An Introduction to Artificial Intelligence for Clinicians
Academic Press Inc (Verlag)
978-0-443-19411-5 (ISBN)
By equipping clinicians with education and skills they can use in the present world of clinical AI, they are better positioned for the future arrival of artificial intelligence into day-to-day patient care. To ensure the broad relevance of this book, the editors also draw on a yet unpublished qualitative evidence synthesis of stakeholder experiences of AI implementation across all healthcare contexts worldwide, which empowers readers to see how clinical AI could benefit patients in their own context and to inspire others to affect change.
It is a valuable resource for clinicians, students, researchers, healthcare professionals and members of medical and biomedical fields who are interested to learn more about artificial intelligence applied to clinical setting and healthcare.
Pearse A Keane received his medical degree from University College Dublin (UCD), graduating in 2002. In 2016, he initiated a formal collaboration between Moorfields Eye Hospital and Google DeepMind, with the aim of developing artificial intelligence (AI) algorithms for the earlier detection and treatment of retinal disease. In August 2018, the first results of this collaboration were published in a journal (Nature Medicine). In May 2020, he jointly led work, again published in Nature Medicine, to develop an early warning system for age-related macular degeneration (AMD), by far the commonest cause of blindness in many countries. In October 2019, he was included on the Evening Standard Progress1000 list of most influential Londoners and in June 2020, he was profiled in The Economist. In 2020, he was listed on the “The Power List by The Ophthalmologist magazine, a ranking of the Top 100 most influential people in the world of ophthalmology. Ciara O’Byrne received her medical degree from Trinity College Dublin in 2018. Her main area of interest is in the development and application of code free deep learning models in ophthalmology with the goal of improved patient care and outcomes. She also has a specific interest in the development of educational resources to provide guidance to clinicians in the field of artificial intelligence. In February 2021, she was heavily involved in the successful development and deliverance of a short course with Moorfields Education “An Introduction to AI for Clinicians. This received excellent feedback and an updated course is currently being planned for 2022. The focus of Jeffry Hogg’s current research is on the implementation of clinical artificial intelligence. Better understanding the influences on the implementation, sustainability and spread of clinical AI tools has emphasized the need for a foundational understanding of clinical AI and the surrounding issues among a broad clinical audience. His current teaching activity involves the design of the Newcastle University digital health curriculum on its medical programme and the use of AI enabled image analysis to develop ophthalmologists’ clinical knowledge and skills. This has allowed him a practical sense of how best to select and communicate aspects of this broad and complex topic to audiences with little or no prior experience. Xiaoxuan Liu is interested in clinical evaluation and implementation of digital health technologies (particularly machine learning algorithms as diagnostic tests) to improve patient care. Her previous work has been around identifying gaps in reporting and methodology in early clinical AI evaluations, and providing guidance for clinicians to critically appraise the evidence-base supporting AI health systems. Alastair Denniston leads a programme of work in health data research and the application of digital healthcare (including artificial intelligence) to improve patient care in the ‘real world’. He is also the Director of INSIGHT, the UK’s Health Data Research Hub for Eye Health, AI Lead for the Centre for Regulatory Science and Innovation (Birmingham, UK), and a Member of the UK Government’s Regulatory Horizons Council.
1. An Introduction to Artificial Intelligence
2. The History of Artificial Intelligence
3. Deep Learning – A Conceptual Framework
4. A Primer on Medical Statistics
5. How to Critically Evaluate the Clinical AI Literature
6. How to Start Your Own Clinical AI Research Project
7. Training a Deep Learning Model
8. Validation of an AI System
9. Implementation Science and AI
10. Challenges and Limitations of Artificial Intelligence
11. The Future Directions of Artificial Intelligence
| Erscheinungsdatum | 05.03.2024 |
|---|---|
| Verlagsort | San Diego |
| Sprache | englisch |
| Maße | 191 x 235 mm |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Informatik ► Weitere Themen ► Bioinformatik | |
| ISBN-10 | 0-443-19411-4 / 0443194114 |
| ISBN-13 | 978-0-443-19411-5 / 9780443194115 |
| Zustand | Neuware |
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