Advances in AI-driven image analysis for sarcoma diagnosis: A comprehensive scoping review

Detecting early-stage sarcoma can be challenging, given that it rarely shows clear symptoms. Further, for pathologists, accurately diagnosing and classifying sarcoma from medical images can also be difficult. To address these issues, researchers have used many AI models to predict and classify diseases from imaging data, including machine learning, deep learning, Transformers, the GPT family, large language models (LLMs), and one of the latest advances, multimodal LLMs. To contextualise this research, this study provides a comprehensive scoping review of recent studies on sarcoma diagnosis and classification. We located relevant articles in notable journals, books, and conference proceedings and summarised each study’s objectives, datasets, methods used, results, and limitations. Our objective was to identify recent AI-based techniques in sarcoma diagnosis that rely on different imaging modalities, including MRIs, CT scans, X-rays, and pathology slides. We have found that CNNs, SVMs, k-NN, decision trees, and their variants have been widely used for image classification. Also, Transformer and hybrid models are increasingly being used to improve performance, with high levels of accuracy reported in several studies. However, generalisability remains a key challenge given the limited number of studies that externally validate their model’s performance or use multi-institution data. The limitations of these studies are also discussed along with potentially useful future directions of research to assist experts. A dedicated section summarises early applications of GPT/LLMs to diagnosing sarcoma, highlighting their preliminary nature and the need for human oversight, reliability assessment, and validation. The article concludes by outlining the challenges of applying AI to solve problems in the current climate, noting issues such as data privacy, regulatory uncertainty, dataset transparency, and the need for external validation. In terms of diagnosing sarcoma from radiology and pathology imaging, there is also a need for calibrated reporting.