From Referral to Reporting: The Potential of Large Language Models in the Radiological Workflow
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View abstract on PubMed
Summary
This summary is machine-generated.Large language models (LLMs) can enhance radiology workflows by optimizing reporting and language-based tasks. Addressing challenges like hallucinations and data privacy is crucial for widespread adoption.
Area Of Science
- Radiology
- Artificial Intelligence
- Medical Informatics
Background
- Increasing workloads in radiology necessitate workflow optimization.
- Large Language Models (LLMs) offer potential solutions for various radiological processes.
Purpose Of The Study
- To review the applications of LLMs in radiology workflows.
- To identify challenges and propose solutions for LLM implementation.
Main Methods
- Review of potential LLM applications in radiology.
- Analysis of challenges including hallucinations, reproducibility, and data protection.
- Exploration of solutions like retrieval-augmented generation (RAG) and cloud-based approaches.
Main Results
- LLMs show promise in optimizing language-based tasks, especially report generation, using RAG and multi-step reasoning.
- Key challenges include hallucinations, reproducibility, data privacy, and ethical concerns.
- Technological advancements like RAG and cloud solutions can accelerate clinical implementation.
Conclusions
- LLMs have significant potential to support and optimize radiology workflows.
- Overcoming technical and ethical challenges is essential for successful LLM integration.
- RAG and cloud-based strategies are promising for advancing LLM adoption in clinical practice.
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