On the utility of ChatGPT in conducting a literature review on deep learning for dopamine transporter SPECT with [¹²³I]ioflupane.

AIM: This study evaluated ChatGPT (GPT-5.2) for drafting a review paper on deep learning in dopamine transporter (DAT)-SPECT with [¹²³I]ioflupane.

METHODS

The review workflow consisted of 3 steps: (i) literature search, (ii) generation of structured summaries with 24 predefined fields for each publication, and (iii) drafting a review paper based on the structured summaries across all publications. A detailed prompt for ChatGPT was iteratively designed for each step with ChatGPT support. A manual literature search was independently performed by an expert in DAT-SPECT and deep learning. ChatGPT-generated structured summaries were manually fact-checked against the full publications and corrected where necessary. The review draft generated by ChatGPT was checked against the corrected summaries.

RESULTS

When prompted to compile an exhaustive list of publications, ChatGPT cited 13 papers, whereas the manual search identified 70 relevant publications, 67 of which were included. Corrections to ChatGPT-generated structured summaries were required in 27 cases (40.3%), affecting one or two of the 24 predefined fields, while no changes were necessary in 40 publications (59.7%). Most corrections could likely have been avoided by more precise prompting. All numerical information (dataset sizes, train-test splits, performance metrics) was correct. The review draft (~950 words) generated by ChatGPT was content-wise meaningful and accurate, but contained referencing errors, including incorrect citations, missing references, and citations of non-existent publications.

CONCLUSIONS

ChatGPT is a highly effective tool for drafting review manuscripts in nuclear medicine imaging, but its limitations in literature retrieval and referencing require careful expert supervision.