Dandelion Health的动态

Last week, Dandelion launched our Clinical AI Marketplace along with a proof of concept paper showing how AI + Real-World Data can generate new clinical insights for life sciences companies. Today, I take a step back and share more about the strategy, structure, and potential of the Clinical AI Marketplace on Dandelion's blog. Give it a read and let me know what you think! https://lnkd.in/eMtBgW-u

Nicholas Gossen shares insights on Dandelion's Clinical AI Health Marketplace, highlighting its game-changing capabilities for life sciences companies. By combining multimodal RWD with validated AI, the Marketplace empowers faster innovation, cost reduction, and enhanced creativity in the industry. The platform accelerates the iteration cycle, centralizes data infrastructure, and fosters external AI collaborations, allowing companies to make bold moves and learn rapidly. Read the full article here: https://lnkd.in/gjDD-3xg

As AI plays an increasingly significant role in healthcare decision-making, trust remains a key concern. How can healthcare leaders ensure transparency, reduce bias, and maintain a balance between AI and human oversight? Building frameworks that guide ethical AI use and foster confidence in these systems are key considerations. We explore key areas that organizations can focus on to approach this challenge and ensure AI leads to better healthcare outcomes: https://bit.ly/4eFeE9P #AIinHealthcare #HealthcareLeaders #EthicalAI #BHMHealthcareSolutions

Foundation models, large language models, and generative artificial intelligence captured the attention of providers, biopharma companies, payers, and investors over the past year, driven by the promise of making healthcare delivery more efficient, innovative, and effective. Learn more about the transformative potential of generative AI on #healthcare in our annual healthcare private equity report.

Generative AI has the potential to transform healthcare in the United States. Health providers are exploring how it can improve the accuracy of medical image interpretation, which could lead to earlier disease detection and timely treatments. Biotech companies are excited about using AI for drug discovery, predicting drug interactions, and identifying biomarkers. Insurers, too, are looking into AI to help analyze huge amounts of data for insights, fraud detection, and risk assessment. However, we need to keep our expectations grounded. The healthcare industry is rightly cautious about adopting new technologies because patient safety is always the top priority. Typically, new tech is introduced in low-risk areas first to ensure it’s safe and reliable. For generative AI, this means starting with less critical tasks until its accuracy is proven. This careful approach helps integrate AI into healthcare responsibly, ensuring it enhances patient care without introducing new risks. #AI #technology #healthinnovation #healthcare https://lnkd.in/gWDX9zKw

By integrating advanced AI technologies into clinical and administrative processes, we are helping to create a future where patient care is more personalized, predictive, and effective. Read more from Rajul Jain thought leader at ProPharma. https://lnkd.in/eaBQwrD8

Just published in JAMA by Robert Califf et. al.: the FDA view on AI. Unsurprisingly, there is a lot of caution around unleashing LLMs in the wild. In the paper, hallucinations are, rightly so, seen as a big risk. I will say tho, that we have really good methods to avoid those (by grounding the LLMs in truth using e.g. Retrieval Augmented Generation (RAG) approaches). Here is a generated summary (checked by me), also attaching a Podcast version of the paper to listen to (first 15 min, full version on YT). ● The FDA has already authorized nearly 1000 AI-enabled medical devices, mostly in radiology and cardiology. They've also received hundreds of submissions for drug development that involve AI. ● They stress the need for coordinated regulation of AI across different sectors in the US government and globally. This means flexible and adaptable regulatory frameworks that can evolve alongside rapidly-changing AI technology. ● Transparency is key! Sponsors need to be upfront about their use of AI, and regulators need the expertise to properly evaluate AI during the premarket development phase. ● The FDA is calling for a total product life cycle approach (TPLC) to AI regulation. A core element of this is continuous, localized monitoring of AI performance after it's been released to the market. ● We're in uncharted territory with large language models (LLMs) and generative AI. The FDA recognizes they need to develop special ways to evaluate these tools, especially when it comes to their unforeseen consequences. ● Big tech may be dominating AI development, but the FDA emphasizes the importance of fair approaches that allow smaller companies, start-ups, and academic institutions to ensure their AI models are safe and effective. ● At the end of the day, it's all about the patient. The FDA pushes for a balance between using AI to optimize financial returns and ensuring it truly improves patient outcomes. ● The FDA plays a critical role, but it takes a village. Strong oversight from the FDA, plus close collaboration between regulated industries, academia, and the agency itself, is crucial for AI to thrive in healthcare and maintain public trust.

FDA's perspective on AI. Calling for a TPLC approach to regulation for sponsors. Efficacy and Safety will drive AI utilized in R&D.

Diving into part two of Yahya Anvar's series on AI in healthcare, this time zooming in on trust and communication. It's great seeing how Envision Pharma Group keeps AI clear and ethically integrated for patient care, setting high standards for trust in life sciences. Can't wait to see how this shapes patient outcomes!

?? ?? 2/3 of the blog series from?Yahya Anvar, Chief of AI Science & Insights at?OKRA.ai, explains trust and communication in AI ?????? Discover more ???https://lnkd.in/dRC9KKa5 #ai?#trust?#healthcare?#innovation?#blog