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Generative Large Language Model-Powered Conversational AI App for Personalized Risk Assessment: Case Study in

Mohammad Amin Roshani1, Xiangyu Zhou1, Yao Qiang2

  • 1Department of Computer Science, Wayne State University, Detroit, MI, United States.

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Summary
This summary is machine-generated.

Generative large language models (LLMs) offer a no-code solution for real-time disease risk assessment, outperforming traditional methods in low-data scenarios. This AI approach enhances clinical decision-making with conversational interactions.

Keywords:
COVID-19artificial intelligenceconversational AIlarge language modelpersonalized risk assessment

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Area of Science:

  • Artificial Intelligence in Healthcare
  • Natural Language Processing
  • Machine Learning for Clinical Decision Support

Background:

  • Large language models (LLMs) show promise for healthcare tasks like disease risk assessment, offering flexibility beyond traditional structured data methods.
  • Generative LLMs can be applied to clinical settings without requiring programming expertise.
  • Conversational AI powered by LLMs presents a novel approach to disease risk stratification.

Purpose of the Study:

  • To evaluate generative LLMs (LLaMA2-7b, Flan-T5-xl) for predicting COVID-19 severity.
  • To develop a real-time, no-code risk assessment solution using chatbot interactions.
  • To compare LLM performance against traditional machine learning classifiers (logistic regression, XGBoost, random forest).

Main Methods:

  • Fine-tuning LLMs with few-shot natural language examples from a pediatric COVID-19 dataset.
  • Developing a mobile application for real-time, no-code risk assessment via clinician-patient dialogue.
  • Comparing LLM performance with traditional classifiers using Area Under the Curve (AUC) and analyzing LLM attention layers for interpretability.

Main Results:

  • Generative LLMs demonstrated strong performance, particularly in low-data settings (e.g., T0-3b-T achieved AUC 0.75 in zero-shot).
  • LLMs showed competitive or superior performance compared to traditional models as data increased (e.g., Flan-T5-xl-T reached AUC 0.70 at 32-shot).
  • The mobile app provided real-time assessments and personalized insights through attention-based feature importance.

Conclusions:

  • Generative LLMs are a robust alternative to traditional classifiers, especially with limited labeled data.
  • LLM-powered conversational AI offers adaptability for clinical settings, enabling real-time, personalized assessments without coding.
  • Further research into LLM applications for disease risk assessment and clinical decision support is warranted.