AI for Medical Image Analysis : Reconciling Innovation and Ethical Considerations

The way doctors identify, treat, and manage illnesses has been completely transformed by the introduction of artificial intelligence (AI) into healthcare. The application of image processing and computer vision technologies is one of the most impactful advancements, which has boosted the accuracy and effectiveness of medical image analysis, enhanced treatment planning and enabled more personalized care. With the use of these technologies, healthcare professionals may now "see beyond" the limits of conventional imaging techniques, gaining deeper understanding and more thorough analyses—both essential for efficient patient care. However, applying AI techniques for medical image analysis has to be conducted while upholding the ethical considerations to ensure the technology benefits patients and healthcare providers while minimizing potential risks. In fact, it is essential to establish a thorough framework that incorporates stringent validation on diverse and representative datasets to mitigate bias and guarantee accuracy across different populations. AI systems must exhibit transparency and explainability, enabling healthcare professionals to comprehend and trust their outputs, while accountability measures distinctly delineate responsibility for AI-generated judgments. In addition, AI systems have to support, not replace, the clinicians, guaranteeing that they continue to play a crucial role in decision-making. The development and deployment of AI-based medica image analysis systems have to be guided by ethical oversight committees to address any emerging issues.

This book, "AI for Medical Image Analysis: reconciling Innovation and ethical considerations," delves into the use of AI in medical image analysis while adhering to ethical considerations. It will cover the technological advancements, applications, strategies and ethical considerations around the use of AI in healthcare imaging. It presents a holistic perspective on how AI-driven computer vision and image processing are reshaping the healthcare landscape and expanding the realm of what is conceivable for medical diagnostics and treatment.

This book will:

•       Highlight the efficiency of AI for the medical image analysis and tumor segmentation, including machine learning and deep learning models.
•           Include case studies across many areas of AI in medical imaging data.
•          Investigate the ethical, regulatory and social considerations of AI in medical image analysis

Present the current challenges and futures research directions.

Najib BEN AOUN received the Master and the Ph.D degrees in Computer Systems engineering from the National School of Engineers Sfax (ENIS), Tunisia. He has around 20 years of teaching as well as Bachelor, Master and Ph.D students mentoring and co-mentoring experience. He is currently associate professor at the Faculty of Computing and Information (FCI) at Al-Baha University, Saudi Arabia. He is also a senior researcher within the REsearch Groups in Intelligent Machines (REGIM-Lab) in Tunisia. His main research interests include applications of machine learning (including deep learning) in computer vision, medical image analysis, agriculture, security/biometry and data science. He has published 30+ research papers in reputed international conferences and high-level journals. He serves in various editorial roles across SCI/SCIE/Scopus-indexed journals, and has edited books with Springer and Frontiers. In addition, he has participated in the organization of several international conferences and workshops and served as a reviewer for numerous international conferences and journals. Dr. Najib is currently a senior IEEE member and a member of ACM, IAPR, MIRLabs, IAES, IEEE and IEEE SPS as well as IEEE SPS IVMSP, MMSP and MLSP technical committee affiliate member.

Sadique Ahmad is the CEO of KnowledgeShare IU Private Limited. Also, he is a Senior Assistant Professor at the Department of Computer Sciences at Bahria University Karachi Campus Pakistan (on leave). Currently, he is working as a Postdoc Fellow at Prince Sultan University, Riyadh KSA. Dr Sadique Ahmad achieved his PhD degree (2019) from the Department of Computer Sciences and Technology, Beijing Institute of Technology China, while a Master’s degree (2015) from the Department of Computer Sciences from IMSciences University Peshawar Pakistan. He has achieved above 50 research articles in peer-reviewed journals and conferences, including top journals such as Information Sciences, Science China Information Sciences, Computational Intelligence and Neuroscience, Physica-A, and IEEE ACCESS. His interests include Cognitive Computing, Deep Cognitive Modeling for Students' Performance Prediction, and Cognitive Modeling in Object Detection using remote sensing images. Also, in parallel, we are focusing on Cognitive Model-based big data processing.

Mohamed Hammad received the Ph.D. degree from the School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China, in 2019. He is currently an Assistant Professor with the Faculty of Computers and Information, Menoufia University, Egypt. He is also a Researcher with the EIAS Data Science Laboratory, College of Computer and Information Sciences, Prince Sultan University. He has published more than 50 articles in international SCI-IF journals. His research interests include biomedical imaging, bioinformatics, cyber security, the IoT, computer vision, machine learning, deep learning, pattern recognition, and biometrics. Furthermore, he served as an Editor Board Member for PLOS One and BMC Bioinformatics, an Associate Editor for International Journal of Information Security and Privacy, a Topics Board Editor for Forensic Sciences (MPDI), and the Guest Editor for many international journals, such as International Journal of Digital Crime and Forensics, Sensors (MDPI), and Information (MDPI). He is a reviewer of more than 500 articles for many prestigious journals and listed in the top 2% of scientists worldwide (according to the recently released list by Stanford University, USA, in 2022 and 2023).