Explainable Artificial Intelligence in the Healthcare Industry

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.

• Preface xxix1 A Review on Explainable Artificial Intelligence for Healthcare 1Rakhi Chauhan2 Explainable Artificial Intelligence (XAI) in Healthcare: Fostering Transparency, Accountability, and Responsible AI Deployment 17Asha S. Manek, Shruti Vashist, Geeta Tripathi and Savita Sindhu3 Illuminating the Diagnostic Path: Unveiling Explainability in Medical Imaging 39Sivanantham S., Anwar Basha H., Thanuja K., Shafiya Banu M., Maithili K. and AnilKumar Ambore4 HealsHealthAI: Unveiling Personalized Healthcare Insights with Open Source Fine-Tuned LLM 67Lavan J. V. and Lakshmi Sangeetha5 Introduction to Explainable AI in EEG Signal Processing: A Review 79Parag Puranik and Rahul Pethe6 Transparency in Disease Diagnosis: Leveraging Interpretable Machine Learning in Healthcare 105Inam Ul Haq, Adil Husain Rather, Syed Zoofa Rufai, Ahmad Shah, Sheetal and Akib Mohi Ud Din Khanday7 Transparency in Text: Unraveling Explainability in Healthcare Natural Language Processing 131Madhan Veeramani, Karthick P., S. Venkateswaran, Sriman B., Shaik Thasleem Bhanu and V. Seedha Devi8 Introduction to Explainable AI in Healthcare: Enhancing Transparency and Trust 161Karthik Srinivasan, Chaithanya Kumar Viralam Ramamurthy, Saravanan Matheswaran and Shermin Shamsudheen9 Interpretable Machine Learning Techniques 185V. Kavitha, K. Suresh, G. Priyadharshini, Shaik Rasheeda Begum and R. Vidhya10 Interpretable Machine Learning Techniques in AI 209Shavez, Poornima, Kanu Goyal, Shweta Sharma and Parul Sharma11 Interpretable Machine Learning Techniques in Medical System—The Role of Data Analytics and Machine Learning 233Venkataraman P., Sunantha D. and Lakshmi S.12 Interpretable AI: Shedding Light on Medical Image Analysis Using Machine Learning Techniques 257S. Bashyam, P. Supraja and Prithiviraj Rajalingam13 Exploring the Role of Explainable AI in Women’s Health: Challenges and Solutions 283Inam Ul Haq and Akib Mohi Ud Din Khanday14 Explainable AI in Healthcare: Introduction 307Amandeep Kaur and Sonali Goyal15 Ethical Implications of Emotion Recognition Technology in Mental Healthcare: Navigating Privacy, Bias, and Therapeutic Boundaries 325R. Ravi, V. Jeya Ramya, B. Prameela Rani, Srikanth Nalluri and M. Jenath16 Bridging the Gap: Clinical Adoption and User Perspectives of Explainable AI in Healthcare 349Shaik Masood Ahamed and J. Jabez17 Application of AI-Based Technologies in the Healthcare Sector: Opportunities, Challenges, and Its Impact—Review 375G. Jegadeeswari and B. Kirubadurai18 A Complete Road Map for Interpretable Machine Learning Techniques Harnessing Various Real-Time Applications 393A. Pandian, V. V. Ramalingam, J. Venkata Subramanian, K. Pradeep Mohan Kumar and S. Padmini19 Future Research Directions: Explainable Artificial Intelligence in Healthcare Industry 423Shamneesh Sharma, Neha Kumra, Meghna Luthra, Vikas Verma and Komal Sharma20 Real-World Applications of Explainable AI in Healthcare 451Urvi, Parul Sharma, Kanu Goyal and Shweta Sharma21 Explainable AI in Medical Imaging, Personalized Medicine, and Bias Reduction: A New Era in Healthcare 467Komal, Ganesh K. Sethi, Shamneesh Sharma and Rajender Kumar22 Understanding Explainability in Medical Imaging 493Annie Silviya S. H., R. Tamizh Kuzhali, Akshaya V., Lakshmi Prabha T. S., Immanuvel Arokia James K. and B. Sriman23 Explainability and Regulatory Compliance in Healthcare: Bridging the Gap for Ethical XAI Implementation 521Uma Maheswari Kalia Moorthy, Asthampatti Marimuthu Jayapalan Muthukumaran, Vijayalakshmi Kaliyaperumal, Shobana Jayakumar and Kalpana Ayanellore Vijayaraghavan24 Envisioning Explainable AI: Significance, Real-Time Applications, and Challenges in Healthcare 563Kannan Chakrapani, Mohamed Iqubal Safa, Saranya Gangadhara Moorthy, Meenakshi Kumaraswamy and George Parimala25 Enlightened XAI: Illuminating Ethics and Equitable Explainability 593Hemalatha P., Manikandan J., B. Balaji and V. Sujitha26 Enhancing Trust and Collaboration Using Explainability in Natural Language Processing for AI-Driven Healthcare 619A. Pandian, K. Pradeep Mohankumar, S. Padmini, Sibi Amaran and K. SreekumarAbout the Editors 651Index 653