Abstract
This thesis fills a knowledge gap in implementing AI for patient care by understanding the challenges with today’s algorithms, demonstrating the value of using AI techniques like machine learning on disease states like sepsis and oncology to identify patient populations who could benefit from certain interventions, understanding the challenges in implementing AI for laboratory medicine, developing a new medical curriculum to train healthcare staff in understanding and using AI, study the regulatory and policy work behind implementing AI by understanding challenges like explainability, blackbox, bias and ethics and demonstrate how you can deploy new technologiesI in healthcare to deliver better patient care, reduce cost of care and deliver accessible care.
| Original language | English |
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| Qualification | PhD |
| Awarding Institution |
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| Supervisors/Advisors |
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| Award date | 16 Sept 2022 |
| Place of Publication | Amsterdam |
| Publisher | |
| Print ISBNs | 9789493278134 |
| Publication status | Published - 16 Sept 2022 |
Keywords
- Artificial Intelligence, Sepsis, Oncology, Personalized Medicine, Machine Learning, Clinical Decision Support, Medical Education, Policy and Regulation
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