The Covid-19 pandemic has profoundly changed the world. The remote workplace has become the norm. We have started looking at personal health differently – the way we work, live, play and do business. AI's use for drug discovery has accelerated post-Covid-19 era. Today, drug discovery is an expensive proposition, with a $2.6 billion cost over 10 years and just a 12% success rate.
Artificial intelligence provides a promising solution for streamlining COVID-19 diagnoses; however, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalized model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the artificial intelligence (AI) model can be distributedly trained and independently executed at each host institution under a federated learning framework without data sharing. Here we show that our federated learning framework model considerably outperformed all of the local models (with a test sensitivity/specificity of 0.973/0.951 in China and 0.730/0.942 in the United Kingdom), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals without the federated learning framework) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans from 3,336 patients collected from 23 hospitals located in China and the United Kingdom. Collectively, our work advanced the prospects of utilizing federated learning for privacy-preserving AI in digital health. The COVID-19 pandemic sparked the need for international collaboration in using clinical data for rapid development of diagnosis and treatment methods. But the sensitive nature of medical data requires special care and ideally potentially sensitive data would not leave the organization which collected it. Xiang Bai and colleagues present a privacy-preserving AI framework for CT-based COVID-19 diagnosis and demonstrate it on data from 23 hospitals in China and the United Kingdom.
Over the past couple of years, the Covid-19 pandemic followed by the intrusion of its variants has has a devastating impact on the global economy. Employment across many industries was under great threat. However, one thing that the catastrophe left unscathed was artificial intelligence. Artificial intelligence came into existence when humans thought of making machines that imitate human behavior. The core aspect of creating AI was to make technology recreate human insights in a better and advanced way.
Petropoulos, Fotios, Apiletti, Daniele, Assimakopoulos, Vassilios, Babai, Mohamed Zied, Barrow, Devon K., Taieb, Souhaib Ben, Bergmeir, Christoph, Bessa, Ricardo J., Bijak, Jakub, Boylan, John E., Browell, Jethro, Carnevale, Claudio, Castle, Jennifer L., Cirillo, Pasquale, Clements, Michael P., Cordeiro, Clara, Oliveira, Fernando Luiz Cyrino, De Baets, Shari, Dokumentov, Alexander, Ellison, Joanne, Fiszeder, Piotr, Franses, Philip Hans, Frazier, David T., Gilliland, Michael, Gönül, M. Sinan, Goodwin, Paul, Grossi, Luigi, Grushka-Cockayne, Yael, Guidolin, Mariangela, Guidolin, Massimo, Gunter, Ulrich, Guo, Xiaojia, Guseo, Renato, Harvey, Nigel, Hendry, David F., Hollyman, Ross, Januschowski, Tim, Jeon, Jooyoung, Jose, Victor Richmond R., Kang, Yanfei, Koehler, Anne B., Kolassa, Stephan, Kourentzes, Nikolaos, Leva, Sonia, Li, Feng, Litsiou, Konstantia, Makridakis, Spyros, Martin, Gael M., Martinez, Andrew B., Meeran, Sheik, Modis, Theodore, Nikolopoulos, Konstantinos, Önkal, Dilek, Paccagnini, Alessia, Panagiotelis, Anastasios, Panapakidis, Ioannis, Pavía, Jose M., Pedio, Manuela, Pedregal, Diego J., Pinson, Pierre, Ramos, Patrícia, Rapach, David E., Reade, J. James, Rostami-Tabar, Bahman, Rubaszek, Michał, Sermpinis, Georgios, Shang, Han Lin, Spiliotis, Evangelos, Syntetos, Aris A., Talagala, Priyanga Dilini, Talagala, Thiyanga S., Tashman, Len, Thomakos, Dimitrios, Thorarinsdottir, Thordis, Todini, Ezio, Arenas, Juan Ramón Trapero, Wang, Xiaoqian, Winkler, Robert L., Yusupova, Alisa, Ziel, Florian
Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.
The concept of artificial intelligence (AI) is so vast that there are many misconceptions. Artificial intelligence has a great future and will be one of the essential tools for organisations in the future. Millions of people have lost their jobs due to the effects of the Covid-19 pandemic and now the machines will take away even more jobs from workers, according to the World Economic Forum. Artificial intelligence will soon rule every single aspect of our life from online shopping to smart cities, quantum computing, blockchain, autonomous vehicles, and cybersecurity. Today's artificial intelligence as well as machine learning, deep learning, and neural networks are driven by statistics, big data analytics, processes, methods, techniques and algorithms.
