Summary of A Perspective For Adapting Generalist Ai to Specialized Medical Ai Applications and Their Challenges, by Zifeng Wang et al.
A Perspective for Adapting Generalist AI to Specialized Medical AI Applications and Their Challenges
by Zifeng Wang, Hanyin Wang, Benjamin Danek, Ying Li, Christina Mack, Hoifung Poon, Yajuan Wang, Pranav Rajpurkar, Jimeng Sun
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 perspective paper explores the integration of Large Language Models (LLMs) in medical applications, covering drug discovery, development, clinical decision support, telemedicine, medical devices, and healthcare insurance. The authors discuss the inner workings of building LLM-powered medical AI applications and introduce a comprehensive framework for their development. They review existing literature, outlining unique challenges of applying LLMs in specialized medical contexts. A three-step framework is proposed: Modeling (breaking down complex workflows), Optimization (crafting prompts and integrating external knowledge), and System engineering (decomposing tasks and leveraging human expertise). The authors also provide a use case playbook for various LLM-powered medical AI applications, such as optimizing clinical trial design, enhancing clinical decision support, and advancing medical imaging analysis. Additionally, they discuss challenges like handling hallucination issues, data ownership and compliance, privacy, intellectual property considerations, compute cost, sustainability issues, and responsible AI requirements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how Large Language Models can be used in medicine to help doctors, researchers, and patients. The authors explain what it takes to build these medical AI applications and provide a step-by-step guide for doing so. They also share examples of how LLMs can be used to improve things like clinical trial design, patient care, and medical imaging analysis. The paper discusses the challenges that come with using LLMs in medicine, such as making sure the information is accurate and private. |
Keywords
» Artificial intelligence » Hallucination » Optimization
