Digital Healthcare and Artificial Intelligence (eBook, PDF)

A Primer for Clinicians and Students
Redaktion: Rainey, Clare; Mcconnell, Jonathan; McFadden, Sonyia

• Format: PDF

Produktbeschreibung

Digital technologies are essential to future-proof our health service. With an ageing population and the landscape of the post-pandemic world making increasing demands on an already-stretched human workforce, maximising the potential of a digital future is necessary. This book introduces the concepts and underpinning technology behind recent developments to give a snapshot of the state of the art in the field of digital health and provides information for clinicians at all levels of experience. The reader will gain insight into digital health in general, with clinical, technical and industrial insights providing context to the theory.

Key Features

• Defines the value that Artificial Intelligence (AI) brings to healthcare, resulting in improved clinical diagnosis and decision-making
• Enables not just imaging staff but other healthcare practitioners to extract the best ways of working with AI using the underpinning knowledge from this text

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 186
• Erscheinungstermin: 2. Februar 2026
• Englisch
• ISBN-13: 9781040506196
• Artikelnr.: 76093663

Autorenporträt

CLARE RAINEY Dr Clare Rainey is a lecturer in the Discipline of Radiography within the School of Medicine in University College Cork, in Ireland. Clare previously held positions in Ulster University including Course Director of the undergraduate Diagnostic Radiography programme and prior to this, in the NHS, where she worked for 11 years as a clinical radiographer. Clare obtained her PhD from Ulster University in Artificial Intelligence for radiographic image interpretation, with focus on the impact of AI on human cognitive processes, including decision making and the impact of user interfaces on trust and automation bias. She has published extensively in well-known, international journals in the fields of AI, human-computer interaction, technology-enabled education and leadership and has contributed to a number of textbooks in the field of AI. Clare supervises master's and PhD students across a range of topics and disciplines including research into technology use in diverse clinical settings and user interface design. She works with colleagues in medicine, computer science/data analytics and, of course, radiography. Through this she supports and promotes open research principles by mentoring students to publish research and sharing of open access data sets. Clare is Chair of the Society of Radiographers AI Advisory Board and is an Associate Editor of the Journal of Medical Imaging and Radiation Sciences. Clare is driven by a passion for exploring how technology can be used responsibly to reduce health disparities both nationally and globally to secure parity of care for all and is committed to the future visibility of radiographer research in AI and other emerging technologies. SONYIA McFADDEN Dr Sonyia Mc Fadden is a registered Diagnostic Radiographer and currently a Senior Lecturer in Diagnostic Radiography and Imaging at Ulster University in Northern Ireland. She completed her MSc in 2000 investigating radiation exposure in interventional cardiology and her PhD in 2010 investigating radiation dose optimisation in paediatric interventional cardiology. Sonyia was awarded the International Society of Radiographers and Radiological Technologists Dosewise International Radiographer of the Year (2014), a Fellowship of the College of Radiographers by Portfolio (2016) and a Gold Medal Award from the Society and College of Radiographers (2021). Sonyia previously held the position of Research Director of the Institute of Nursing and Health Research in the Faculty of Life and Health Sciences at Ulster University and is the past Chair of the Society of Radiographers AI Advisory Board in the UK. Currently Sonyia is on the Editorial board of the international journal Cardiovascular Imaging Asia, she has over 20 years clinical experience in general radiography/interventional cardiology and 24 years teaching and research experience in an academic setting. Sonyia has several areas of ongoing research, publishes/presents widely, supervises and assesses PhD students locally, nationally and internationally. She is passionate about education, training, and research in all aspects of medical imaging and strives to advance the field of research in medical imaging through shared knowledge. JONATHAN McCONNELL Dr Jonathan McConnell is a registered Diagnostic Radiographer. He is currently a Senior Lecturer in Diagnostic Radiography and Imaging at the University of Salford Manchester and Visiting Professor at Ulster University in Northern Ireland. He completed his MSc in 1999 investigating radiographer reporting consensus of role extension between the reporters and their managers. Jonathan completed his PhD at Monash University, Australia in 2015 asking whether role development by radiographer interpretation could reposition the profession in Australia. Having a mixed clinical and academic career, he led the radiographer reporting team at NHS Greater Glasgow and Clyde as a consultant grade radiographer between 2013 and 2022, receiving the national team award for Scotland in 2016 and was awarded Fellowship by Portfolio of the College of Radiographers in 2019. Jonathan has served multiple roles in the UK and internationally including advisory positions to the New Zealand Registration Board, the education committee for the State of Victoria Registration Board, as radiographer (role development and advanced practice) to NHS Education for Scotland, the Scottish Radiology Transformation Programme, the College of Radiographers Consultant Radiographers Advisory Group, Diagnostic Imaging Advisory Group and Assessment and Accreditation Board. He contributed to the CoR Education and Career Framework development between 2020 and 2022 as a consultant radiographer and frequently reviews articles from a range of peer reviewed journals and has been an editorial board member and now International Advisory Group member for the 'Radiography' journal since 2005. Currently he acts as a Fitness to Practice Registrant Panel member for the UK Health and Care Professions Council and is a member of the National Institute for Health and Care Research, Radiographer Research Incubator steering group. Jonathan has several areas of ongoing research, publishes/presents widely, supervises and assesses PhD students. He is passionate about education, training, and research in all aspects of medical imaging and enthusiastically advances the field of research in medical imaging.

Inhaltsangabe

Foreword by Susan C. Shelmerdine. Foreword by Raymond Bond. Preface.
Editors' Biographies. List of Contributors. 1. Introduction and Learning
Objectives. 2. Person/Patient-Centred Practice with AI: The Patient, the
Practitioner and the Use of Technology to Enable Better Patient Care. 3.
Background to Technology Used in Healthcare. 4. Statistical Analyses and
Reporting of Performance of AI Models: An Introduction to AI Statistics. 5.
Human-Computer Interaction and Biases Associated with Technology Use in the
Context of Healthcare. 6. Educating Clinicians of the Future in AI. 7.
Opportunities and Equity in Imaging AI: Implications for Indigenous and
Underrepresented Populations. 8. AI in Cardiology. 9. AI in Radiology. 10.
Artificial Intelligence in Onco-Radiology: From Promise to Practice,
Industry to Implementation. 11. The Application of Artificial Intelligence
to Histopathology. 12. AI in Ophthalmology. 13. AI in Dermatology:
AI-Enabled Diagnostics in Dermatology - A Case Study in Skin Cancer
Devices. 14. Sustainability in Clinical AI. Case Study A: Practical
Implication - How to Integrate AI Responsibly into Life and Personal Health
- The Hear Glue Ear Example. Case Study B: AI for Adaptive Learning - A
Case Study in Personalising Mammography Education. Case Study C:
Integrating Artificial Intelligence into Healthcare - The Patient's
Perspective. Case Study D: Transforming Radiology Workflows at Leiden
University Medical Centre (LUMC) - A Case Study in the Clinical Integration
of AI for Chest X-Ray Interpretation. Index.