Abstract

Primarily afflicting young adults and teenagers, the most prevalent primary malignancy of the bones is osteosarcoma. For enhancing patient results and directing proper therapy approaches, early diagnosis and precise risk classification are essential. The changing nature of artificial intelligence (AI) in revolutionizing osteosarcoma identification and prognosis is investigated in this systematic review. In examining imaging, histopathological, clinical, and genomic data, the review combines results from current literature and emphasizes artificial intelligence uses, including DL, radiomics, CNNs, and ensemble machine learning models. Although risk stratification models have shown promise in forecasting metastasis, therapeutic response, and survival outcomes, artificial intelligence-driven diagnostic models have high accuracy in differentiating malignant from benign lesions. Notwithstanding these improvements, great hurdles include data heterogeneity, model interpretability, restricted applicability, and incorporation into clinical workflows. The paper also notes recent initiatives to improve clinical acceptance, including explainable AI, multimodal data fusion, and federated learning. The review underlines first the transformational opportunities of artificial intelligence in providing osteosarcoma patients with customary medication and then supports teamwork among clinicians, scientists, and technologies to fully realize its full promise in practical oncology settings.

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