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Large Language Models can Help with Biostatistics and Coding Needed in Radiology Research.

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Large language models (LLMs) can assist radiology researchers by generating code for statistical analysis and deep learning tasks. While useful for initial drafts, researchers must validate LLM-generated code due to potential errors and hallucinations.

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Area of Science:

  • Radiology research
  • Medical imaging analysis
  • Artificial intelligence in medicine

Background:

  • Radiology research frequently involves complex data handling, statistical analysis, and coding.
  • Large language models (LLMs) have demonstrated capabilities in bioinformatics, suggesting potential applications in radiology.

Purpose of the Study:

  • To evaluate the efficacy of LLMs in providing statistical and deep learning solutions and code for radiology research.
  • To compare the performance of ChatGPT-4o, ChatGPT-3.5, and Google Gemini in generating code for biostatistics and deep learning tasks.

Main Methods:

  • LLMs (ChatGPT-4o, ChatGPT-3.5, Gemini) were assessed using web-based chat interfaces.
  • Experiment 1 involved generating R code for statistical tests and data visualization using a Cancer Imaging Archive dataset.
  • Experiment 2 involved generating Python code for transformer-based deep learning models for pneumonia detection using the RSNA-STR dataset.

Main Results:

  • ChatGPT-4o provided correct statistical answers in 7 out of 8 scenarios, with fewer runtime errors in generated R code compared to ChatGPT-3.5 and Gemini.
  • Both ChatGPT-4o and Gemini successfully generated initial Python code for deep learning tasks, with errors resolved through iterative prompting.
  • LLMs demonstrated utility in providing baseline code and assisting in error resolution for radiology research tasks.

Conclusions:

  • LLMs can effectively assist in coding for radiology research, offering initial code for data visualization, statistical tests, and deep learning models.
  • Researchers must critically review and validate LLM-generated code due to potential inaccuracies, hallucinations, and data privacy concerns.
  • LLMs serve as valuable tools for researchers needing foundational code and troubleshooting assistance in radiology research.