The value of artificial intelligence combined with multimodal data analysis in tumor immunotherapy and targeted therapy

The advent of immunotherapy and targeted therapy has revolutionized oncology treatment in recent years, marking a transformative shift toward precision medicine. Artificial intelligence (AI) technology has played a pivotal role in tumor immunotherapy, targeted therapy, and related fields, by enhancing treatment efficacy prediction, identifying novel targets, and enabling personalized therapeutic strategies. In its early stages, AI development relied primarily on unimodal data processing architectures, which were limited to analyzing single data types and thus constrained in functionality. However, recent breakthroughs in data acquisition and integration have ushered in a new era of multimodal data fusion in oncology. Consequently, the integration of AI with multimodal data analytics now supports improved diagnostic accuracy, more precise prognosis prediction, and optimized individualized treatment planning-particularly in tumor immunotherapy and targeted therapy. Concurrently, this rapidly evolving domain faces pressing challenges, including data scarcity and lack of standardization, difficulty in aligning multimodal data, and the opaque, "black-box" nature of complex deep-learning models. Therefore, in this review, we aimed to summarize common AI technologies used in tumor diagnosis and treatment, multimodal data sources and fusion methods, applications of AI combined with multimodal data analysis in tumor immunotherapy and targeted therapy, and the key challenges currently facing this integrated approach.