Using AI–ML to Augment the Capabilities of Social Media for Telehealth and Remote Patient Monitoring
Artificial intelligence (AI) and machine learning (ML) have revolutionized the way health organizations approach social media. The sheer volume of data generated through social media can be overwhelming, but AI and ML can help organizations effectively manage this information to improve telehealth,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Healthcare |
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| Online Access: | https://www.mdpi.com/2227-9032/11/12/1704 |
| _version_ | 1797594622842634240 |
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| author | Ricky Leung |
| author_facet | Ricky Leung |
| author_sort | Ricky Leung |
| collection | DOAJ |
| description | Artificial intelligence (AI) and machine learning (ML) have revolutionized the way health organizations approach social media. The sheer volume of data generated through social media can be overwhelming, but AI and ML can help organizations effectively manage this information to improve telehealth, remote patient monitoring, and the well-being of individuals and communities. Previous research has revealed several trends in AI–ML adoption: First, AI can be used to enhance social media marketing. Drawing on sentiment analysis and related tools, social media is an effective way to increase brand awareness and customer engagement. Second, social media can become a very useful data collection tool when integrated with new AI–ML technologies. Using this function well requires researchers and practitioners to protect users’ privacy carefully, such as through the deployment of privacy-enhancing technologies (PETs). Third, AI–ML enables organizations to maintain a long-term relationship with stakeholders. Chatbots and related tools can increase users’ ability to receive personalized content. The review in this paper identifies research gaps in the literature. In view of these gaps, the paper proposes a conceptual framework that highlights essential components for better utilizing AI and ML. Additionally, it enables researchers and practitioners to better design social media platforms that minimize the spread of misinformation and address ethical concerns more readily. It also provides insights into the adoption of AI and ML in the context of remote patient monitoring and telehealth within social media platforms. |
| first_indexed | 2024-03-11T02:24:56Z |
| format | Article |
| id | doaj.art-eec1af265d004aff8742f9de54cb4176 |
| institution | Directory Open Access Journal |
| issn | 2227-9032 |
| language | English |
| last_indexed | 2024-03-11T02:24:56Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Healthcare |
| spelling | doaj.art-eec1af265d004aff8742f9de54cb41762023-11-18T10:37:58ZengMDPI AGHealthcare2227-90322023-06-011112170410.3390/healthcare11121704Using AI–ML to Augment the Capabilities of Social Media for Telehealth and Remote Patient MonitoringRicky Leung0School of Public Health, University at Albany, Albany, NY 12222, USAArtificial intelligence (AI) and machine learning (ML) have revolutionized the way health organizations approach social media. The sheer volume of data generated through social media can be overwhelming, but AI and ML can help organizations effectively manage this information to improve telehealth, remote patient monitoring, and the well-being of individuals and communities. Previous research has revealed several trends in AI–ML adoption: First, AI can be used to enhance social media marketing. Drawing on sentiment analysis and related tools, social media is an effective way to increase brand awareness and customer engagement. Second, social media can become a very useful data collection tool when integrated with new AI–ML technologies. Using this function well requires researchers and practitioners to protect users’ privacy carefully, such as through the deployment of privacy-enhancing technologies (PETs). Third, AI–ML enables organizations to maintain a long-term relationship with stakeholders. Chatbots and related tools can increase users’ ability to receive personalized content. The review in this paper identifies research gaps in the literature. In view of these gaps, the paper proposes a conceptual framework that highlights essential components for better utilizing AI and ML. Additionally, it enables researchers and practitioners to better design social media platforms that minimize the spread of misinformation and address ethical concerns more readily. It also provides insights into the adoption of AI and ML in the context of remote patient monitoring and telehealth within social media platforms.https://www.mdpi.com/2227-9032/11/12/1704AIMLhealth organizationssocial media |
| spellingShingle | Ricky Leung Using AI–ML to Augment the Capabilities of Social Media for Telehealth and Remote Patient Monitoring Healthcare AI ML health organizations social media |
| title | Using AI–ML to Augment the Capabilities of Social Media for Telehealth and Remote Patient Monitoring |
| title_full | Using AI–ML to Augment the Capabilities of Social Media for Telehealth and Remote Patient Monitoring |
| title_fullStr | Using AI–ML to Augment the Capabilities of Social Media for Telehealth and Remote Patient Monitoring |
| title_full_unstemmed | Using AI–ML to Augment the Capabilities of Social Media for Telehealth and Remote Patient Monitoring |
| title_short | Using AI–ML to Augment the Capabilities of Social Media for Telehealth and Remote Patient Monitoring |
| title_sort | using ai ml to augment the capabilities of social media for telehealth and remote patient monitoring |
| topic | AI ML health organizations social media |
| url | https://www.mdpi.com/2227-9032/11/12/1704 |
| work_keys_str_mv | AT rickyleung usingaimltoaugmentthecapabilitiesofsocialmediafortelehealthandremotepatientmonitoring |