The precision diagnosis and treatment of chest wall tumors heavily rely on imaging assessment, which is limited by the subjectivity of conventional methods and their insensitivity to microscopic features. Artificial intelligence (AI) technologies, particularly radiomics and deep learning, offer new opportunities to overcome these bottlenecks. It is important to note, however, that direct AI research focused specifically on chest wall tumors remains scarce. Most current insights are derived from methodological borrowing and conceptual extrapolation of studies on other solid tumors, such as lung, breast, and esophageal cancers. This review systematically outlines the potential application framework of AI in the management of chest wall tumors, encompassing benign-malignant differentiation, non-invasive pathological subtyping, early treatment response prediction, and prognosis stratification based on multi-modal imaging. It discusses the core model-building strategies and validation processes centered on data fusion, and critically analyzes the unique challenges in this field, including data scarcity, model interpretability, and clinical translation. Moving forward, key directions for translating the potential of AI into clinical reality include fostering the development of dedicated chest wall tumor datasets, conducting prospective validation studies, and exploring "imaging-genomics" integration as well as dynamic decision-support systems.
Traditional Chinese medicine (TCM) emphasizes syndrome differentiation and individualized interventions. However, in real-world clinical studies, complex confounding and the unobservability of counterfactual outcomes pose major challenges for evaluating individualized treatment effects. Causal machine learning, grounded in the potential outcomes framework, provides a flexible, nonparametric approach to model treatment assignment and outcome distributions. By adjusting for baseline and time-varying confounding, it enables the generation of patient-specific counterfactual outcomes and more accurate estimation of causal effects at the individual level. This methodological advance addresses the limitations of conventional approaches that focus mainly on average treatment effects. Taking advanced colorectal cancer as a case example, we demonstrate how causal machine learning can identify "which patients benefit most from which treatment?" thereby supporting personalized clinical decision-making and optimization of therapeutic strategies in TCM. Overall, causal machine learning holds both methodological significance and direct clinical value for individualized efficacy research in TCM.