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Addressing the challenges of AI-based telemedicine: Best practices and lessons learned

Telemedicine is the use of technology to provide healthcare services and information remotely, without requiring physical proximity between patients and healthcare providers. The coronavirus disease 2019 (COVID-19) pandemic has accelerated the rapid growth of telemedicine worldwide. Integrating arti...

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Autores principales: Sharma, Sachin, Rawal, Raj, Shah, Dharmesh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wolters Kluwer - Medknow 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10671014/
https://www.ncbi.nlm.nih.gov/pubmed/38023098
http://dx.doi.org/10.4103/jehp.jehp_402_23
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author Sharma, Sachin
Rawal, Raj
Shah, Dharmesh
author_facet Sharma, Sachin
Rawal, Raj
Shah, Dharmesh
author_sort Sharma, Sachin
collection PubMed
description Telemedicine is the use of technology to provide healthcare services and information remotely, without requiring physical proximity between patients and healthcare providers. The coronavirus disease 2019 (COVID-19) pandemic has accelerated the rapid growth of telemedicine worldwide. Integrating artificial intelligence (AI) into telemedicine has the potential to enhance and expand its capabilities in addressing various healthcare needs, such as patient monitoring, healthcare information technology (IT), intelligent diagnosis, and assistance. Despite the potential benefits, implementing AI in telemedicine presents challenges that can be overcome with physician-guided implementation. AI can assist physicians in decision-making, improve healthcare delivery, and automate administrative tasks. To ensure optimal effectiveness, AI-powered telemedicine should comply with existing clinical practices and adhere to a framework adaptable to various technologies. It should also consider technical and scientific factors, including trustworthiness, reproducibility, usability, availability, and cost. Education and training are crucial for the appropriate use of new healthcare technologies such as AI-enabled telemedicine. This article examines the benefits and limitations of AI-based telemedicine in various medical domains and underscores the importance of physician-guided implementation, compliance with existing clinical practices, and appropriate education and training for healthcare providers.
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spelling pubmed-106710142023-09-29 Addressing the challenges of AI-based telemedicine: Best practices and lessons learned Sharma, Sachin Rawal, Raj Shah, Dharmesh J Educ Health Promot Review Article Telemedicine is the use of technology to provide healthcare services and information remotely, without requiring physical proximity between patients and healthcare providers. The coronavirus disease 2019 (COVID-19) pandemic has accelerated the rapid growth of telemedicine worldwide. Integrating artificial intelligence (AI) into telemedicine has the potential to enhance and expand its capabilities in addressing various healthcare needs, such as patient monitoring, healthcare information technology (IT), intelligent diagnosis, and assistance. Despite the potential benefits, implementing AI in telemedicine presents challenges that can be overcome with physician-guided implementation. AI can assist physicians in decision-making, improve healthcare delivery, and automate administrative tasks. To ensure optimal effectiveness, AI-powered telemedicine should comply with existing clinical practices and adhere to a framework adaptable to various technologies. It should also consider technical and scientific factors, including trustworthiness, reproducibility, usability, availability, and cost. Education and training are crucial for the appropriate use of new healthcare technologies such as AI-enabled telemedicine. This article examines the benefits and limitations of AI-based telemedicine in various medical domains and underscores the importance of physician-guided implementation, compliance with existing clinical practices, and appropriate education and training for healthcare providers. Wolters Kluwer - Medknow 2023-09-29 /pmc/articles/PMC10671014/ /pubmed/38023098 http://dx.doi.org/10.4103/jehp.jehp_402_23 Text en Copyright: © 2023 Journal of Education and Health Promotion https://creativecommons.org/licenses/by-nc-sa/4.0/This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.
spellingShingle Review Article
Sharma, Sachin
Rawal, Raj
Shah, Dharmesh
Addressing the challenges of AI-based telemedicine: Best practices and lessons learned
title Addressing the challenges of AI-based telemedicine: Best practices and lessons learned
title_full Addressing the challenges of AI-based telemedicine: Best practices and lessons learned
title_fullStr Addressing the challenges of AI-based telemedicine: Best practices and lessons learned
title_full_unstemmed Addressing the challenges of AI-based telemedicine: Best practices and lessons learned
title_short Addressing the challenges of AI-based telemedicine: Best practices and lessons learned
title_sort addressing the challenges of ai-based telemedicine: best practices and lessons learned
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10671014/
https://www.ncbi.nlm.nih.gov/pubmed/38023098
http://dx.doi.org/10.4103/jehp.jehp_402_23
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