Redefining Virtual Assistants in Health Care: The Future With Large Language Models
This editorial explores the evolving and transformative role of large language models (LLMs) in enhancing the capabilities of virtual assistants (VAs) in the health care domain, highlighting recent research on the performance of VAs and LLMs in health care information sharing. Focusing on...
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| Format: | Article |
| Language: | English |
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JMIR Publications
2024-01-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2024/1/e53225 |
| _version_ | 1797351194560036864 |
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| author | Emre Sezgin |
| author_facet | Emre Sezgin |
| author_sort | Emre Sezgin |
| collection | DOAJ |
| description |
This editorial explores the evolving and transformative role of large language models (LLMs) in enhancing the capabilities of virtual assistants (VAs) in the health care domain, highlighting recent research on the performance of VAs and LLMs in health care information sharing. Focusing on recent research, this editorial unveils the marked improvement in the accuracy and clinical relevance of responses from LLMs, such as GPT-4, compared to current VAs, especially in addressing complex health care inquiries, like those related to postpartum depression. The improved accuracy and clinical relevance with LLMs mark a paradigm shift in digital health tools and VAs. Furthermore, such LLM applications have the potential to dynamically adapt and be integrated into existing VA platforms, offering cost-effective, scalable, and inclusive solutions. These suggest a significant increase in the applicable range of VA applications, as well as the increased value, risk, and impact in health care, moving toward more personalized digital health ecosystems. However, alongside these advancements, it is necessary to develop and adhere to ethical guidelines, regulatory frameworks, governance principles, and privacy and safety measures. We need a robust interdisciplinary collaboration to navigate the complexities of safely and effectively integrating LLMs into health care applications, ensuring that these emerging technologies align with the diverse needs and ethical considerations of the health care domain. |
| first_indexed | 2024-03-08T12:56:51Z |
| format | Article |
| id | doaj.art-03dc28b10c084fad8fd2ea15cd7486ba |
| institution | Directory Open Access Journal |
| issn | 1438-8871 |
| language | English |
| last_indexed | 2024-03-08T12:56:51Z |
| publishDate | 2024-01-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Journal of Medical Internet Research |
| spelling | doaj.art-03dc28b10c084fad8fd2ea15cd7486ba2024-01-19T16:00:31ZengJMIR PublicationsJournal of Medical Internet Research1438-88712024-01-0126e5322510.2196/53225Redefining Virtual Assistants in Health Care: The Future With Large Language ModelsEmre Sezginhttps://orcid.org/0000-0001-8798-9605 This editorial explores the evolving and transformative role of large language models (LLMs) in enhancing the capabilities of virtual assistants (VAs) in the health care domain, highlighting recent research on the performance of VAs and LLMs in health care information sharing. Focusing on recent research, this editorial unveils the marked improvement in the accuracy and clinical relevance of responses from LLMs, such as GPT-4, compared to current VAs, especially in addressing complex health care inquiries, like those related to postpartum depression. The improved accuracy and clinical relevance with LLMs mark a paradigm shift in digital health tools and VAs. Furthermore, such LLM applications have the potential to dynamically adapt and be integrated into existing VA platforms, offering cost-effective, scalable, and inclusive solutions. These suggest a significant increase in the applicable range of VA applications, as well as the increased value, risk, and impact in health care, moving toward more personalized digital health ecosystems. However, alongside these advancements, it is necessary to develop and adhere to ethical guidelines, regulatory frameworks, governance principles, and privacy and safety measures. We need a robust interdisciplinary collaboration to navigate the complexities of safely and effectively integrating LLMs into health care applications, ensuring that these emerging technologies align with the diverse needs and ethical considerations of the health care domain.https://www.jmir.org/2024/1/e53225 |
| spellingShingle | Emre Sezgin Redefining Virtual Assistants in Health Care: The Future With Large Language Models Journal of Medical Internet Research |
| title | Redefining Virtual Assistants in Health Care: The Future With Large Language Models |
| title_full | Redefining Virtual Assistants in Health Care: The Future With Large Language Models |
| title_fullStr | Redefining Virtual Assistants in Health Care: The Future With Large Language Models |
| title_full_unstemmed | Redefining Virtual Assistants in Health Care: The Future With Large Language Models |
| title_short | Redefining Virtual Assistants in Health Care: The Future With Large Language Models |
| title_sort | redefining virtual assistants in health care the future with large language models |
| url | https://www.jmir.org/2024/1/e53225 |
| work_keys_str_mv | AT emresezgin redefiningvirtualassistantsinhealthcarethefuturewithlargelanguagemodels |