Trustworthy Ai in Cancer Imaging Research. Este artículo no está disponible.Trustworthy Ai in Cancer Imaging Research

The book covers multiple aspects and challenges, from legal to technical and validation, in the emerging topic of AI in cancer imaging, bringing together the experience of top researchers and flagship projects.

The aim of this book is to address the important questions: “How to design AI that is trustworthy”, and “How to validate AI trustworthiness” in the scope of AI for cancer imaging research. The book discusses overall considerations and the generation of a framework; preparing for trustworthy AI, including the data and metadata for quality, transparency and traceability; implementing trustworthy AI with algorithms and Decision Support Systems; and validating trustworthy AI.

Chapters 2 and 3 are available open access under a Creative Commons Attribution 4.0 International License via Springerlink.

This is an ideal resource for researchers from technical and clinical research sites, postgraduate students, and healthcare professionals in cancer imaging and beyond.

Ioanna Chouvarda (F), Electrical Engineer, PhD, is an Associate Professor of Medical Informatics and Biomedical Data Analysis, at the School of Medicine, Aristotle University of Thessaloniki. She has been involved in medical informatics research for over 25 years. She is particularly interested in a) biomedical data analysis and development of machine learning methods with biomedical images, biosignals and biological data, and b) digital health applications and services incorporating such models. She has worked in numerous national/European research projects in the field of digital health and biomedical informatics, has coordinated AUTH’s work in seven projects and has been an evaluator of numerous proposals and projects. She has developed a number of undergraduate and graduate courses in biomedical informatics/engineering, some with an interdisciplinary scope, and has supervised many graduate students. 

Sara Colantonio is a senior researcher at the Institute of Information Science and Technologies "Alessandro Faedo" of the National Research Council of Italy, based in Pisa and a member of the "Signals and Images" Laboratory, where she is coordinating the activities of a multidisciplinary team of researchers on the topics of artificial intelligence for health and wellbeing. Her research interests include artificial intelligence, machine learning, assistive technologies, personal informatics, quantified self, and trustworthy AI. She coordinated an FP7 project focused on the first sensorised smart mirror for cardiometabolic disease prevention, which granted her an award as one of the 40 Top Transformers in health in 2016. She is a member of the international multi-stakeholder group of experts "Artificial Intelligence for Health Imaging" (AI4HI), contributing to the definition of the FUTURE-AI guidelines on trustworthy AI in medical applications.

Gianna Tsakou is a senior project manager, analyst and researcher of EC and nationally funded IT research projects with over 25 years of experience in both the public and private sector. She is currently working as a Senior Project Manager / Senior Analyst in the Research and Development Lab of Maggioli S.P.A., where she has the overview of the eHealth EC funded projects, notably those related to prevention, diagnosis and treatment of cancer using big data and AI. She has coordinated and/or participated in more than 30 R&D projects through which she has acquired expertise and know-how in a wide range of IT research areas among which: Artificial Intelligence (AI) in cancer imaging, trustworthy AI, health-related big data repositories, healthy ageing and independent living, eHealth services, accessible services for people with disabilities / eInclusion, multi-modal interfaces. She is a co-author in more than 30 scientific publications and one of the 4 co-initiators and coordinators of the "Artificial Intelligence for Health Imaging" (AI4HI) network.

Dr. Guang Yang is an Associate Professor (Senior Lecturer) in the Bioengineering Department and Imperial-X at Imperial College London. He holds a UKRI Future Leaders Fellowship and serves as an Honorary Senior Lecturer in the School of Biomedical Engineering & Imaging Sciences at King's College London. He is an Associate Editor of IEEE Transactions and npj Digital Medicine. His research group is dedicated to developing novel and translational techniques for imaging and biomedical data analysis. The group’s focus encompasses research and development in data-driven fast imaging, data harmonization, data synthesis, federated learning, explainable AI, and AI in drug discovery. Currently, his work spans a wide range of clinical applications in ageing, cardiovascular disease, lung disease, and oncology.