ChatGPT assisted generation of systematic review ideas in urooncology

OBJECTIVE: The aim of this study was to analyze the performance of ChatGPT in the generation of new systematic review ideas in urooncology. METHODS: In September 2024, we requested ChatGPT Version 4.0 to generate 10 systematic review ideas in general urooncology and also 10 ideas for each of the four subcategories: bladder cancer, prostate cancer, renal cell carcinoma, and testicular cancer. We utilized PubMed and Scopus to examine 50 ideas to determine whether prior systematic reviews had addressed them. Novelty was defined as a topic without prior systematic review. RESULTS: ChatGPT generated 30% original systematic review ideas, with 15 out of 50 being novel. The novelty rate for general urooncology was 50%. The rates for subcategories were 10% for both bladder and prostate cancer, 50% for renal cell carcinoma, and 30% for testicular malignancies. Approximately 10% of general research concepts external to systematic reviews were novel. CONCLUSION: ChatGPT performed very well in producing creative, apt, and partially viable systematic review ideas in urooncology. Although human judgment is still required to determine feasibility and create proposals with better accuracy, ChatGPT and other large language models can be useful aids while designing research, especially for dynamic and information-heavy disciplines such as urooncology.