Karol Przystalski, Rohit M. Thanki

Explainable Machine Learning in Medicine

• Broschiertes Buch

Produktbeschreibung

This book covers a variety of advanced communications technologies that can be used to analyze medical data and can be used to diagnose diseases in clinic centers. The book is a primer of methods for medicine, providing an overview of explainable artificial intelligence (AI) techniques that can be applied in different medical challenges. The authors discuss how to select and apply the proper technology depending on the provided data and the analysis desired. Because a variety of data can be used in the medical field, the book explains how to deal with challenges connected with each type. A number of scenarios are introduced that can happen in real-time environments, with each pared with a type of machine learning that can be used to solve it.

Produktdetails

• Synthesis Lectures on Engineering, Science, and Technology
• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-031-44879-9
• Seitenzahl: 100
• Erscheinungstermin: 15. Dezember 2024
• Englisch
• Abmessung: 240mm x 168mm x 6mm
• Gewicht: 187g
• ISBN-13: 9783031448799
• ISBN-10: 3031448790
• Artikelnr.: 72595735

Autorenporträt

Dr. Karol Przystalski obtained a PhD degree in Computer Science in 2017 at the Jagiellonian University in Cracow, Poland. He used to be the CTO and founder of Codete, an Exadel company. He is working with Fortune 500 companies on data science projects. He has been a lecturer at the Jagiellonian University in Cracow since 2010. His areas of research interest are medical imaging analysis, artificial intelligence, large language models, machine learning security, pattern recognition, and image processing. Dr. Jan K. Argasi¿ski got his PhD in Studies on Art (New Media/Software Studies) in 2015. Also holds master's degrees in New Media Studies, Philosophy, and completed his studies in Neurobiology at the Jagiellonian University in Krakow. He is an Assistant Professor at the JU's Institute of Applied Computer Science and Senior Researcher at the Sano - Centre for Personalized Computational Medicine. Authored over 30 various scientific publications (incl. co-authoring of 3 books) and gave over 100 scientific and popular science presentations. His areas of expertise are artificial intelligence, affective computing, computational neuroscience and VR/AR. Member of Polish Artificial Intelligence Society (PSSI) since 2019. Dr. Natalia Lipp is a psychologist with a Ph.D. degree in Social Sciences obtained in 2022 at Jagiellonian University. She is an assistant fellow at the Institute of Applied Psychology at JU and a postdoctoral researcher at Sano - Centre for Personalized Computational Medicine. Her research concerns the psychological factors of human-computer interaction, particularly the role of immersion and imagination in task performance in virtual reality. She is devoted to enhancing user's well-being and increasing trust in AI. Dawid Pacholczyk, currently advancing in a PhD program, concentrates on implementing simulations and Large Language Models for grooming emerging managers. He holds a master's degree in Software Architecture and has asserted his expertise as an Assistant Professor at the Polish-Japanese Academy of IT. Also is the esteemed author of a pivotal book on product management and has disseminated multiple industry articles, elucidating the applications of Augmented Reality in e-commerce. He had manifested his scholarly insights in various publications within the augmented reality domain. His research primarily navigates through Generative AI, management simulation, future managers training techniques, usage of AI at schools, practical aspects of AR.

Inhaltsangabe

1 Introduction.- 2 Medical Tabular Data.- 3 Natural Language Processing for Medical Data Analysis.- 4 Computer Vision for Medical Data Analysis.- 5 Time Series Data Used for Diseases Recognition and Anomaly Detection.- 6 Summary.