Generative Large Language Model—Powered Conversational AI App for Personalized Risk Assessment: Case Study in COVID-19
Generative LLMs provide a robust alternative to traditional classifiers, particularly in scenarios with limited labeled data. Their ability to handle unstructured inputs and deliver personalized, real-time assessments without coding makes them highly adaptable to clinical settings. This study underscores the potential of LLM-powered conversational artificial intelligence (AI) in health care and encourages further exploration of its use for real-time, disease risk assessment and decision-making support.
Authors
- Srinivasan Suresh (ORCID: https://orcid.org/0000-0003-1717-7593)
- Dongxiao Zhu (ORCID: https://orcid.org/0000-0002-0225-7817)
- Yao Qiang (ORCID: https://orcid.org/0000-0003-2995-3385)
- Usha Sethuraman (ORCID: https://orcid.org/0000-0002-5183-0325)
- Steven D. Hicks (ORCID: https://orcid.org/0000-0002-9579-6798)
- Xiangyu Zhou (ORCID: https://orcid.org/0009-0000-0776-8045)
- Mohammad Amin Roshani
Institutions
- Oakland University (US)
- Wayne State University (US)
- Children's Hospital of Pittsburgh (US)
- Children's Hospital of Michigan (US)
- Penn State Milton S. Hershey Medical Center (US)
Publication Details
- Journal
- JMIR AI
- Published
- 2025-02-23
- DOI
- https://doi.org/10.2196/67363
- Citations
- 11
- Primary Topic
- Topic Modeling
- Type
- article
- Field-Weighted Citation Impact
- 27.29