Toward global citizenship? People (de)bordering their lives during COVID-19 in Latin America and Europe.

The COVID-19 pandemic highlighted global interdependencies, accompanied by widespread calls for worldwide cooperation against a virus that knows no borders, but responses were led largely separately by national governments. In this tension between aspiration and reality, people began to grapple with how their own lives were affected by the global nature of the pandemic. In this article, based on 493 qualitative interviews conducted between 2020 and 2021, we explore how people in Argentina, Austria, Bolivia, Ecuador, Ireland, Italy and Mexico experienced, coped with and navigated the global nature of the pandemic. In dialogue with debates about the parameters of the 'global' in global health, we focus on what we call people's everyday (de)bordering practices to examine how they negotiated (dis)connections between 'us' and 'them' during the pandemic. Our interviewees' reactions moved from national containment to an increasing focus on people's unequal socio-spatial situatedness. Eventually, they began to (de)border their lives beyond national lines of division and to describe a new normal: a growing awareness of global connectedness and a desire for global citizenship. This newfound sense of global interrelatedness could signal support for and encourage transnational political action in times of crises.

Artificial intelligence for early detection of renal cancer in computed tomography: A review.

Renal cancer is responsible for over 100,000 yearly deaths and is principally discovered in computed tomography (CT) scans of the abdomen. CT screening would likely increase the rate of early renal cancer detection, and improve general survival rates, but it is expected to have a prohibitively high financial cost. Given recent advances in artificial intelligence (AI), it may be possible to reduce the cost of CT analysis and enable CT screening by automating the radiological tasks that constitute the early renal cancer detection pipeline. This review seeks to facilitate further interdisciplinary research in early renal cancer detection by summarising our current knowledge across AI, radiology, and oncology and suggesting useful directions for future novel work. Initially, this review discusses existing approaches in automated renal cancer diagnosis, and methods across broader AI research, to summarise the existing state of AI cancer analysis. Then, this review matches these methods to the unique constraints of early renal cancer detection and proposes promising directions for future research that may enable AI-based early renal cancer detection via CT screening. The primary targets of this review are clinicians with an interest in AI and data scientists with an interest in the early detection of cancer.

Erratum: Author Correction: Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence.

[This corrects the article DOI: 10.1038/s42256-021-00421-z.].

Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence.

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.