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Artificial Intelligence in Health AI in the battle against COVID-19

6.4. Wearable Technologies the progression of the disease in patients, enabling timely
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Wearable technologies have been instrumental in the interventions. In addition, AI-driven algorithms have
early detection and symptom monitoring of COVID- been applied to remotely monitor patients’ vital signs,
19 patients during the pandemic. Wearable devices such thereby reducing the exposure risk for healthcare workers
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as smartwatches and biometric trackers continuously and other patients. 65
gather physiological and activity data, such as heart rate, 7.3. Telemedicine
daily steps, and sleep patterns. AI systems then analyze this
data to detect deviations that may indicate infection, even Telemedicine, a component of eHealth, involves using
before clinical symptoms manifest. 58 information and communication technology to deliver,
manage, and monitor health-care services remotely.
AI has emerged as an indispensable tool in the detection During the COVID-19 pandemic, telemedicine emerged
and diagnosis of COVID-19. Its application in imaging, as a vital tool, especially for patients in isolation. It
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symptom assessment, and wearable technology has not enabled these patients to receive medical care without
only expedited the diagnostic process but also enhanced risking exposure for themselves or health-care providers
its precision. to the virus. Furthermore, it alleviated the strain on

7. AI in COVID-19 treatment and healthcare facilities, conserved resources such as personal
management protective equipment, and played a crucial role in the
global management of the pandemic.
The role of AI in the treatment and management of COVID- The surge in demand for healthcare services during the
19, spanning from drug discovery to patient management pandemic has underscored the significance of telemedicine,
to telemedicine, has proven instrumental. By leveraging with AI playing a crucial role in its expansion. AI has
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vast datasets, machine learning algorithms, and predictive facilitated remote diagnosis and consultation services,
analytics, AI has enabled healthcare providers to identify ensuring continuity of care while minimizing the risk of
potential drugs for treatment, optimize treatment virus transmission. Moreover, AI-powered chatbots have
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protocols, and improve patient outcomes. The integration
of AI in these areas not only enhances the efficiency of been employed to provide initial medical assessments
healthcare services but also supports the ongoing efforts based on symptoms reported by patients, thus alleviating
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to control and mitigate the impact of the pandemic. In the strain on medical facilities.
exploring the various applications of AI in COVID-19 8. AI in COVID-19 prediction and analytics
treatment and management, this section highlights the
innovative strategies and tools that have been developed AI has been utilized in the domain of COVID-19
and their significant impact on public health responses. prediction and analytics as part of the global response to
the pandemic. AI models and NLP algorithms have proven
7.1. Drug discovery pivotal in epidemiological modeling, optimizing resource
AI has played an essential role in expediting the drug allocation, and analyzing social media to gauge public
discovery process for COVID-19 treatment. Machine sentiment and disseminate information.
learning algorithms have been utilized to predict the 8.1. Epidemiological modeling
structure of the SARS-CoV-2 virus, thereby identifying
potential targets for drug therapy. 60,61 Furthermore, AI AI has played a critical role in epidemiological modeling,
platforms such as DeepMind’s AlphaFold have made providing forecasts essential for planning and intervention
significant contributions to understanding the protein strategies. Sophisticated machine learning models based
folding of the virus, which is crucial for the development of on reinforcement learning have been employed to predict
antiviral drugs. The deployment of AI in virtual screening the spread of the virus, assess the impact of public health
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has also allowed researchers to rapidly assess millions of interventions, and estimate the burden on healthcare
chemical compounds, streamlining the identification of systems. 69,70 Neural network methods have been
viable drug candidates. 63,64 implemented to identify COVID-19 clusters, providing
insights into how socioeconomic factors and spatial
7.2. Patient management and monitoring distribution relate to the spread of COVID-19 cases.
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In the domain of patient management and monitoring, AI These models have been crucial in informing government
systems have been deployed to predict patient outcomes policies, such as implementing lockdowns and organizing
and optimize resource allocation. Predictive analytics have vaccination campaigns, to mitigate the spread of the
provided healthcare professionals with tools to forecast virus. 72

Volume 1 Issue 2 (2024) 7 doi: 10.36922/aih.2401