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AI Hype and Radiology: A Plea for Realism and Accuracy

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This opinion piece is inspired by the old Danish proverb: "Making predictions is hard, especially about the future" (1). As every reader knows, the momentum of artificial intelligence (AI) and the eventual implementation of deep learning models seem assured. Some pundits have gone considerably further, however, and predicted a sweeping AI takeover of radiology. Although many radiologists support AI and believe it will enable greater efficiency, a recent study of medical students found very different reactions (2). While such doomsday predictions are understandably attention-grabbing, they are highly unlikely, at least in the short term.


UK rolls out AI-based cancer detection for NHS patients

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Leader in AI-powered cancer diagnostics, Ibex Medical Analytics and provider of digital pathology services in the NHS, LDPath, have announced the UK's first rollout of clinical grade AI application for cancer detection in pathology. This platform will support pathologists in enhancing diagnostic accuracy and efficiency. Over the years, a global increase in cancer cases has coincided with a decline in the number of pathologists around the world. Traditional pathology involves manual processes that have remained the same for years. These processes involve slides to be analysed by pathologists using microscopes, and reporting is often carried out on pieces of paper.


AI Conversations: Reinventing the Retail Industry with an 'Edge'

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The sense of discovery and surprise at something that matches an unrecognized yearning can be exhilarating. But the sheer size of many stores is exhausting -- with some, I need a site map, flashlight, and overnight bag to take it all in. In others, the inventory is specialized and packed in so tightly that online seems a better way to find what I want. If you're primarily an online shopper, you may be asking how relevant brick-and-mortar stores are. The answer is: quite relevant.


Benefits of Artificial Intelligence in Inventory Management

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The use of artificial intelligence (AI) in inventory management can help remove the inefficiencies in the current processes by enabling a predictive approach and reducing errors by automating operations. The current inventory management practices are laced with challenges and inefficiencies. Inventory management is mostly done manually, and thus takes a lot of time. Similarly, there is a high chance of error that can impact business operations. This can lead to customer complaints due to the gap between demand and supply which is usually caused by incorrect information input. As a result, deliveries to customers are delayed, which can lead to businesses losing out on customers and facing reputational losses.


How the Evolution of NLP Supports Personalized Digital Experiences

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The personalized digital experience is all about communication The back and forth between companies and consumers is changing from the traditional outbound push strategy, to the more empowering experience that pulls users into the inbound interaction when they want to engage. Consumers are becoming more savvy but ultimately want to be understood, educated and not sold to. In order to do that, the digital experience needs to evolve towards “human”-like intelligence. Here’s what a personalized digital experience should communicate to a user: “Talk to me! I am listening!” “You don’t need special skills to talk to me. I am designed


How to Improve a Machine Learning Algorithm: Regularization

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This article explains the reason for the poor performance of a machine learning algorithm and how to improve it. If the machine learning algorithm does not work as well as you expected, almost all the time it happens because of bias or variance. The algorithm may be suffering from either underfitting or overfitting or a bit of both. If the machine learning algorithm does not work as well as you expected, almost all the time it happens because of bias or variance. The algorithm may be suffering from either underfitting or overfitting or a bit of both.


How AI is helping WWF battle illegal deforestation

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Digitalisation has already had a major impact on the world of business, but now a project helmed by Deloitte has helped the World Wildlife Fund wield new technology in the fight to protect the world's most fragile ecosystems. Supported by the Deloitte Impact Foundation, the Cognitive Deforestation Prevention programme uses artificial intelligence to prevent illegal deforestation. According to Mark Boersma, the Senior Manager within Consulting who leads the Deloitte Impact Foundation initiative, the ideas behind the Cognitive Deforestation Prevention began to form five years ago, during a trip to the Indonesian island of Sumatra. During the expedition with his wife, they encountered rare species such as orang-utans and a group of black gibbons, but they were also struck with the serious toll human activity was taking on the shrinking natural world. Recounting the experience on Deloitte's website, Boersma said, "I remember that around the rainforest, we were struck with acres and acres of palm oil trees which had often come in place for the less intrusive rubber plantations. This is when I started to think about the effect that our palm oil consumption is having on our world… [When] WWF sent out an open request for proposals for their Early Warning System and I instantly knew that I wanted to be a part of it."


A machine learning approach to automatic detection of irregularity in skin lesion border using dermoscopic images

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Skin lesion border irregularity is considered an important clinical feature for the early diagnosis of melanoma, representing the B feature in the ABCD rule. In this article we propose an automated approach for skin lesion border irregularity detection. The approach involves extracting the skin lesion from the image, detecting the skin lesion border, measuring the border irregularity, training a Convolutional Neural Network and Gaussian naive Bayes ensemble, to the automatic detection of border irregularity, which results in an objective decision on whether the skin lesion border is considered regular or irregular. The approach achieves outstanding results, obtaining an accuracy, sensitivity, specificity, and F-score of 93.6%, 100%, 92.5% and 96.1%, respectively.


Top 10 Advantages & Disadvantages of Artificial Intelligence?

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Artificial Intelligence (AI) does not belong to the future – it is happening now. With the global AI software market surging by 154 percent year-on-year, this industry is predicted to be valued at 22.6 billion US dollars by 2025. Invented by John McCarthy in 1950, Artificial Intelligence is the ability of machines or computer programs to learn, think, and reason, much like a human brain. An AI system is fed in data and instructions, based on which the system draws conclusions and performs functions. It keeps learning human reasoning and logic with time, getting efficient on-the-go.


Human-Artificial intelligence collaborations best for skin cancer diagnosis

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Artificial intelligence (AI) improved skin cancer diagnostic accuracy when used in collaboration with human clinical checks, an international study including University of Queensland researchers has found. The global team tested for the first time whether a'real world', collaborative approach involving clinicians assisted by AI improved the accuracy of skin cancer clinical decision making. UQ's Professor Monika Janda said the highest diagnostic accuracy was achieved when crowd wisdom and AI predictions were combined, suggesting human-AI and crowd-AI collaborations were preferable to individual experts or AI alone "This is important because AI decision support has slowly started to infiltrate healthcare settings, and yet few studies have tested its performance in real world settings or how clinicians interact with it," Professor Janda said. "Inexperienced evaluators gained the highest benefit from AI decision support and expert evaluators confident in skin cancer diagnosis achieved modest or no benefit. "These findings indicated a combined AI-human approach to skin cancer diagnosis may be the most relevant for clinicians in the future." Although AI diagnostic software has demonstrated expert level accuracy in several image-based medical studies, researchers have remained unclear on whether its use improved clinical practice. "Our study found that good quality AI support was useful to clinicians but needed to be simple, concrete, and in accordance with a given task," Professor Janda said. "For clinicians of the future this means that AI-based screening and diagnosis might soon be available to support them on a daily basis.