biostudies-literatureUnknown27Huang W<h4>Background</h4>Influenza in mainland China results in a large number of outpatient and emergency visits related to influenza-like illness (ILI) annually. While deep learning models show promise for improving influenza forecasting, their technical complexity remains a barrier to practical implementation. Large language models, such as ChatGPT, offer the potential to reduce these barriers by supporting automated code generation, debugging, and model optimization.<h4>Objective</h4>This study aimed to evaluate the predictive performance of several deep learning models for ILI positive rates in mainland China and to explore the auxiliary role of ChatGPT-assisted development in facilitating model implementation.<h4>Methods</h4>ILI positivity rate data spanning from 2014 to 2024 were obtainedJournal of medical Internet researche74423https://www.ebi.ac.uk/biostudies/studies/S-EPMC12227151biostudies-literatureChatGPT-Assisted Deep Learning Models for Influenza-Like Illness Prediction in Mainland China: Time Series Analysis.PMC12227151Jiang JLai JXie XZhan BZhang MWei WYe LLiang HHuang WHe XLai SChen RYuan ZfalseChatGPT-Assisted Deep Learning Models for Influenza-Like Illness Prediction in Mainland China: Time Series Analysis.<h4>Background</h4>Influenza in mainland China results in a large number of outpatient and emergency visits related to influenza-like illness (ILI) annually. While deep learning models show promise for improving influenza forecasting, their technical complexity remains a barrier to practical implementation. Large language models, such as ChatGPT, offer the potential to reduce these barriers by supporting automated code generation, debugging, and model optimization.<h4>Objective</h4>This study aimed to evaluate the predictive performance of several deep learning models for ILI positive rates in mainland China and to explore the auxiliary role of ChatGPT-assisted development in facilitating model implementation.<h4>Methods</h4>ILI positivity rate data spanning from 2014 to 2024 were obtained2025-01-01T00:00:00Z2025 Jun2026-03-31T11:10:43.569Z2025-07-28T03:04:19.328ZS-EPMC122271514057765810.2196/74423