Summary of Ai in Radiological Imaging Of Soft-tissue and Bone Tumours: a Systematic Review Evaluating Against Claim and Future-ai Guidelines, by Douwe J. Spaanderman (1) et al.
AI in radiological imaging of soft-tissue and bone tumours: a systematic review evaluating against CLAIM and FUTURE-AI guidelines
by Douwe J. Spaanderman, Matthew Marzetti, Xinyi Wan, Andrew F. Scarsbrook, Philip Robinson, Edwin H.G. Oei, Jacob J. Visser, Robert Hemke, Kirsten van Langevelde, David F. Hanff, Geert J.L.H. van Leenders, Cornelis Verhoef, Dirk J. Gruühagen, Wiro J. Niessen, Stefan Klein, Martijn P.A. Starmans
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The systematic review focuses on Artificial Intelligence (AI) methods using radiological imaging for diagnosing and prognosing soft-tissue and bone tumours. The study highlights the challenges in translating AI methods into clinical practice, evaluating studies against the Checklist for AI in Medical Imaging (CLAIM) and the FUTURE-AI international consensus guidelines. The review includes 325 articles that performed moderately on CLAIM but poorly on FUTURE-AI. Imaging-AI tools for STBT remain at the proof-of-concept stage, indicating room for improvement. Future efforts should focus on design, development, evaluation, and data reproducibility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study looks at how artificial intelligence is used in medical imaging to help diagnose and predict soft-tissue and bone tumours. Researchers found that many of these AI methods are not yet ready for real-world use because they haven’t been tested well enough or explained clearly enough. The authors suggest that future work should focus on making sure AI methods are designed with clinical needs in mind, can be trusted to make good decisions, and provide transparent results. |
