Leveraging Generative AI for Clinical Evidence Summarization Needs to Ensure Trustworthiness
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929297172725760 |
|---|---|
| author | Zhang, Gongbo Jin, Qiao McInerney, Denis Jered Chen, Yong Wang, Fei Cole, Curtis L. Yang, Qian Wang, Yanshan Malin, Bradley A. Peleg, Mor Wallace, Byron C. Lu, Zhiyong Weng, Chunhua Peng, Yifan |
| author_facet | Zhang, Gongbo Jin, Qiao McInerney, Denis Jered Chen, Yong Wang, Fei Cole, Curtis L. Yang, Qian Wang, Yanshan Malin, Bradley A. Peleg, Mor Wallace, Byron C. Lu, Zhiyong Weng, Chunhua Peng, Yifan |
| contents | Evidence-based medicine promises to improve the quality of healthcare by empowering medical decisions and practices with the best available evidence. The rapid growth of medical evidence, which can be obtained from various sources, poses a challenge in collecting, appraising, and synthesizing the evidential information. Recent advancements in generative AI, exemplified by large language models, hold promise in facilitating the arduous task. However, developing accountable, fair, and inclusive models remains a complicated undertaking. In this perspective, we discuss the trustworthiness of generative AI in the context of automated summarization of medical evidence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_11211 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Leveraging Generative AI for Clinical Evidence Summarization Needs to Ensure Trustworthiness Zhang, Gongbo Jin, Qiao McInerney, Denis Jered Chen, Yong Wang, Fei Cole, Curtis L. Yang, Qian Wang, Yanshan Malin, Bradley A. Peleg, Mor Wallace, Byron C. Lu, Zhiyong Weng, Chunhua Peng, Yifan Artificial Intelligence Evidence-based medicine promises to improve the quality of healthcare by empowering medical decisions and practices with the best available evidence. The rapid growth of medical evidence, which can be obtained from various sources, poses a challenge in collecting, appraising, and synthesizing the evidential information. Recent advancements in generative AI, exemplified by large language models, hold promise in facilitating the arduous task. However, developing accountable, fair, and inclusive models remains a complicated undertaking. In this perspective, we discuss the trustworthiness of generative AI in the context of automated summarization of medical evidence. |
| title | Leveraging Generative AI for Clinical Evidence Summarization Needs to Ensure Trustworthiness |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2311.11211 |