Agentic AI and Large Language Models in Radiology: Opportunities and Hallucination Challenges
Sara Salehi, Yashbir Singh, Kelly K Horst, Quincy A Hathaway, Bradley J Erickson, João Paulo Ramos Teixeira, Luis Coelho, João Paulo Pereira do Carmo · 2025
Read Agentic AI and Large Language Models in Radiology: Opportunities and Hallucination Challenges by Sara Salehi, Yashbir Singh, Kelly K Horst, Quincy A Hathaway, Bradley J Erickson, João Paulo Ramos Teixeira, Luis Coelho, João Paulo Pereira do Carmo (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12729288.txt.
Description
The field of radiology is experiencing rapid adoption of large language models (LLMs), yet their tendency to generate hallucinations (plausible but incorrect information) remains a significant barrier to trust. This comprehensive review evaluates emerging agentic artificial intelligence (AI) approaches, including multi-agent role-based systems, retrieval-augmented generation (RAG), and uncertainty quantification, to assess their potential for reducing hallucinations in radiology workflows.
Catalog record
| CREATOR | Sara Salehi, Yashbir Singh, Kelly K Horst, Quincy A Hathaway, Bradley J Erickson, João Paulo Ramos Teixeira, Luis Coelho, João Paulo Pereira do Carmo |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 28 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |