The integration of artificial intelligence (AI) into clinical workflows for soft tissue sarcomas (STS) marks a major step forward in precision oncology. As diagnostic complexity grows, AI-driven genomics—particularly in detecting fusion genes—enhances sensitivity, speed, and accuracy, improving patient stratification and guiding targeted therapies. The convergence of AI with multi-omic datasets, including transcriptomic, epigenomic, and imaging data, offers a holistic view of sarcoma biology, enabling earlier detection and more personalized treatment. Achieving these benefits, however, requires global collaboration, standardized protocols, and continuous clinician education. Ultimately, AI’s transformative potential in STS management depends not only on technological innovation but also on the establishment of robust frameworks for its clinical adoption.
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Khizar et al. (2025) studied this question.
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