Artificial Intelligence Is Reshaping Healthcare amid COVID-19: A Review in the Context of Diagnosis & Prognosis

• Published in: Diagnostics (Basel) Vol. 11; no. 9; p. 1604
• Main Authors: Saha, Rajnandini, Aich, Satyabrata, Tripathy, Sushanta, Kim, Hee-Cheol
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 02.09.2021; MDPI
• Subjects: Artificial intelligence; Bioinformatics; Body temperature; Coronaviruses; COVID-19; diagnosis; Disease transmission; Epidemics; Fever; healthcare; Illnesses; Infections; interpretive structural modeling; Lungs; Older people; Pandemics; Pneumonia; Severe acute respiratory syndrome coronavirus 2; Statistical analysis; X-rays
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics11091604

Preventing respiratory failure is crucial in a large proportion of COVID-19 patients infected with SARS-CoV-2 virus pneumonia termed as Novel Coronavirus Pneumonia (NCP). Rapid diagnosis and detection of high-risk patients for effective interventions have been shown to be troublesome. Using a large, computed tomography (CT) database, we developed an artificial intelligence (AI) parameter to diagnose NCP and distinguish it from other kinds of pneumonia and traditional controls. The literature was studied and analyzed from diverse assets which include Scopus, Nature medicine, IEEE, Google scholar, Wiley Library, and PubMed. The search terms used were ‘COVID-19’, ‘AI’, ‘diagnosis’, and ‘prognosis’. To strengthen the overall performance of AI in COVID-19 diagnosis and prognosis, we segregated several components to perceive threats and opportunities, as well as their inter-dependencies that affect the healthcare sector. This paper seeks to pick out the crucial fulfillment of factors for AI with inside the healthcare sector in the Indian context. Using critical literature review and experts’ opinion, a total of 11 factors affecting COVID-19 diagnosis and prognosis were detected, and we eventually used an interpretive structural model (ISM) to build a framework of interrelationships among the identified factors. Finally, the matrice d’impacts croisés multiplication appliquée á un classment (MICMAC) analysis resulted the driving and dependence powers of these identified factors. Our analysis will help healthcare stakeholders to realize the requirements for successful implementation of AI.