AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data

By KC Santosh. Journal of medical systems, 2020

315 citations recorded by the source corpus.

The novel coronavirus (COVID-19) outbreak, which was identified in late 2019, requires special attention because of its future epidemics and possible global threats. Beside clinical procedures and treatments, since Artificial Intelligence (AI) promises a new paradigm for healthcare, several different AI tools that are built upon Machine Learning (ML) algorithms are employed for analyzing data and decision-making processes. This means that AI-driven tools help identify COVID-19 outbreaks as well as forecast their nature of spread across the globe. However, unlike other healthcare issues, for COVID-19, to detect COVID-19, AI-driven tools are expected to have active learning-based cross-population train/test models that employs multitudinal and multimodal data, which is the primary purpose of the paper.

Subfields: Infectious Diseases, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/e0ce6638f4a1936a64c6435aa197f112484fd052

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Citations in the corpus

Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices (2020, background): “The state-of-the-art put forth in Table 1 claims that AI along-with radiology imaging of COVID-19 positive patients can be helpful for timely and accurate diagnosis of disease [12].”
Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model (2021, unknown): “The results showed that the prediction errors of the proposed model are signiﬁcantly lower than using traditional monolithic neural networks.”