healthcare: Fostering Transparency, Accountability, and Responsible AI Deployment 17 Asha S. Manek, Shruti Vashist, Geeta Tripathi and Savita Sindhu 3 Illuminating the Diagnostic Path: Unveiling Explainability in Medical Imaging 39 Sivanantham S., Anwar Basha H., Thanuja K., Shafiya Banu M., Maithili K. and AnilKumar Ambore 4 HealsHealthAI: Unveiling Personalized Healthcare Insights with Open Source Fine-Tuned LLM 67 Lavan J. V. and Lakshmi Sangeetha 5 Introduction to Explainable AI in EEG Signal Processing: A Review 79 Parag Puranik and Rahul Pethe 6 Transparency in Disease Diagnosis: Leveraging Interpretable Machine Learning in Healthcare 105 Inam Ul Haq, Adil Husain Rather, Syed Zoofa Rufai, Ahmad Shah, Sheetal and Akib Mohi Ud Din Khanday 7 Transparency in Text: Unraveling Explainability in Healthcare Natural Language Processing 131 Madhan Veeramani, Karthick P., S. Venkateswaran, Sriman B., Shaik Thasleem Bhanu and V. Seedha Devi 8 Introduction to Explainable AI in Healthcare: Enhancing Transparency and Trust 161 Karthik Srinivasan, Chaithanya Kumar Viralam Ramamurthy, Saravanan Matheswaran and Shermin Shamsudheen 9 Interpretable Machine Learning Techniques 185 V. Kavitha, K. Suresh, G. Priyadharshini, Shaik Rasheeda Begum and R. Vidhya 10 Interpretable Machine Learning Techniques in AI 209 Shavez, Poornima, Kanu Goyal, Shweta Sharma and Parul Sharma 11 Interpretable Machine Learning Techniques in Medical System-The Role of Data Analytics and Machine Learning 233 Venkataraman P., Sunantha D. and Lakshmi S. 12 Interpretable AI: Shedding Light on Medical Image Analysis Using Machine Learning Techniques 257 S. Bashyam, P. Supraja and Prithiviraj Rajalingam 13 Exploring the Role of Explainable AI in Women's Health: Challenges and Solutions 283 Inam Ul Haq and Akib Mohi Ud Din Khanday 14 Explainable AI in Healthcare: Introduction 307 Amandeep Kaur and Sonali Goyal 15 Ethical Implications of Emotion Recognition Technology in Mental Healthcare: Navigating Privacy, Bias, and Therapeutic Boundaries 325 R. Ravi, V. Jeya Ramya, B. Prameela Rani, Srikanth Nalluri and M. Jenath 16 Bridging the Gap: Clinical Adoption and User Perspectives of Explainable AI in Healthcare 349 Shaik Masood Ahamed and J. Jabez 17 Application of AI-Based Technologies in the Healthcare Sector: Opportunities, Challenges, and Its Impact-Review 375 G. Jegadeeswari and B. Kirubadurai 18 A Complete Road Map for Interpretable Machine Learning Techniques Harnessing Various Real-Time Applications 393 A. Pandian, V. V. Ramalingam, J. Venkata Subramanian, K. Pradeep Mohan Kumar and S. Padmini 19 Future Research Directions: Explainable Artificial Intelligence in Healthcare Industry 423 Shamneesh Sharma, Neha Kumra, Meghna Luthra, Vikas Verma and Komal Sharma 20 Real-World Applications of Explainable AI in Healthcare 451 Urvi, Parul Sharma, Kanu Goyal and Shweta Sharma 21 Explainable AI in Medical Imaging, Personalized Medicine, and Bias Reduction: A New Era in Healthcare 467 Komal, Ganesh K. Sethi, Shamneesh Sharma and Rajender Kumar 22 Understanding Explainability in Medical Imaging 493 Annie Silviya S. H., R. Tamizh Kuzhali, Akshaya V., Lakshmi Prabha T. S., Immanuvel Arokia James K. and B. Sriman 23 Explainability and Regulatory Compliance in Healthcare: Bridging the Gap for Ethical XAI Implementation 521 Uma Maheswari Kalia Moorthy, Asthampatti Marimuthu Jayapalan Muthukumaran, Vijayalakshmi Kaliyaperumal, Shobana Jayakumar and Kalpana Ayanellore Vijayaraghavan 24 Envisioning Explainable AI: Significance, Real-Time Applications, and Challenges in Healthcare 563 Kannan Chakrapani, Mohamed Iqubal Safa, Saranya Gangadhara Moorthy, Meenakshi Kumaraswamy and George Parimala 25 Enlightened XAI: Illuminating Ethics and Equitable Explainability 593 Hemalatha P., Manikandan J., B. Balaji and V. Sujitha 26 Enhancing Trust and Collaboration Using Explainability in Natural Language Processing for AI-Driven Healthcare 619 A. Pandian, K. Pradeep Mohankumar, S. Padmini, Sibi Amaran and K. Sreekumar About the Editors 651 Index 653
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