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ChatGPT and radiology report: potential applications and limitations.
Marco Parillo1, Federica Vaccarino2,3, Bruno Beomonte Zobel4,3
1Radiology, Multizonal Unit of Rovereto and Arco, APSS Provincia Autonoma Di Trento, Trento, Italy. marco.parillo@apss.tn.it.
Large language models like ChatGPT show promise in radiology reporting, assisting radiologists with tasks like data extraction and report generation. However, careful integration and validation are needed due to limitations such such as potential biases and data privacy concerns.
Area of Science:
- Artificial Intelligence in Medicine
- Medical Imaging Analysis
Background:
- Growing interest in large language models (LLMs) like ChatGPT for medical applications.
- Radiology reports are a key area for AI-driven analysis due to their text-based nature.
Purpose of the Study:
- To provide an overview of the strengths and limitations of ChatGPT in radiological reporting.
- To explore the potential clinical applications of LLMs in radiology.
Main Methods:
- Review of current research and capabilities of ChatGPT in processing and understanding radiology reports.
- Analysis of potential benefits and drawbacks of integrating ChatGPT into radiological workflows.
Main Results:
- ChatGPT can assist radiologists with diagnosis support, structured reporting, data extraction, error identification, and patient-friendly report generation.
- Identified limitations include hallucinations, bias, data privacy issues, and challenges in complex clinical scenarios.
Conclusions:
- ChatGPT offers potential benefits but cannot replace radiologists.
- Responsible integration requires careful planning, rigorous validation, and addressing ethical and technical challenges.
- Radiologists are well-positioned to lead the integration of AI tools like ChatGPT in radiology.
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