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In this case study, we spotlight a RedBrick AI customer, Annalise.ai, who is at the vanguard of radiology AI with their comprehensive AI algorithms for "assignment," "assistance," and "assurance." Annalise.ai's core differentiation comes from its focus on comprehensive algorithms that can provide deep and broad assessments of medical images. Their CXR and CTB products are industry-leading and widely used in the community. In this case study, we discuss how RedBrick AI helps Annalise.ai create high-quality training data. "RedBrick AI is really integral to how we develop AI at Annalise.ai. It allows us to label all of our data in a comprehensive and efficient manner, and also save time using the automatic segmentation functions" - Jarrel Seah. https://lnkd.in/eYzZyJgf.

Franziska Lobig and her paper “To pay or not to pay for artificial intelligence applications in radiology” was highlighted in one of the latest AuntMinnie.com articles discussing the reimbursement of #MedicalImaging AI applications. The article highlights the requirements for reimbursement, which in turn can help support the widespread adoption of medical #AI technologies. Notably, Franziska Lobig and her co-authors have developed a framework to help determine the value of an AI solution based on its clinical utility which can help determine guidelines for reimbursement. This is a significant step towards ensuring that AI apps with demonstrated clinical utility are appropriately reimbursed, ultimately improving outcomes for both patients and providers. Read the Aunt Minnie article here: https://lnkd.in/e3UqqzcT Delve into the clinical utility framework by Lobig et al. https://lnkd.in/edXhiGTK   #Radiology #ArtificialIntelligence #TransformingRadiology Exclusively for healthcare professional use. PP-CALA-ALL-0845-1 February 2024

Our CEO Ron Nag recently shared his insights with Pulse 2.0 on the evolving role of AI in medical imaging and diagnostics. Ron discusses the challenges and opportunities in this space and the importance of AI-driven solutions in improving patient outcomes. You can read the full interview, here: https://bit.ly/3AS4UKN #HealthcareAI #MedicalImaging #AI #BreastCancer

✍️ "Could AI disrupt the way we perceive trust in healthcare? With emerging technology, radiologists may find an unexpected assistant in artificial intelligence. But what does AI mean for the future of radiology? Could it redefine patient trust, or increase uncertainties among healthcare professionals? An intriguing evolution awaits us, which might make us reconsider the role of radiologists in our healthcare landscape. Will they welcome this digital ally or see it as a threat? Let's delve into how AI is reshaping the field of radiology with a lens of empathy and curiosity."

This paper discusses the implementation of AI in a radiology department of a large academic medical center in the Netherlands, emphasizing a holistic approach that addresses technology, workflow, and organizational challenges. 1️⃣ The study identifies the need for a scalable, seamless AI implementation. A Vendor-Neutral AI platform was implemented, which streamlined the integration of multiple AI applications, reduced overhead costs, and ensured a secure, standardized AI implementation process. 2️⃣ Addressing workflow integration, the study highlights the establishment of an Image Processing Group for consistent AI application use and the validation of AI algorithms to match local data characteristics. Emphasis was placed on integrating AI results into clinical workflows, improving radiologist interaction with AI outputs. 3️⃣ The paper underscores the importance of managing expectations and experience with AI. The Clinical AI Implementation Group was formed to evaluate AI project proposals, assess their viability, and disseminate lessons learned, fostering a culture of continuous learning and collaboration. 4️⃣ The paper emphasizes the necessity of a holistic approach to AI implementation in healthcare, integrating technological, workflow, and organizational aspects to achieve sustainable value from AI. This approach goes beyond simply adding AI to existing systems and involves comprehensive restructuring and realignment to create lasting improvements in clinical practice. The insights from this case study are particularly valuable for healthcare institutions looking to implement AI in their clinical workflows, offering practical strategies for addressing common challenges in AI adoption. ✍🏻 Kim, B., Romeijn, S., van Buchem, M. et al. A holistic approach to implementing artificial intelligence in radiology. Insights Imaging 15, 22 (2024). DOI: 10.1186/s13244-023-01586-4 ✅ Subscribe to my newsletter and stay at the forefront of groundbreaking studies. Get started here: https://lnkd.in/eR7qichj.

Discover the role of AI in the Medical Imaging field in this incredible article. Let’s continue to revolutionize patient care! 💊🤖 #AI #ArtificialIntelligence #MedicalImaging

Bringing you our recent case study with Rad AI showcasing their transformative partnership with Kolena. Kolena's AI quality platform was instrumental in refining Rad AI's machine learning models, significantly enhancing their ML pipelines. They were able to boost their capability to deliver cutting-edge, reliable AI-driven radiology services, and reinforce customer trust. #casestudy #AI #machinelearning

"Kolena's testing suite has been a transformative tool for Rad AI, allowing us to optimize our model testing capabilities and evaluate the model performance with precision and granularity..." Check out an example of how the Rad AI team leveraged Kolena to save 90% of their time spent on model testing and validation. Read the full case study below 📖 👇 #CaseStudy #RadAI #HealthcareInnovation #LLMs #AIinHealthcare

🚀 Thrilled to share a case study from our collaboration with Rad AI, a leader in revolutionizing radiology with GenAI. Founded by the youngest US radiologist in history, Rad AI is on a mission to empower physicians, reduce burnout, and elevate patient care to unprecedented levels. 🔍 Key Achievements: • Enhanced Accuracy: Leveraging Kolena's AI quality technology, Rad AI has further reduced errors in radiology reports by nearly 80%, making significant strides towards eliminating diagnostic errors. • Innovation at Scale: Saved more than 90% of time spent on model testing and validation (from 2 weeks/model to 1-2 hours) which led to a substantial increase in the speed of model development. • Elevated Precision in Model Testing: more precise adjustments and optimizations, propelling generative models to unprecedented levels of accuracy and reliability. This collaboration stands as a testament to how GenAI can redefine healthcare, and Rad AI continues to set the benchmark for AI in healthcare. A huge thank you to Rad AI's leadership and team: Doktor, Deniz, Sara Ho 👏 💬 I'd love to hear your thoughts on how AI is shaping the future of healthcare and the potential challenges we might face along the way. This case study is brought to you by our very own, Skip Everling! #machinelearning #genai #healthcare