Artificial intelligence enabled COVID-19 detection: techniques, challenges and use cases

Deep Learning and Machine Learning are becoming more and more popular as their algorithms get progressively better, and their use is expected to have the large effect on improving the health care system. Also, the pandemic was a chance to show how adding AI to healthcare infrastructure could help, since infrastructures around the world are overworked and falling apart. These new technologies can be used to fight COVID-19 because they are flexible and can be changed. Based on these facts, we looked at how the ML and DL-based models can be used to deal with the COVID-19 pandemic problem and what the pros and cons of each are. This paper gives a full look at the different ways to find COVID-19. We looked at the COVID-19 issues in a systematic way and then rated the methods and techniques for finding it based on their availability, ease of use, accuracy, and cost. We have also shown in pictures how well each of the detection techniques works. We did a comparison of different detection models based on the above factors. This helps researchers understand the different methods and the pros and cons of using them as the basis for their research. In the last part, we talk about the open challenges and research questions that come with putting these techniques together with other detection methods.

Automated summary

Deep Learning and Machine Learning are gaining popularity due to their improving algorithms, and their application in healthcare is expected to have a significant impact. The addition of AI to healthcare infrastructure has shown potential in improving overburdened and crumbling infrastructures worldwide, especially during the pandemic. These flexible and adaptable technologies can be used to combat COVID-19. This paper extensively examines various ML and DL-based models for detecting COVID-19, analyzing their pros and cons. It provides a systematic study of COVID-19 issues and rates detection methods based on availability, ease of use, accuracy, and cost. The comparison of different detection models helps researchers understand their advantages and disadvantages as a foundation for further research. The paper concludes by discussing the challenges and research questions associated with integrating these techniques with other detection methods.