Bai, Xiang, Wang, Hanchen, Ma, Liya, Xu, Yongchao, Gan, Jiefeng, Fan, Ziwei, Yang, Fan, Ma, Ke, Yang, Jiehua, Bai, Song, Shu, Chang, Zou, Xinyu, Huang, Renhao, Zhang, Changzheng, Liu, Xiaowu, Tu, Dandan, Xu, Chuou, Zhang, Wenqing, Wang, Xi, Chen, Anguo, Zeng, Yu, Yang, Dehua, Wang, Ming-Wei, Holalkere, Nagaraj, Halin, Neil J., Kamel, Ihab R., Wu, Jia, Peng, Xuehua, Wang, Xiang, Shao, Jianbo, Mongkolwat, Pattanasak, Zhang, Jianjun, Liu, Weiyang, Roberts, Michael, Teng, Zhongzhao, Beer, Lucian, Sanchez, Lorena Escudero, Sala, Evis, Rubin, Daniel, Weller, Adrian, Lasenby, Joan, Zheng, Chuangsheng, Wang, Jianming, Li, Zhen, Schönlieb, Carola-Bibiane, Xia, Tian
Title: Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence One sentence summary: An efficient and effective privacy-preserving AI framework is proposed for CT-based COVID-19 diagnosis, based on 9,573 CT scans of 3,336 patients, from 23 hospitals in China and the UK. Abstract Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalised model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution under a federated learning framework (FL) without data sharing. Here we show that our FL model outperformed all the local models by a large yield (test sensitivity /specificity in China: 0.973/0.951, in the UK: 0.730/0.942), We further evaluated the model on the hold-out (collected from another two hospitals leaving out the FL) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK. Collectively, our work advanced the prospects of utilising federated learning for privacy-preserving AI in digital health. MAIN TEXT Introduction As the gold standard for identifying COVID-19 carriers, reverse transcription-polymerase chain reaction (RT-PCR) is the primary diagnostic modality to detect viral nucleotide in specimens from cases with suspected infection. It has been reported that coronavirus carriers present certain radiological features in chest CTs, including ground-glass opacity, interlobular septal thickening, and consolidation, which can be exploited to identify COVID-19 cases.
Federated learning is an emerging privacy-preserving AI technique where clients (i.e., organisations or devices) train models locally and formulate a global model based on the local model updates without transferring local data externally. However, federated learning systems struggle to achieve trustworthiness and embody responsible AI principles. In particular, federated learning systems face accountability and fairness challenges due to multi-stakeholder involvement and heterogeneity in client data distribution. To enhance the accountability and fairness of federated learning systems, we present a blockchain-based trustworthy federated learning architecture. We first design a smart contract-based data-model provenance registry to enable accountability. Additionally, we propose a weighted fair data sampler algorithm to enhance fairness in training data. We evaluate the proposed approach using a COVID-19 X-ray detection use case. The evaluation results show that the approach is feasible to enable accountability and improve fairness. The proposed algorithm can achieve better performance than the default federated learning setting in terms of the model's generalisation and accuracy.
"Hundreds of AI tools have been built to catch covid. That's a bold statement by Will Douglas Heaven, senior editor for AI at MIT Technology Review, and is quite likely correct. Despite dozens upon dozens of machine learning algorithms designed to diagnose patients or predict just how sick COVID-19 might make them, two independent reviews published in the British Medical Journal and Nature came to the same conclusion: none of them worked. But let's not write off artificial intelligence's impact on COVID-19 too soon. Though most ML algorithms failed, there's one area where they succeeded and succeeded big. Data scientists at Moderna managed to pull off a modern-day miracle using cloud infrastructure and machine learning, as recounted by Moderna chief data and AI officer Dave Johnson. Why did Moderna succeed while many other efforts failed? Given how fast medical researchers hastened to respond to the COVID-19 threat, it's understandable why so many data science projects failed.
In light of the COVID-19 pandemic, it is an open challenge and critical practical problem to find a optimal way to dynamically prescribe the best policies that balance both the governmental resources and epidemic control in different countries and regions. To solve this multi-dimensional tradeoff of exploitation and exploration, we formulate this technical challenge as a contextual combinatorial bandit problem that jointly optimizes a multi-criteria reward function. Given the historical daily cases in a region and the past intervention plans in place, the agent should generate useful intervention plans that policy makers can implement in real time to minimizing both the number of daily COVID-19 cases and the stringency of the recommended interventions. We prove this concept with simulations of multiple realistic policy making scenarios.
The significant increase in the number of individuals with chronic ailments (including the elderly and disabled) has dictated an urgent need for an innovative model for healthcare systems. The evolved model will be more personalized and less reliant on traditional brick-and-mortar healthcare institutions such as hospitals, nursing homes, and long-term healthcare centers. The smart healthcare system is a topic of recently growing interest and has become increasingly required due to major developments in modern technologies, especially in artificial intelligence (AI) and machine learning (ML). This paper is aimed to discuss the current state-of-the-art smart healthcare systems highlighting major areas like wearable and smartphone devices for health monitoring, machine learning for disease diagnosis, and the assistive frameworks, including social robots developed for the ambient assisted living environment. Additionally, the paper demonstrates software integration architectures that are very significant to create smart healthcare systems, integrating seamlessly the benefit of data analytics and other tools of AI. The explained developed systems focus on several facets: the contribution of each developed framework, the detailed working procedure, the performance as outcomes, and the comparative merits and limitations. The current research challenges with potential future directions are addressed to highlight the drawbacks of existing systems and the possible methods to introduce novel frameworks, respectively. This review aims at providing comprehensive insights into the recent developments of smart healthcare systems to equip experts to contribute to the field.