Summary of Generative Llm Powered Conversational Ai Application For Personalized Risk Assessment: a Case Study in Covid-19, by Mohammad Amin Roshani et al.
Generative LLM Powered Conversational AI Application for Personalized Risk Assessment: A Case Study in COVID-19
by Mohammad Amin Roshani, Xiangyu Zhou, Yao Qiang, Srinivasan Suresh, Steve Hicks, Usha Sethuraman, Dongxiao Zhu
First submitted to arxiv on: 23 Sep 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 paper introduces a novel disease risk assessment approach powered by large language models (LLMs), eliminating the need for programming required by traditional machine learning approaches. The authors fine-tune pre-trained LLMs using natural language examples, comparing their performance with traditional classifiers in various experimental settings. A mobile application is developed to integrate these fine-tuned LLMs as generative AI (GenAI) cores, enabling real-time interaction between clinicians and patients through conversational interfaces. The approach achieves high Area Under the Curve (AUC) scores with a limited number of fine-tuning samples, outperforming discriminative classification methods in low-data regimes. This work highlights the potential of generative LLMs for interactive no-code risk assessment and encourages further research in this emerging field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how big language models can be used to assess the risk of diseases without needing to write code. The authors fine-tune these models using a few examples, then test them against traditional methods. They create an app that lets doctors and patients talk to each other in real-time, providing personalized risk assessments. This approach does well even with limited data and can be useful for making decisions about patient care. |
Keywords
» Artificial intelligence » Auc » Classification » Fine tuning » Machine learning
