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AI: Opportunities, Capabilities and Limits
There is no doubt that Artificial Intelligence (AI) can transform how we deliver care. It can increase clinician productivity and improve efficiency and enable existing healthcare systems to provide care to more people than before. AI can also improve data analysis and utilisation, facilitate better decision-making and promote early diagnosis and...
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Artificial Intelligence and Echocardiography: Are We Ready for Automation?
Artificial intelligence has many potential applications in the field of cardiac imaging and echocardiography is not an exception. There are clear examples in different aspects like cardiac chamber quantification, assistance on the interpretation of stress echocardiography or the evaluation of valvular heart disease. We need to be prepared for automation...
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Learning From Each Other: An Artificial Intelligence Perspective in Healthcare
An overview of how exchange of knowledge can advance Artificial Intelligence (AI) in healthcare with total data privacy. Key Points Artificial Intelligence is playing an important role in taking better decisions across multiple sectors and the healthcare sector is not an exception. Rule of thumb: The larger the datasets the better...
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What Can the NHS Learn from Public Sector Supply Chain Attacks?
As organisations become increasingly connected through technology, there is a rising threat of becoming collateral in cyberattacks. It is imperative to evaluate the security practices and stances taken within any third-party organisation involved. Key Points The NHS plans to launch 42 Integrated Care Systems (ICS) across England this year....
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The Current State of AI in Diagnostic Imaging and How to Improve its Clinical Value
More well-curated data, external validation of algorithms, and user-friendly workflow integration will help translate the potential of AI into clinical routine. Key Points Representative data sets and external validation are key issues for AI algorithms to become more accurate and generalisable. Machine learning is not only...
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Artificial Intelligence and Radiomics at the University of Florence
This paper provides an overview of current research at the Department of Radiology of the University of Florence. It concerns radiomics along with Artificial Intelligence (AI) applied to various medical fields. The need for personalised medicine and the transition from qualitative to quantitative imaging are at the heart of the projects...
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The Knowledge Model and Enabling Artificial Intelligence
The purpose of this paper is to provide a growth path to AI development. It is based on principles learned from launching CT, MRI, Neuro Vascular, and PACS. Key Points Create a model (The Knowledge Model) that will build with additional concepts as they develop, resulting in synergistic thinking (Causal Knowledge...
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Digital Twin Technologies - Shortening Waiting Lists and Reducing Inefficiencies
Digital twin technologies and the use of artificial intelligence and data science to revolutionise the provision, delivery and sustainability of healthcare. Key Points The NHS needs to look to different approaches to achieve sustainable, high-quality healthcare that meets both patient need and expectations. Using a combination of retrospective,...
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Pain Assessment in Critical Illness
This narrative paper reports the practical assessment of pain in critically ill (ICU) patients, based on current evidence and guidelines. Introduction Pain is one of the top stressful symptoms experienced by critically ill patients hospitalised in intensive care units (ICU) (Chanques et al. 2015). This is because critical pathologies...
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Nutrition Monitoring and Patient Data Management Systems
An overview of nutritional targets and their impact on critically ill patients and the need to use a systematic approach to nutritional support for optimal patient outcomes. Critically ill patients are often hypermetabolic and catabolic and are at a higher risk of underfeeding. Nutritional support for these patients can prevent energy...
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