OBJECTIVE To provide guidance on the scientific and standardized application of artificial intelligence （AI） and multimodal fusion techniques in constructing predictive models for adverse drug reactions （ADR） of antineoplastics， thereby enhancing the safety of antineoplastic therapy. METHODS Jointly initiated by the Clinical Pharmacy and Translational Pharmacy Society of the Liaoning Provincial Life Sciences Association and other professional societies， and organized by the Fourth Affiliated Hospital of China Medical University， the writing group conducted a comprehensive search of domestic and international databases using keywords such as “multimodal data” “artificial intelligence” “adverse drug reactions”“antineoplastics” and “models” to collect guidelines， expert consensuses， and relevant guidance literature. Based on this search， and combined with China’s practical experience in applying AI to oncology pharmaceutical care， a consensus framework and preliminary recommendations were drafted. Following evaluation and discussion by an external expert panel， the writing group incorporated the assessment results and formulated recommendations based on those endorsed by 90% or more of the experts. RESULTS &CONCLUSIONS After multiple rounds of review， discussion and revision， this consensus outlines the process for constructing an ADR prediction model of antineoplastics， encompassing “defining research objectives-data preparation-selection of algorithmic frameworks-design of multimodal data fusion strategies-model training and validation-interpretability analysis-clinical deployment-publication of results”. It also formulates 18 recommendations regarding core aspects such as data preparation， algorithm framwork selection， multimodal data fusion strategy design， model validation and interpretability analysis， and clinical deployment. These recommendations are intended for use by physicians in the field of oncology， clinical pharmacists， researchers， and AI technology developers to assist in making optimal clinical treatment decisions.