On the role of artificial intelligence in medical imaging of COVID-19
Jannis Born,
David Beymer,
Deepta Rajan,
Adam Coy,
Vandana V. Mukherjee,
Matteo Manica,
Prasanth Prasanna,
Deddeh Ballah,
Michal Guindy,
Dorith Shaham,
Pallav L. Shah,
Emmanouil Karteris,
Jan L. Robertus,
Maria Gabrani,
Michal Rosen-Zvi
Affiliations
Jannis Born
IBM Research Europe, Zurich, Switzerland; Department for Biosystems Science & Engineering, ETH Zurich, Zurich, Switzerland; Corresponding author
David Beymer
IBM Almaden Research Center, San Jose, CA, USA; Corresponding author
Deepta Rajan
IBM Almaden Research Center, San Jose, CA, USA
Adam Coy
IBM Almaden Research Center, San Jose, CA, USA; Vision Radiology, Dallas, TX, USA
Vandana V. Mukherjee
IBM Almaden Research Center, San Jose, CA, USA
Matteo Manica
IBM Research Europe, Zurich, Switzerland
Prasanth Prasanna
Department of Radiology and Imaging Sciences, University of Utah Health Sciences Center, Salt Lake City, UT, USA; IBM Almaden Research Center, San Jose, CA, USA
Deddeh Ballah
Department of Radiology, Seton Medical Center, Daly City, CA, USA; IBM Almaden Research Center, San Jose, CA, USA
Michal Guindy
Assuta Medical Centres Radiology, Tel-Aviv, Israel; Ben-Gurion University Medical School, Be'er Sheva, Israel
Dorith Shaham
Department of Radiology, Hadassah-Hebrew University Medical Center, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
Pallav L. Shah
Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK; Chelsea & Westminster Hospital, London, UK; National Heart & Lung Institute, Imperial College London, London, UK
Emmanouil Karteris
College of Health, Medicine and Life Sciences, Brunel University London, London, UK
Jan L. Robertus
Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK; National Heart & Lung Institute, Imperial College London, London, UK
Maria Gabrani
IBM Research Europe, Zurich, Switzerland
Michal Rosen-Zvi
IBM Research Haifa, Haifa, Israel; Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
Summary: Although a plethora of research articles on AI methods on COVID-19 medical imaging are published, their clinical value remains unclear. We conducted the largest systematic review of the literature addressing the utility of AI in imaging for COVID-19 patient care. By keyword searches on PubMed and preprint servers throughout 2020, we identified 463 manuscripts and performed a systematic meta-analysis to assess their technical merit and clinical relevance. Our analysis evidences a significant disparity between clinical and AI communities, in the focus on both imaging modalities (AI experts neglected CT and ultrasound, favoring X-ray) and performed tasks (71.9% of AI papers centered on diagnosis). The vast majority of manuscripts were found to be deficient regarding potential use in clinical practice, but 2.7% (n = 12) publications were assigned a high maturity level and are summarized in greater detail. We provide an itemized discussion of the challenges in developing clinically relevant AI solutions with recommendations and remedies. The bigger picture: During the COVID-19 pandemic, medical imaging (CT, X-ray, ultrasound) has played a key role in addressing the magnified need for speed, low cost, ubiquity, and precision in patient care. The contemporary digitization of medicine and rise of artificial intelligence (AI) induce a quantum leap in medical imaging: AI has proven equipollent to healthcare professionals across a diverse range of tasks, and hopes are high that AI can save time and cost and increase coverage by advancing rapid patient stratification and empowering clinicians.This review bridges medical imaging and AI in the context of COVID-19 and conducts the largest systematic review of the literature in the field. We identify several gaps and evidence significant disparities between clinicians and AI experts and foresee a need for improved, interdisciplinary collaboration to develop robust AI solutions that can be deployed in clinical practice.The key challenges on that roadmap are discussed alongside recommended solutions.
