AI in Healthcare

AI assisted Pediatric Bone Age Workflow Study

2 min · 3 de sep de 2025
Portada del episodio AI assisted Pediatric Bone Age Workflow Study

Descripción

In this episode of the AI in Healthcare podcast, we break down a new study by Jeong and colleagues that explores the impact of AI assistance on radiologist reading times and workflow efficiency. One‑line takeaway: AI assistance reduced reading time and improved throughput for bone‑age radiograph interpretation in a real‑world retrospective cohort. Reference The Impact of Artificial Intelligence on Radiologists’ Reading Time in Bone Age Radiograph Assessment Citation: Jeong S, Han K, Kang Y, et al. Journal of Imaging Informatics in Medicine. 2025 Aug;38(4):1915‑1923. https://pubmed.ncbi.nlm.nih.gov/39528879/

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In this episode of the AI in Healthcare podcast, we explore new research on using gradient-boosted models to predict coronary artery disease.  00:00 Introduction to AI in Healthcare Podcast 00:09 Gradient-Boosted Models in Coronary Artery Disease Prediction 00:47 Improving Referral Pathways with Transparent Models 01:18 Pragmatic Steps for Clinical Implementation 01:40 Key Takeaways and Recommendations 01:55 Conclusion Williams MC, Guimaraes ARM, Jiang M, Kwieciński J, Weir-McCall JR, Adamson PD, et al. Machine learning to predict high-risk coronary artery disease on CT in the SCOT-HEART trial. Open Heart. 2025 Sep 1;12(2):e003162. doi:10.1136/openhrt-2025-003162. PMCID: PMC12406813. PMID: 40889953.

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