European Radiology

Excited to share our latest publication titled "Deep learning reconstruction algorithm and high-concentration contrast medium: feasibility of a double-low protocol in coronary computed tomography angiography" now available in European Radiology! In this article, we explore how feasible it is to reduce contrast volume and radiation dose using high-concentration Contrast Media and Deep Learning reconstruction. A big thank you to my co-authors and to my mentor Prof. Andrea Laghi for their invaluable contributions! Domenico De Santis Giuseppe Tremamunno Curzio Santangeli Tiziano Polidori Giovanna Bona Marta Zerunian Antonella Del Gaudio Luca Pugliese Check out the full article here: https://lnkd.in/dYYwaHu4 Sapienza Università di Roma Azienda ospedaliero-universitaria Sant'Andrea

🔦 #BrendansBest: Building Trust in AI for Radiology—A Complex, Yet Crucial Task 🔦 Artificial Intelligence (AI) is revolutionizing radiology, but the key to its successful implementation hinges on one critical factor: trust. In a recent article published in European Radiology, Magnus Bergquist et al.explored the multifaceted requirements needed to foster trust in AI tools in clinical radiology, providing valuable insights for both developers and clinicians. Trust in AI is not a simple, one-dimensional concept—it spans several key areas that need to be addressed for AI to truly enhance patient care. Here’s what the study found: 🔑 Key Findings: Reliability: AI tools must consistently perform at high levels of accuracy, ensuring that they can be relied upon in clinical settings. Transparency: Stakeholders need to understand how AI makes decisions, which requires clear, understandable explanations from developers. Quality Verification: There must be mechanisms in place to continually verify the performance and quality of AI tools. Inter-organizational Compatibility: AI tools should seamlessly integrate into existing healthcare systems, working across various platforms and organizations. While these aspects lay the foundation, the study also emphasizes that trust in AI must be built collaboratively. It’s not just about the technology—it’s about how stakeholders, from developers to clinicians, work together to meet these demands. Why Does This Matter for Radiology? Radiology is at the forefront of AI integration, and while the technology promises to improve accuracy, speed, and efficiency, it cannot replace human expertise. Instead, AI should support radiologists by enhancing their ability to diagnose and treat patients. However, this can only happen if radiologists and other healthcare professionals trust the tools they are using. Developing this trust is a complex process, but one that is crucial for AI to achieve its full potential. It’s about striking the right balance between technological innovation and human oversight. Final Thoughts: Trust in AI is a journey, not a destination. As this study shows, building that trust requires addressing both substantial and procedural demands. It’s a collaborative effort between developers, radiologists, and healthcare organizations, ensuring that AI solutions are reliable, transparent, and integrated into the fabric of clinical care. 📚 Read the full article here: https://lnkd.in/eCWEGczn How do you think we can best foster trust in AI within radiology? What challenges or opportunities do you see? Let’s discuss! 👇

💡3D ultrasound for thyroid nodules and ablations?💡 Please read all about it in our latest article, published in European Radiology Experimental. https://lnkd.in/eQGArgMf Thanks to all the authors for this great collaboration: Srirang Manohar, Michel Versluis, Sicco Braak, MD, PhD and Wyger Brink.

🔦 #BrendansBest: Building Trust in AI for Radiology—A Complex, Yet Crucial Task 🔦 Artificial Intelligence (AI) is revolutionizing radiology, but the key to its successful implementation hinges on one critical factor: trust. In a recent article published in European Radiology, Magnus Bergquist et al.explored the multifaceted requirements needed to foster trust in AI tools in clinical radiology, providing valuable insights for both developers and clinicians. Trust in AI is not a simple, one-dimensional concept—it spans several key areas that need to be addressed for AI to truly enhance patient care. Here’s what the study found: 🔑 Key Findings: Reliability: AI tools must consistently perform at high levels of accuracy, ensuring that they can be relied upon in clinical settings. Transparency: Stakeholders need to understand how AI makes decisions, which requires clear, understandable explanations from developers. Quality Verification: There must be mechanisms in place to continually verify the performance and quality of AI tools. Inter-organizational Compatibility: AI tools should seamlessly integrate into existing healthcare systems, working across various platforms and organizations. While these aspects lay the foundation, the study also emphasizes that trust in AI must be built collaboratively. It’s not just about the technology—it’s about how stakeholders, from developers to clinicians, work together to meet these demands. Why Does This Matter for Radiology? Radiology is at the forefront of AI integration, and while the technology promises to improve accuracy, speed, and efficiency, it cannot replace human expertise. Instead, AI should support radiologists by enhancing their ability to diagnose and treat patients. However, this can only happen if radiologists and other healthcare professionals trust the tools they are using. Developing this trust is a complex process, but one that is crucial for AI to achieve its full potential. It’s about striking the right balance between technological innovation and human oversight. Final Thoughts: Trust in AI is a journey, not a destination. As this study shows, building that trust requires addressing both substantial and procedural demands. It’s a collaborative effort between developers, radiologists, and healthcare organizations, ensuring that AI solutions are reliable, transparent, and integrated into the fabric of clinical care. 📚 Read the full article here: https://lnkd.in/eCWEGczn How do you think we can best foster trust in AI within radiology? What challenges or opportunities do you see? Let’s discuss! 👇