Transforming Gender-Based Healthcare with AI and Machine Learning

This book provides a thorough exploration of the intersection between gender-based healthcare disparities and the transformative potential of AI and Machine Learning. It covers a wide range of topics from fundamental concepts to practical applications.

Dr. Meenu Gupta is an associate professor at the UIE-CSE Department, Chandigarh University, India. She is pursuing her Post Doc Fellowship from MIR Lab, USA. She completed her Ph.D. in Computer Science and Engineering from Ansal University, Gurgaon, India, in 2020. She has more than 16 years of teaching experience. Her research areas cover machine learning, intelligent systems, and data mining, with a specific interest in artificial intelligence, image processing and analysis, smart citiers, data analysis, and human/brain machine interaction (BMI). She has edited more than 17 books and authored four engineering books. She reviews several journals including Big Data, Artificial Intelligence Review, CMC, Scientific Reports, and Digital Health. She is a life member of ISTE and IAENG. She is also a senior member of IEEE. she has authored or co-authored more than 37 book chapters and over 200 papers in refereed international journals and conferenced. She also organized many conferences technically sponsored by the IEEE Delhi Section and AIP. Dr. Rakesh Kumar is professor and associate director at the UIE-CSE Department, Chandigarh University, Punjab, India. He is ursuing his Post Doc Fellowship from MIR Lab, USA. He completed his Ph.D. in Computer Science and Engineering from Punjab Technical University, Jalandhar in 2017. He has more than 20 years of teaching experience. His research interests are IoT, machine learning, and natural language processing. He has edited more than seven books with reputed publishers like Taylor & Francis Group, and authored five books. He works as a reviewer for several journals, including Big Data, CMC, Scientific Reports, TSP, Multimedia Tools and Applications, and IEEE Access. He is a senior member of the IEEE. He has authored or co-authored more than 170 publications in various national and international conferences and journals. He is also an organizer and editor of many international conferences under the ageis of IEEE and AIP. Dr. Zhongyu Lu is a professor in the Department of Computer Science and is the research group leader of information and system Engineering (ISE) at the Centre of High Intelligent Computing (CHIC). She was previously team leader in the IT department of Charlesworth Group Publishing Company. She successfully led and completed two research projects in XML database systems and document processing in collaboration with Beijing University. Both systems were deployed as part of company commercial productions. Professor Lu is UKCGE Recognized Research Supervisor (UK Council of Postgraduate Education) and has published 11 academic books and more than 200 peer reviewed academic papers. Her research publications have 35,606 reads and 1008 citations by international colleagues, according to incomplete statistics from ResearchGate, Scopus, and Google Scholar. Professor Lu has acted as the founder and program chair for the International XML Technology Workshop for 11 years and serves as chair of various international conferences. She is the founder and editor-in-chief of International Journal of Information Retrieval Research, serves as a BCS examiner of Database and Advanced Database Management Systems, and is an FHEA. She has been the UOH principle investigator for four recent EU interdisciplinary (computer science nad psychology) projects: Endurmecca (student responses systems) (143545-LLP-NO-KA3-KA3MP), DO-IT (multilingual student response system) used by more than 15 EU countries (2009--1-NO1-LEO05--01046), and DONE-IT (mobile exam system) (511485-LLP-1--2010-NO-KA3-KA3MP), HRLAW2016--3090/001--001.

1. AI and Machine Learning in Modern Healthcare. 2. Revolutionizing Gender-Specific Healthcare: Harnessing Deep Learning for Transformative Solutions. 3. From Data to Diagnosis: AI's Role in Gender-Responsive Care. 4. Technology-Driven Approaches to Gender Inclusive Healthcare. 5. Unlocking Gender-Based Health Insights with Predictive Analytics. 6. Machine Learning's Precision in Tailoring Healthcare Solutions. 7. Real-World Success Stories How Technology Transforms Gender Healthcare. 8. Patient-Centric Technology Empowerment. 9. Exploring Cutting-Edge Technologies Shaping Gender Health. 10. Safeguarding Data and Ensuring Security in Digital Healthcare. 11. AI and ML Fundamentals: A Primer for Healthcare Professionals. 12. Early Ethical Considerations and Societal Impacts on Gender Health. 13. Examination of AI's role in Diagnosis, Treatment, and Patient care. 14. Future Trends and Ethical Challenges in Transforming Gender Health Care Using AI and ML.