Signify Research Ltd’s Post

🌟 𝗧𝗥𝗔𝗜𝗡: 𝗔𝗜 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗮𝗻𝗱 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗔𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗲 𝗖𝗹𝗶𝗻𝗶𝗰𝗮𝗹 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 Founded in 2023 as a spin-off from the IRCCS Humanitas Research Hospital, our mission is clear: revolutionizing clinical trials and precision medicine leveraging Generative AI. 🔬 What makes us different? Our Generative AI technology is clinically validated (tested on large real-world patient populations) and backed by solid scientific evidence, thanks to our collaboration with healthcare experts and one of Europe's most advanced AI research centers. 💡 Our focus: - Advanced AI models: #trained on real-world clinical data to analyze multimodal information (genomic, textual, imaging). - Large Language Models: #LLM model to extract value from medical records to enhance efficiency and accuracy across clinical domains. - Real-world applications: from multimodal #SyntheticData for clinical trials to #DigitalTwin for predicting patient outcomes. We’re already making waves in European consortia and strategic technology partnerships. Our goal? To redefine the future of healthcare. 👉 Join us on this journey towards a new era in medicine, where Generative AI transforms clinical research and patient care. Saverio D'Amico Matteo G Della Porta #Humanitas #Train #HealthcareAI #GenerativeAI #AIForHealth #PharmaInnovation #PersonalizedMedicine #ClinicalTrials #AI #ArtificialIntelligence

🚀 AI in Medicine: say goodbye to the need for sensitive and expensive data Dive into the future with the emerging startup Ryver.ai and unlock the potential of AI in Medicine without the biggest obstacle – lack of clinical, accessible data! 💡 While generative AI in the form of Large Language Models (LLMs) are already transforming industries, the full power of GenAI for generating images and videos is yet to be harnessed. Discover this groundbreaking business opportunity in the medical space! 🏥✨ 🔍 What to expect: - Learn about synthetic data created by generative diffusion models and find out just how much real data is needed for analyzing CT, MRI, and other medical imaging data. 🧬 - Put your skills into practice by participating in a team competition for the best X-ray classifier using synthetic data. The goal will be to use as much synthetic data as possible and still get a good classifier result. 🔬 - Network with CTO and Co-founder of RYVER.AI Kathrin and enjoy the food and drinks! 🍫🍹 📅 Date & Time: Wednesday, June 12th, 2024 from 18:30 to 20:00 📍 Location: TUM Main Campus - Arcisstraße 21, Room 2760 👉 Sign up link: in the comments 🎧 Listen to our related „May I AI?!“ podcast episode to dive into the topic even before the workshop! 🌟Secure your spot now and don’t miss out on this chance to learn about how to revolutionize medical AI! 🌟📊 #MedicalAI #GenAI #InnovationInHealth

📣 April 5th #frAIday Talk: "Towards Resilient AI in Healthcare" Can AI navigate the uncertainties of healthcare? Join us this #frAIday at 12:15 PM when Paolo Soda, a distinguished Professor at University Campus Bio-Medico di Roma will share intriguing insights on the capabilities of resilient AI in healthcare. In an era where healthcare demands personalized solutions, the role of AI becomes increasingly vital. This talk will delve into the complexities of medical imaging, clinical data analysis, and explore the potential of multimodal learning and generative AI to shape a future where every individual receives optimal care, precisely when and how they need it. This talk is open to everyone via Zoom! Register for #frAIday to receive the link: https://lnkd.in/dhvifgk3 #AIresearch #TechTalk #AI #Seminar #MachineLearning #HealthTech #TechTalk #TransdisciplinaryAI #TAIGA #MedicalImaging #DataAnalysis #ResilientAI #DataAnalysis Henry Lopez-Vega Anna Lundberg

🎉 Episode 21 of The Neural Medwork is out! In this edition, we explore: 🧠 Core Concept: Diffusion Models - Understanding how these powerful AI models transform noise into meaningful data, with real applications in medical imaging, drug discovery, and EHR synthesis. 📑 Research Paper: A fascinating study on developing a trust-based framework for medical AI integration, highlighting 8 crucial components for successful implementation. 💡 Tips & Tricks: A practical guide on sentiment classification using few-shot learning - perfect for analyzing patient feedback and monitoring healthcare trends. 🆕 We've made it even easier to digest our content! Using NotebookLM, we've condensed the information into a 10-minute podcast format you can enjoy on the go (see Spotify Link in the newsletter). Please let us know if you'd like to see our previous editions converted to podcast format too! 🇨🇦 Stay tuned for upcoming episodes featuring incredible Canadian AI initiatives - we've got some amazing guests lined up to share their groundbreaking work. https://lnkd.in/gAedhQZ9 Michael Zhou #HealthcareAI #MedicalEducation #ArtificialIntelligence #HealthTech #MedicalAI #DigitalHealth #Healthcare

