Summary of Generative Ai in Health Economics and Outcomes Research: a Taxonomy Of Key Definitions and Emerging Applications, An Ispor Working Group Report, by Rachael Fleurence et al.
Generative AI in Health Economics and Outcomes Research: A Taxonomy of Key Definitions and Emerging Applications, an ISPOR Working Group Report
by Rachael Fleurence, Xiaoyan Wang, Jiang Bian, Mitchell K. Higashi, Turgay Ayer, Hua Xu, Dalia Dawoud, Jagpreet Chhatwal
First submitted to arxiv on: 26 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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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 proposes a taxonomy for generative artificial intelligence (AI) in health economics and outcomes research (HEOR), explores emerging applications, and outlines methods to enhance accuracy and reliability of AI-generated outputs. The review defines foundational generative AI concepts, highlights current HEOR applications such as systematic literature reviews, health economic modeling, real-world evidence generation, and dossier development. Approaches like prompt engineering, retrieval-augmented generation, model fine-tuning, and domain-specific models are introduced to improve AI accuracy and reliability. Generative AI shows significant potential in HEOR, enhancing efficiency, productivity, and offering novel solutions to complex challenges. Foundation models automate complex tasks, but challenges remain in scientific reliability, bias, interpretability, and workflow integration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Generative AI can help with health economics and outcomes research (HEOR) by making it more efficient and accurate. This paper shows how generative AI can be used for things like looking at lots of studies, doing economic models, generating real-world evidence, and making reports. It also talks about ways to make sure the AI is good and reliable. The paper says that AI has a lot of potential in HEOR and could help with many problems. |
Keywords
» Artificial intelligence » Fine tuning » Prompt » Retrieval augmented generation
