Beyond Human Limits: The Promise and Pitfalls of Large Language Models in Radiology Research
Bibliographic record
Abstract
This review examines the applications and challenges of large language models (LLMs), like OpenAI's ChatGPT, in radiology research. ChatGPT can assist radiology researchers in generating new ideas, finding and summarizing research papers, designing studies, analyzing data, and facilitating manuscript writing. LLMs are powerful tools with numerous applications in radiology research. However, users should be mindful of potential pitfalls, such as producing incorrect or biased outputs and inconsistent responses, along with ethical and privacy concerns. We discuss approaches to optimize models and address these issues, including prompting techniques like chain-of-thought prompting, retrieval-augmented generation, and fine-tuning. For researchers, prompt engineering can be particularly effective. This review seeks to demonstrate how researchers can utilize ChatGPT for radiology research while offering strategies to mitigate associated risks. We aim to help researchers harness these potent tools to safely boost their productivity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".