In a recent study published in PLOS Digital Health, researchers reviewed the existing literature on the use of synthetic intelligence (AI) in healthcare to characterize AI programs used in clinical programs of the 2019 coronavirus disease pandemic (COVID-19), investigate the where, timing, and extent of AI use in healthcare, and the review of regulatory approval processes in the United States (US). UU. ).
Despite the large number of approvals granted through the U. S. Food and Drug Administration, the U. S. Food and Drug Administration is not allowed to do so. With the U. S. Food and Drug Administration (FDA) on AI programs in healthcare over the past six years, adoption of AI programs in other healthcare spaces has been limited. In addition, data on the progression and use of AI programs in the COVID-19 pandemic is limited, unlike the significant and immediate expansion of telehealth and vaccine technologies.
While previous reviews tested the potential uses, challenging situations, and effects of AI programs for the clinical reaction to COVID-19, many reviews found methodological flaws and potential biases in the use of AI programs in clinical practice. report on the development, testing, and programs of AI in clinical reactions to COVID-19.
In this scope review, researchers searched the educational and grey literature for studies that provided answers to the following 4 questions: 1) which AI programs are used in clinical responses to COVID-19; (2) what the locations, timelines and degree of use of these programmes are; 3) How those programs differ from the pre-pandemic generation of health care and how strict the U. S. FDA approval criteria are. U. S. for those programs; and 4) what is the public evidence recommending the use of AI programs in healthcare?
The study began through consultations with fitness stakeholders such as physicians, patient advocates, insurers and fitness formula representatives, researchers, policymakers, industry representatives, and public fitness officials to download recommendations on exam design and documents to be included in the review.
We then searched several databases for literature available after January 2020 on the use of AI in COVID-19 clinical responses, and known AI programs from the literature review were tested in detail to download more data on developer and usage.
Applications were included in the review if they met 3 criteria. First, the app had a function similar to the patient’s physical condition as a component of the patient’s evolution, diagnosis, decision-making or treatment process. no studies were included.
Second, the application of AI has been directly related to the clinical reaction to COVID-19. Apps found on government fitness websites and medical testing and symptom monitoring sites, even for limited periods of time, qualify for the exam. Finally, the app used synthetic intelligence, device learning, or deep learning algorithms.
The scope of use of the AI application was decided through the number of patients treated with the use of the AI application in the COVID-19 clinical response. The Organization for Economic Cooperation and Development’s ranking of high, medium and low-income countries was used to determine where AI programs are used.
The effects informed the use of 66 AI programs in the clinical reaction to COVID-19, which were grouped into six functional categories. of pneumonia, pneumothorax, or other lung abnormalities due to COVID-19. Symptom-checking AI apps used patient-provided demographics, threat factors, and symptoms to calculate COVID-19 threats and provide healthcare recommendations.
AI applications for patient impairment monitored the important symptoms and fitness prestige of COVID-19 patients and provided data for making healthcare decisions. These were used by doctors in hospitals, assisted living services and to monitor patients remotely at home.
Several apps have predicted the likelihood of COVID-19 infections from other sources of information, such as volatile biological compounds in breath, geographically aggregated data, patient demographics, blood results, and luminescent signals from antigen strips.
Some apps used medical and demographic knowledge to wait for the threat of severe COVID-19 outcomes and were used by doctors in hospitals, telehealth services, and outpatient clinics. immune reaction, etc.
These programs used neural networks, complex tree-based methods, and supervised and unsupervised device methods. A large number of programs were implemented between January and June 2020, in the initial stages of the pandemic, and were widely used in high-income countries such as the United States and China, and are rarely used in middle- to low-income countries.
We found no trials comparing the use of AI apps in the COVID-19 reaction, and the few publications supporting the use of some of the apps were independent assessments.
The scope review concluded with the location, function, potential benefits, scope, type, and entry knowledge of AI programs used in clinical practice during the COVID-19 pandemic. educational reports on the use of these AI models in clinical practice.
Limited evidence in the literature hinders the benefits of using AI in pandemic response efforts. More studies on real AI programs in healthcare are needed.
Written By
Chinta Sidharthan is a Bangalore-based India. Su academic background is in evolutionary biology and genetics, and has extensive experience in clinical studies, teaching, clinical writing and herpetology. Chinta holds a PhD in evolutionary biology from the Indian Institute of Science and is passionate about science education, writing, animals, wildlife and conservation. For his doctoral studies, he explored the origins and diversification of blind snakes in India, where he conducted extensive fieldwork in the jungles of southern India. He has won the Governor General Bronze Medal and Gold Medal for Academic Excellence award from the University of Bangalore and has published his studies in high-impact journals.
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