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Generative AI has immensely influenced various fields, such as education, marketing, art and music, and especially healthcare. Generative AI can benefit the patient through various approaches. For instance, it can enhance the image qualities negatively affected by radiation reduction, preventing patients from needing to repeat the image-taking process. Also, the generation of one type of image from another more expensive one can help patients save funds. Generative AI facilitates the administrative process, letting the doctor focus more on the treatment process. It even goes further by helping medical professionals with diagnosis and decision- making, suggesting possible treatment plans according to the patient symptoms.
This book introduces several practical GenAI healthcare applications, especially in medical imaging, pandemic prediction, synthetic data generation, clinical administration support, professional education, patient engagement, and clinical decision support, providing a review of efficient GenAI tools and frameworks in this area. GenAI empowers the treatment process through several methods; however, some ethical, privacy, and security challenges require attention. Despite the challenges presented, GenAI technological and inherited characteristics smooth the path of improvement for it in the future.
List of contents
Generative AI in Medical Imaging.- Generative AI in Pandemic Prediction.- Application of GenAI in Synthetic Data Generation in the healthcare system.- Application of GenAI in Clinical Administration Support.- Application of Generative AI in Patient Engagement.- Application of Generative AI in Drug discovery.- Application of Generative AI in Resource Optimization in Healthcare Systems.- Ethics and Regulations in Generative AI.
About the author
Dr. Azadeh Zamanifar is currently head of AI/ software department and an assistant professor of Islamic Azad university, science and research branch. Her research interests include IoT based health care systems, machine learning, deep learning, and distributed systems. She received B.SC in Tehran university in 2002. She received her M.SC from Iran university of science and technology in 2008. She received her Ph.D. from Shahid Beheshti university in December 2016.
Dr. Miad Faezipour is currently an Associate Professor of Electrical and Computer Engineering Technology with the School of Engineering Technology, Purdue University, West Lafayette, IN, USA. She is the Founding Director of the Digital/Biomedical Embedded Systems and Technology (D-BEST) Research Laboratory. She is also jointly appointed with the Regenstrief Center for Healthcare Engineering (RCHE), and a core faculty member of the Applied AI Research Center (AARC) at Purdue University. Her research interests include healthcare technology with embedded intelligence, digital/biomedical embedded hardware/software co-designs, biomedical signal/image processing, computer vision, healthcare/biomedical informatics, artificial intelligence, and AI-based bio-data augmentation. She is a Senior Member of IEEE, Engineering in Medicine and Biology Society (EMBS), and the IEEE Women in Engineering.
Summary
Generative AI has immensely influenced various fields, such as education, marketing, art and music, and especially healthcare. Generative AI can benefit the patient through various approaches. For instance, it can enhance the image qualities negatively affected by radiation reduction, preventing patients from needing to repeat the image-taking process. Also, the generation of one type of image from another more expensive one can help patients save funds. Generative AI facilitates the administrative process, letting the doctor focus more on the treatment process. It even goes further by helping medical professionals with diagnosis and decision- making, suggesting possible treatment plans according to the patient symptoms.
This book introduces several practical GenAI healthcare applications, especially in medical imaging, pandemic prediction, synthetic data generation, clinical administration support, professional education, patient engagement, and clinical decision support, providing a review of efficient GenAI tools and frameworks in this area. GenAI empowers the treatment process through several methods; however, some ethical, privacy, and security challenges require attention. Despite the challenges presented, GenAI technological and inherited characteristics smooth the path of improvement for it in the future.
