The Promise of AI in Detection, Diagnosis, and Epidemiology for Combating COVID-19: Beyond the Hype.

Steffen E. Petersen,Musa Abdulkareem

doi:10.3389/frai.2021.652669

Abstract

COVID-19 has created enormous suffering, affecting lives, and causing deaths. The ease with which this type of coronavirus can spread has exposed weaknesses of many healthcare systems around the world. Since its emergence, many governments, research communities, commercial enterprises, and other institutions and stakeholders around the world have been fighting in various ways to curb the spread of the disease. Science and technology have helped in the implementation of policies of many governments that are directed toward mitigating the impacts of the pandemic and in diagnosing and providing care for the disease. Recent technological tools, artificial intelligence (AI) tools in particular, have also been explored to track the spread of the coronavirus, identify patients with high mortality risk and diagnose patients for the disease. In this paper, areas where AI techniques are being used in the detection, diagnosis and epidemiological predictions, forecasting and social control for combating COVID-19 are discussed, highlighting areas of successful applications and underscoring issues that need to be addressed to achieve significant progress in battling COVID-19 and future pandemics. Several AI systems have been developed for diagnosing COVID-19 using medical imaging modalities such as chest CT and X-ray images. These AI systems mainly differ in their choices of the algorithms for image segmentation, classification and disease diagnosis. Other AI-based systems have focused on predicting mortality rate, long-term patient hospitalization and patient outcomes for COVID-19. AI has huge potential in the battle against the COVID-19 pandemic but successful practical deployments of these AI-based tools have so far been limited due to challenges such as limited data accessibility, the need for external evaluation of AI models, the lack of awareness of AI experts of the regulatory landscape governing the deployment of AI tools in healthcare, the need for clinicians and other experts to work with AI experts in a multidisciplinary context and the need to address public concerns over data collection, privacy, and protection. Having a dedicated team with expertise in medical data collection, privacy, access and sharing, using federated learning whereby AI scientists hand over training algorithms to the healthcare institutions to train models locally, and taking full advantage of biomedical data stored in biobanks can alleviate some of problems posed by these challenges. Addressing these challenges will ultimately accelerate the translation of AI research into practical and useful solutions for combating pandemics.

Highlights

COVID-19, a type of coronavirus disease caused by Severe Acute Respiratory Syndrome Corona-Virus 2 (SARS-CoV-2), has created enormous suffering, affecting lives and causing deaths
Areas where artificial intelligence (AI) techniques are being used in the detection, diagnosis and epidemiological predictions, forecasting and social control for combating COVID-19 are discussed, highlighting areas of successful applications and underscoring issues that need to be addressed to achieve significant progress in battling COVID-19 and future pandemics
Misuse of AI models can result when the datasets used for model training do not take into account future use-case conditions; for example, radiologists can adapt to change in magnetic resonance imaging (MRI) field strength and breathing motion artifacts but these changes will affect the performance of AI models unless they have been allowed for during the training of the models (Brady and Neri, 2020)

Summary

INTRODUCTION

COVID-19, a type of coronavirus disease caused by Severe Acute Respiratory Syndrome Corona-Virus 2 (SARS-CoV-2), has created enormous suffering, affecting lives and causing deaths. In order to address the problem of lack of large datasets of COVID-19 patients for developing AI-based models, researchers, such as in Jin et al (2020b) and Zhao et al (2020a), have used different techniques such as data augmentation and transfer learning, to solve the CT image classification problems for COVID-19 diagnosis. AI techniques have been used to develop applications for managing and control the spread of the COVID19 pandemic Technologies, such as drones and surveillance cameras equipped with AI-based models for enforcing social isolation (Ahmed et al, 2021), have been reported. As for the impacts of the various social control strategies, the reader is invited to consult (Chang et al, 2020; Hellewell et al, 2020; Kissler et al, 2020; Koo et al, 2020) for further information

DISCUSSION

Findings

Data Accessibility

CONCLUSION