Artificial intelligence and machine learning

A growing body of research shows that improving diagnostic and treatment efficiency can save lives. Artificial intelligence (AI) in healthcare is a new research topic. Human engineering and domain expertise were initially necessary to transform raw data into algorithms. One type of machine learning called deep learning creates representations from raw data, with an algorithm determining how much change should be done. That deep learning can learn from huge amounts of data is its utility. It can categorize, analyze, and forecast data to identify patterns. Weak clinical integration makes measuring current effect difficult, but simulation data reveals AI's potential to enhance screening accuracy and efficiency, minimize effort, and potentially diagnose sickness better than experts. In medical imaging, deep learning algorithms categorize, segment, and identify objects in pictures and movies. Studies on AI-based breast cancer, cardiac imaging, and melanoma screening showed promising results. Evolved deep learning algorithms such as convolutional neural networks (CNNs) effectively assess spatially invariant input. In trials assessing their diagnostic utility in object classification, CNNs were close to or at the physician level in identifying skin cancer, cardiovascular risk, and breast cancer. During the COVID-19 pandemic, AI was used for everything from vaccine/drug discovery to diagnosis, according to Abd-Alrazaq. Now, most AI systems actively combine physicians and algorithms, enhancing accuracy and efficiency. © 2023 Elsevier Inc. All rights reserved.

The Covid-19 crisis and manufacturing: How should national and local industrial strategies respond?

The ongoing Covid-19 crisis and recession represent one of the biggest shocks to the UK manufacturing ecosystem yet, and comes at a time when the ecosystem was already in a worrying situation after decades of deindustrialisation, a decade of austerity and an impending ?Brexit?. The effects of this shock will also be unevenly felt due to the geography of the UK manufacturing ecosystem, amplifying the need for a successful response to ensure that places are not left (further) behind. This paper assesses the pre-Covid-19 ecosystem to ascertain the areas and industries likely to be particularly impacted by the crisis, and to understand existing issues. These issues are important to consider due to the implications for choosing strategies moving forward, for which two are appraised here. First, the reshoring of supply chains is considered in light of recent government comments, but difficulties in implementation may arise due to the highly fragmented nature of UK policy frameworks. Second, an acceleration of the ?grand challenges? approach is likely but limited by issues of connectivity in the ecosystem and small and medium-sized firm disengagement. We suggest that any strategy moving forward must strike a balance between such strategies