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Volume 2 Issue 6, June 2020

Volume 2 Issue 6

A path for AI in the pandemic

In three Comments this issue several groups of experts discuss what role AI can play in the fight against the COVID-19 pandemic. Though AI and machine learning researchers are ready and willing to play their part, it is not an easy task to identify where developments can be most useful. A close collaboration with health workers is required, as well as consideration of how new tools can make a global impact, with adaptability to local situations. One fast-emerging application of machine learning is in data-driven, digital solutions for tracing and tracking COVID-19 infections, but there are alarm bells ringing over the dangers of surveillance creep. In a series of short interviews we delve into the debate about contact track-and-trace apps and the whether it is possible to get the balance right between protecting public health and safeguarding civil rights with digital surveillance tools.

See Luengo-Oroz et al., Peiffer-Smadja et al., Hu et al. and Q&A

Image: sleepyfellow / Alamy Stock Photo. Cover Design: Karen Moore.

Editorial

  • Editorial |

    Expectations are high for AI to help fight COVID-19. But before AI tools can make an impact, global collaboration and high-quality data and model sharing are needed.

Correspondence

Comment & Opinion

  • Comment |

    The COVID-19 pandemic poses a historical challenge to society. The profusion of data requires machine learning to improve and accelerate COVID-19 diagnosis, prognosis and treatment. However, a global and open approach is necessary to avoid pitfalls in these applications.

    • Nathan Peiffer-Smadja
    • , Redwan Maatoug
    •  & Jean-Rémi King
  • Comment |

    In an unprecedented effort of scientific collaboration, researchers across fields are racing to support the response to COVID-19. Making a global impact with AI tools will require scalable approaches for data, model and code sharing; adapting applications to local contexts; and cooperation across borders.

    • Miguel Luengo-Oroz
    • , Katherine Hoffmann Pham
    •  & Bernardo Mariano
  • Comment |

    The attention and resources of AI researchers have been captured by COVID-19. However, successful adoption of AI models in the fight against the pandemic is facing various challenges, including moving clinical needs as the epidemic progresses and the necessity to translate models to local healthcare situations.

    • Yipeng Hu
    • , Joseph Jacob
    •  & Danail Stoyanov
  • Q&A |

    Contact-tracing apps could help keep countries open before a vaccine is available. But do we have a sufficient understanding of their efficacy, and can we balance protecting public health with safeguarding civil rights? We interviewed five experts, with backgrounds in digital health ethics, internet law and social sciences.

    • Yann Sweeney

Reviews

  • Perspective |

    Medical imaging data is often subject to privacy and intellectual property restrictions. AI techniques can help out by offering tools like federated learning to bridge the gap between personal data protection and data utilisation for research and clinical routine, but these tools need to be secure.

    • Georgios A. Kaissis
    • , Marcus R. Makowski
    •  & Rickmer F. Braren
  • Perspective |

    China’s New Generation Artificial Intelligence Development Plan was launched in 2017 and lays out an ambitious strategy, which intends to make China one of the world’s premier AI innovation centre by 2030. This Perspective presents the view from a group of Chinese AI experts from academia and industry about the origins of the plan, the motivations and main focus for attention from research and industry.

    • Fei Wu
    • , Cewu Lu
    •  & Yunhe Pan

Research

  • Article |

    A fundamental problem in network science is how to find an optimal set of key players whose activation or removal significantly impacts network functionality. The authors propose a deep reinforcement learning framework that can be trained on small networks to understand the organizing principles of complex networked systems.

    • Changjun Fan
    • , Li Zeng
    •  & Yang-Yu Liu
  • Article |

    Spiking neural networks and in-memory computing are both promising routes towards energy-efficient hardware for deep learning. Woźniak et al. incorporate the biologically inspired dynamics of spiking neurons into conventional recurrent neural network units and in-memory computing, and show how this allows for accurate and energy-efficient deep learning.

    • Stanisław Woźniak
    • , Angeliki Pantazi
    •  & Evangelos Eleftheriou
  • Article |

    Vascular abnormalities are challenging for diagnostic imaging due to the complexity of vasculature and the non-uniform scattering from biological tissues. The authors present an unsupervised learning algorithm for vascular feature recognition from small sets of biomedical images acquired from different modalities. They demonstrate the utility of their diagnostic approach on vascular images of thrombosis, internal bleeding and colitis.

    • Yong Wang
    • , Mengqi Ji
    •  & Qionghai Dai
  • Article |

    A lot of scientific literature is unstructured, which makes extracting information for biomedical databases difficult. Hong and colleagues show that a distant supervision approach, using latent tree learning and recurrent units, can extract drug–target interactions from literature that were previously unknown.

    • Lixiang Hong
    • , Jinjian Lin
    •  & Jianyang Zeng
  • Article |

    Tumour mutational burden (TMB) shows promise as a biomarker in cancer immunotherapy, but it usually requires whole-exome sequencing, which is costly, time-consuming and unavailable at most hospitals. The authors develop a machine learning algorithm that uses standard H&E histopathological images to quickly, inexpensively and accurately predict TMB. The approach may have applications as a tool to screen and prioritize patient samples and subsequent treatments.

    • Mika S. Jain
    •  & Tarik F. Massoud

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