Summary of Generative Ai For Health Technology Assessment: Opportunities, Challenges, and Policy Considerations, by Rachael Fleurence et al.
Generative AI for Health Technology Assessment: Opportunities, Challenges, and Policy Considerations
by Rachael Fleurence, Jiang Bian, Xiaoyan Wang, Hua Xu, Dalia Dawoud, Mitch Higashi, Jagpreet Chhatwal
First submitted to arxiv on: 9 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper highlights the transformative potential of generative Artificial Intelligence (AI) and foundation models, particularly large language models (LLMs), in health technology assessment (HTA). The authors explore their applications in four critical areas: evidence synthesis, evidence generation, clinical trials, and economic modeling. Generative AI can assist in automating literature reviews, proposing search terms, screening abstracts, and extracting data with notable accuracy. It can also facilitate the analysis of large collections of real-world data, enhancing the speed and quality of real-world evidence (RWE) generation. Additionally, generative AI can optimize trial design, improve patient matching, and manage trial data more efficiently. Furthermore, it can aid in the development of health economic models from conceptualization to validation, streamlining the overall HTA process. The authors emphasize the need for careful evaluation and responsible use of these technologies to ensure their scientific validity, minimize risk of bias, and consider equity and ethical implications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how Artificial Intelligence (AI) can help make health technology assessment (HTA) better. HTA is like a big report that helps us understand what works best in healthcare. The authors show how AI can be used to speed up the process of reviewing research papers, analyzing data, and designing clinical trials. They also explain how AI can help with making economic models, which are important for understanding the costs and benefits of different treatments. Overall, the paper highlights the potential of AI to make HTA more efficient and effective. However, it also emphasizes the need for careful evaluation and responsible use of these technologies to ensure they are used correctly. |
