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.  

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/

Join us on the AI in Healthcare podcast as we discuss into the groundbreaking development of VentAI, a reinforcement learning algorithm designed to recommend optimal ventilator settings for ICU patients.

00:00 Introduction to AI in Healthcare

00:06 VentAI: Revolutionizing Ventilator Settings

00:20 Study and Validation of VentAI

00:54 Performance and Comparison with Clinician Care

01:37 The Role of AI in Clinical Decision Making

01:56 Future of AI in Intensive Care

02:02 Conclusion and Follow Us