COVID-19 Impact on Global Artificial Intelligence in Medical Imagin... in the Healthcare Industry
INTRODUCTION
The current outbreak of coronavirus disease 2019 (COVID-19) caused by Severe Acute Respiratory Syndrome coronavirus-2 (SARS-CoV-2) has posed a severe danger to the community and has a major impact on the economy, (GDP) Gross Domestic Product, and business worldwide. The World Health Organization (WHO) has announced COVID-19, a pandemic having a high transmission rate that affects many lives. Spike-S protein interaction in SARS-CoV-2 with Ace-2 receptor present in the Human lungs is the primary reason for this high transmissibility. This is the main reason or greatest challenge in the Healthcare market and the biggest challenge for healthcare providers in the year 2020. In December 2019, Chinese health officials informed World Health Organization (WHO) about amid serious conditions when a cluster of 40 patients with pneumonia like symptoms, most of them are connected to Huanan Sea-food market. Looking towards the current condition it is not China who is suffering from global loss of any market since the number of cases graph in China is stabilized. Countries such as the U.S., India, Brazil, Russia, and many more countries across the globe face the loss in market and a decline in (GDP) Gross Domestic Product. After the declaration of pandemic many countries goes into a complete lockdown state, including China, India, the US, Russia and Germany among others in order to decrease the spread of COVID-19, some countries like China, New Zealand and South Korea succeed because of their strict laws and increased number of testing, Chinese company Baidus have initiated Heat mapping from which testing became easier and helped in the stabilization of graph. Still, the majority of the other countries including India & US has been drastically failed and hence are these were having much higher number of cases. Because of the contagion nature of COVID-19 the demand for Artificial Intelligence in Medical Imaging Market has increased. However, the growth is anticipated to be increased at a higher pace then it is now. Still, due to pandemic and lockdown globally the growth is subdued, it has also been forecasted by research analyst’s that after pandemic this growth will further increases. The reason for this increase in demand is due to covid-19, people are now more conscious about their health, further, more Artificial Intelligence reports generated are much more reliable than physical examination’s A.I. can detect even the finest crack in bone fracture X-rays, which is generally not possible for human eyes or physicians, this is the reason why Radiologist depend on A.I. more now. Due to COVID-19 medical imaging or diagnosis in the clinical specialties like Pulmonology, Cardiology, Breast, and Neurology markets will get huge revenue in the future. Apart from that, Liver, Prostate, and muscular imaging are some other clinical applications. Artificial Intelligence or machine learning can be implemented in X-rays, Computed Tomography, Magnetic Resonance Imaging, Ultrasound and Molecular Imaging among others. According to the weekly epidemiology report of 25 October 2020 by World Health Organization (WHO), 42 million cases of corona have been reported globally and 1157509 patients are dead due to the coronavirus. The outbreak of COVID-19 is having a huge impact on the healthcare industry, which also means the need for A.I. and machine learning to find some suitable compounds that can bind with Spike protein of SARS-CoV-2 and disrupt the binding of Ace-2 and Spike protein of SARS-CoV-2. Diagnosis point of view have discussed Artificial intelligence (AI) generated reports are more reliable if proper integration and practical applications is performed.

IMPACT ON DEMAND
Artificial intelligence (A.I.) and machine learning have already demonstrated there efficacy, accuracy, and refinement of results that’s why radiologists and pathologists know the value of Artificial intelligence (A.I.). Hence, they are looking to hasten their productivity and potentially improve their accuracy in Healthcare imaging and diagnosis of clinical abnormalities and problems related to patients.