🌟 Day 23 of #100DaysOfLearning Challenge! 🎯 Today's Highlights: 1️⃣ LeetCode Practice:   - Solved 10 new algorithm problems! 🧩 2️⃣ Data Structure Review:   - Focused on the Array and hashing 🧠    3️⃣ Exciting Find: Google’s Latest Blog on Generative AI in Medical Imaging! 💡 Google published a groundbreaking blog today: Using generative AI to investigate medical imagery models and datasets. Here are the key takeaways: - Explainability in AI Healthcare: Emphasizes the need for explainable AI to ensure responsible use in healthcare, addressing the limitations of traditional saliency-based approaches.    - Generative Models for Visual Explanations: Proposes using generative models to visualize specific visual changes driving ML decisions, revealing biases and novel insights not evident through quantitative metrics. - StylEx Method: Employed in a study published in *The Lancet eBioMedicine*, tested across three imaging modalities (external eye photos, fundus photos, and chest X-rays) and eight prediction tasks. - Framework Stages: Operates through four stages—classifier training, StylEx model training, automatic attribute selection, and expert panel review. - Key Findings:  - Positive Controls: Identified known attributes like cataract spokes and retinal vein dilation, validating the framework.  - Novel Signals: Discovered new associations, such as eyelid margin pallor linked to elevated HbA1c levels, suggesting further research avenues.  - Confounders: Uncovered biases, such as eyeliner thickness correlating with lower hemoglobin levels, highlighting socio-cultural factors in model interpretation. - Interdisciplinary Collaboration: Introduces the Interdisciplinary Expert Panel to Advance Equitable Explainable AI, stressing the need for collaboration to interpret results rigorously. - Future Research: Advocates for continued research and collaboration between ML researchers, clinicians, and social scientists to leverage AI responsibly in healthcare, improve diagnostics, and address biases. This blog showcases the potential of generative models to enhance explainability in medical imaging ML models. Looking forward to exploring these insights further! 🔗 Links in Comments #AI #MachineLearning #LeetCode #TechInnovation #HealthcareAI #GenerativeAI

Google Research recently published a study in The Lancet eBioMedicine on using generative AI to enhance the explainability of medical imaging models. By employing the StylEx method, the team visualized and analyzed key attributes influencing machine learning decisions in medical imagery, uncovering both known and new insights. This interdisciplinary framework identifies biases and suggests further research opportunities in healthcare AI. The study's method involved training classifiers, using a StyleGAN-based generator, automatically selecting influential visual attributes, and reviewing these attributes with an expert panel. Read more about this approach to advancing explainable AI in medical imaging. #AI #Healthcare #ExplainableAI #MedicalImaging https://lnkd.in/eQF-D95H

Sign up for our upcoming seminar on "Challenges and Strategies for the Implementation of AI in Healthcare," one of the most important sectors as technology rapidly advances.

🔬 **New Research Alert: The Pivotal Role of AI in Medical Diagnosis and Treatment Prediction!** 🤖 I'm excited to share our latest research, which underscores the transformative potential of artificial intelligence in addressing complex medical challenges. Here are some key highlights: - AI in Action: Our study demonstrates how AI, particularly machine learning models like the random forest, can significantly enhance medical diagnosis and treatment predictions with an impressive accuracy of 85.19%. - Innovative Use of GANs: We've explored Generative Adversarial Networks (GANs) for data augmentation and synthesis, showing how GANs can improve classifiers such as MLP and AdaBoost, although they present challenges for decision tree and KNeighbors models. - Addressing Data Scarcity: By leveraging fully synthetic data, we offer a promising solution to the often limited availability of medical data. - Key Insights: Our feature importance analysis highlights the impact of treatment frequency on patient outcomes, making our models more interpretable and actionable. This research addresses current challenges in AI-driven medical applications. It introduces novel GAN-based approaches for more effective data augmentation and synthesis, aiming to improve patient care through better treatment outcome predictions. #AI #MachineLearning #Healthcare #MedicalResearch #GenerativeAdversarialNetworks #DataScience #Innovation #HealthcareAI #ResearchBreakthroughs

👉🏼 Brief Review and Primer of Key Terminology for Artificial Intelligence and Machine Learning in Hypertension 🤓 Patrick Dunn 👇🏻 https://lnkd.in/esyMhjua 🔍 Focus on data insights: - Recent breakthroughs in AI have revolutionized healthcare. - AI can process information like a human and outperform in repetitive tasks. - Machine learning utilizes neural networks and deep learning from structured or unstructured data sets. 💡 Main outcomes and implications: - AI can enhance hypertension diagnosis and treatment through remote patient monitoring. - Use of generative AI to communicate with patients and healthcare professionals. - Potential for digital therapeutics in hypertension management. 📚 Field significance: - Advancements in AI and machine learning are transforming healthcare practices. - Improved patient care and treatment outcomes through AI technologies. - Integration of AI in hypertension management shows promising results. 🗄️: [#ArtificialIntelligence #MachineLearning #Hypertension #Healthcare #DataInsights]

This study evaluates the performance of multimodal AI models in medical diagnostics using the NEJM Image Challenge dataset, comparing their accuracy to human collective intelligence. 1️⃣ Anthropic's Claude 3 models showed the highest accuracy, surpassing average human performance by about 10%. 2️⃣ Human collective intelligence achieved a 90.8% accuracy rate, outperforming all AI models. 3️⃣ GPT-4 Vision Preview was selective, often responding to easier questions with smaller images and longer texts. 4️⃣ OpenAI’s GPT-4 Vision Preview answered only 76% of the cases, while other models responded to all queries. 5️⃣ The study highlights the potential and current limitations of multimodal AI in clinical diagnostics. 6️⃣ Ethical and reliability concerns arise from the integration of multimodal AI in medical diagnostics. 7️⃣ The EU AI Act emphasizes the need for transparency, robustness, and human oversight in high-risk AI systems, including medical AI.