Abstract
The global COVID-19 pandemic has resulted in huge pressures on healthcare systems, with lung imaging, from chest radiographs (CXR) to computed tomography (CT) and ultrasound (US) of the thorax, playing an important role in the diagnosis and management of patients with coronavirus infection. The AI community reacted rapidly to the threat of the coronavirus pandemic by contributing numerous initiatives of developing AI technologies for interpreting lung images across the different modalities. We performed a thorough review of all relevant publications in 2020 [1] and identified numerous trends and insights that may help in accelerating the translation of AI technology in clinical practice in pandemic times. This workshop is devoted to the lessons learned from this accelerated process and in paving the way for further AI adoption.
In particular, the objective is to bring together radiologists and AI experts to review the scientific progress in the development of AI technologies for medical imaging to address the COVID-19 pandemic and share observations regarding the data relevance, the data availability and the translational aspects of AI research and development. We aim at understanding if and what needs to be done differently in developing technologies of AI for lung images of COVID-19 patients, given the pressure of an unprecedented pandemic - which processes are working, which should be further adapted, and which approaches should be abandoned.
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Acknowledgments
MRZ thanks the 8400 The Health Network and the Israeli Society for HealthTech for facilitating discussions with key opinion leaders on lessons learned.
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Gabrani, M. et al. (2021). Lessons Learned from the Development and Application of Medical Imaging-Based AI Technologies for Combating COVID-19: Why Discuss, What Next. In: Oyarzun Laura, C., et al. Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning. DCL PPML LL-COVID19 CLIP 2021 2021 2021 2021. Lecture Notes in Computer Science(), vol 12969. Springer, Cham. https://doi.org/10.1007/978-3-030-90874-4_13
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DOI: https://doi.org/10.1007/978-3-030-90874-4_13
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