ArticleFrontiers in oncology2026
Automated detection of primary soft tissue sarcomas of the extremities using artificial intelligence and ChatGPT.
Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Toward clinical value, not just accuracy: deep learning segmentation of soft tissue sarcoma.Skeletal radiology · 2026Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: Developing effective Convolutional Neural Networks (CNN) for soft tissue sarcoma detection often requires numerous iterations and adjustments, demanding specialized IT (Information Technology) skills. This study aims to use ChatGPT 4 to simplify CNN adaptation, reducing the need for specialized IT skills while enabling efficient exploration of training configurations to enhance diagnostic accuracy. Methods: This study leveraged a preexisting Artificial Intelligence (AI) model adapted using a preexisting Convolutional Neural Network (CNN). The study involved 54 participants diagnosed with primary soft tissue sarcomas in the extremities and possessing complete Magnetic Resonance Imaging (MRI) datasets. AI adaptations and programming were conducted using TensorFlow and verified with ChatGPT. Model training involved a dataset split of 70% training, 15% validation and 15% test set on patient level split, processed over eight epochs. Results: The adapted CNN model demonstrated significant improvement across various MRI sequences, achieving high accuracy levels (up to 98.5%) and excellent sensitivity and specificity rates. The model performed robustly in differentiating tumor presence in MR images, with test accuracies as high as 93.9%. The inclusion of a Gradient-weighted Class Activation Mapping (Grad-CAM) heat map and probability scores in the diagnostic outputs further enhanced interpretative capabilities. Conclusion: This study highlights the potential of AI, particularly CNNs, in the early and accurate detection of soft tissue sarcomas, underscoring the technology's adaptability across different imaging modalities. The integration of large language models like ChatGPT into the model adaptation process emphasizes the reduced need for specialized IT skills, making advanced diagnostic tools more accessible and potentially improving diagnostic accuracy and patient outcomes in radiology and oncology.
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Registered trials
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