Endometriosis is a gynecologic inflammatory condition that affects up to 10% of reproductive-aged women worldwide. The disease exhibits heterogeneous presentations and is associated with a prolonged diagnostic delay, often exceeding seven years, because existing diagnostic modalities such as transvaginal ultrasound, magnetic resonance imaging, and the biomarker cancer antigen 125 (CA-125) are suboptimal. This review examines how machine learning (ML) is playing an increasingly significant role in early, non-surgical endometriosis diagnosis through two main approaches: symptom clustering and imaging integration. Unsupervised ML algorithms such as k-means, partitioning around medoids, and Bayesian networks have demonstrated success in identifying clinically informative endometriosis phenotypes from patient-reported symptoms and electronic health records. Concurrently, ML models such as convolutional neural networks and radiomics approaches have achieved high accuracy in lesion detection from imaging data, in some cases surpassing human interpretation. Despite these advances, significant challenges remain, including limited access to large, annotated multimodal datasets, the absence of widely accepted evaluation standards, and concerns regarding interpretability and generalizability. Multicenter, integrative studies and the incorporation of explainability techniques are recommended as potential strategies to address these gaps. Finally, multimodal ML approaches that combine symptomatology and imaging data hold substantial promise for reducing diagnostic delays, facilitating early intervention, and improving clinical outcomes in the management of endometriosis.

The tumor microenvironment [TME] is a dynamic ecosystem where spatial organization and metabolic reprogramming play a crucial role in immune response, tumor progression, and therapeutic response. Recent breakthroughs in spatial transcriptomics, metabolomics, and multiplexed imaging studies have shown that complex immunometabolic niches are involved in therapeutic resistance, including conventional and immunotherapeutic approaches. Artificial intelligence [AI] technology has been recognized as a revolutionary concept that allows the integration of complex data, thereby facilitating the scalable extraction of spatial, molecular, and cellular features from routine histopathology and multi-omics platforms. This review of the current evidence on AI-based spatial immunometabolic studies of the tumor microenvironment aims to provide a comprehensive overview of the current evidence, including AI-based spatial immunometabolic studies of the tumor mi-croenvironment, with special reference to digital pathology, spatial transcriptomics, and multimodal data fusion. The current challenges, including data heterogeneity, model interpretability, generalizability, and biological validation, will be discussed. The emerging trends in AI-based spatial immunometabolism, including multimodal foundation models, federated learning, and spatially resolved target discovery, will be discussed. AI-based spatial immunometabolism will be a cornerstone in precision oncology, with the potential to improve patient stratification, therapeutic approaches, and clinical translation.