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In the dynamic landscape of healthcare, the convergence of data, AI and collaborative efforts is reshaping the way we approach challenges and opportunities in academic medical research. Join experts from Indiana University, Manifold, and Snowflake to explore how large-scale multi-modal data in clinical research is creating both opportunities to improve patient outcomes and challenges for existing research infrastructure.

Ever wondered how AI could revolutionize research analysis in healthcare? Meet Discovery – our AI and data management platform specifically designed to enhance the workflows of physicians and researchers. We recently had the privilege of discussing the impact of Discovery with Prof. Marion Munk, M.D., Ph.D, who shared her invaluable experience with the platform. The verdict? Overwhelmingly positive! Professor Munk highlighted how Discovery offers unparalleled AI-driven insights into diseases, accelerating the process of analysis, making it more objective and significantly faster. One of the standout features of Discovery is its rapid volumetric AI analysis of OCT images paired with user-friendly progression graphs. These innovations provide deep insights into eye diseases, empowering healthcare professionals to enhance their clinical judgments and expedite their research findings. Are you ready to explore how Discovery can transform your research and enable you to make data-driven decisions? Dive into the future of research with us and revolutionize your approach to healthcare analysis. Discover more about our platform and its potential to empower your research ?? https://buff.ly/49lZBPu

The marvel of medical research breakthroughs is rapidly transforming our health. Each day, we unveil the invisible, from groundbreaking genetic studies and precision medicine to the revolutionary potential of AI in data analysis. More than ever, we need to appreciate the power and potential of medical research. Through clinical trials and epidemiological studies, we're improving patient outcomes and life quality. Genetic research is opening the door to personalized medicine, enhancing our understanding of inherited diseases. AI and machine learning are deciphering patterns in mountains of data, charting the future of medical data analysis. However, advancement must be tempered with ethics. Ensuring patient safety, maintaining privacy, and securing informed consent are non-negotiable. The journey from laboratory discovery to clinical application, or translational research, is an opportune space where promise of innovation becomes a reality for patients. Meanwhile, interdisciplinary collaboration is shattering traditional silos. By integrating diverse expertise, we're accelerating discovery and forging a holistic understanding of health and disease. As we navigate the vast ocean of medical research, let's celebrate its transformative power, appreciate the importance of translational research and embrace the potential of interdisciplinary collaboration. We are on the precipice of a healthcare revolution; let's stride towards a future where disease and illness are footnotes in the annals of human health. Uncover the full story at [Blog link] and join us in this exciting journey towards a healthier future. #MedicalResearch #GeneticStudies #PrecisionMedicine #AI #MachineLearning #PatientSafety #TranslationalResearch #InterdisciplinaryCollaboration #HealthcareRevolution Read more on our blog: https://lnkd.in/e84yQ-XC

?? Reminder: Abstract submission for the 2024 AMIA AI Evaluation Showcase Stage III is now extended to August 9! Submit your health impact studies and share your evaluation approaches. All are welcome to participate, even if you missed earlier stages. ?? Submit here: https://lnkd.in/gEZmnrkd Webinar recording: https://lnkd.in/gahUw7mK #Research #HealthAI #MedicalInformatics #AI

??Scientific paper: Detecting algorithmic bias in medical-AI models using trees Abstract: With the growing prevalence of machine learning and artificial intelligence-based medical decision support systems, it is equally important to ensure that these systems provide patient outcomes in a fair and equitable fashion. This paper presents an innovative framework for detecting areas of algorithmic bias in medical-AI decision support systems. Our approach efficiently identifies potential biases in medical-AI models, specifically in the context of sepsis prediction, by employing the Classification and Regression Trees (CART) algorithm. We verify our methodology by conducting a series of synthetic data experiments, showcasing its ability to estimate areas of bias in controlled settings precisely. The effectiveness of the concept is further validated by experiments using electronic medical records from Grady Memorial Hospital in Atlanta, Georgia. These tests demonstrate the practical implementation of our strategy in a clinical environment, where it can function as a vital instrument for guaranteeing fairness and equity in AI-based medical decisions. ;Comment: 26 pages, 9 figures Continued on ES/IODE ?? https://etcse.fr/9onq ------- If you find this interesting, feel free to follow, comment and share. We need your help to enhance our visibility, so that our platform continues to serve you.

If you seek innovative ideas on how AI can be practically applied in healthcare to enhance patient outcomes, I highly recommend attending this session. #ai #healthcare

There are new groundbreaking developments happening almost everyday with the the use of AI in different domains. Today, we are discussing something that can revolutionize the healthcare systems. ???? A deep learning model developed by researchers at Taiwan’s National Defense Medical Center has significantly reduced mortality rates among critically ill patients by predicting heart-risk and enabling timely medical intervention. This model analyzes patients' electrocardiogram (ECG) data to generate a risk score. Patients identified with high-risk scores receive immediate attention from medical professionals, resulting in a remarkable reduction in mortality among high-risk patients during clinical trials. Watch the full video here ???? where we delve into how this deep learning model works, the impressive clinical trial results, and the future implications for healthcare! https://lnkd.in/dtcJ-P9j

To successfully implement AI in healthcare, it is essential not only to support achieving the intended benefits—such as reducing uncertainty through enhanced precision and predictive capabilities—but also to address the new uncertainties that may arise as a result of introducing the technology. In this study, we explore these challenges in the context of AI used to predict the risk of mortality following discharge from the emergency department. Excellent work by Daniel Tyskbo.

In the evolving landscape of medical research, the focus on the accuracy and quality of systematic literature reviews (SLRs) has never been more critical. These reviews form the backbone of evidence-based decisions in healthcare, guiding everything from treatment protocols to policy formulation. However, a common challenge researchers face is the time-consuming and often subjective nature of manual risk of bias (RoB) assessments. This is where?RoB Master, our state-of-the-art AI-powered tool, steps in to transform the process. https://lnkd.in/gb_tyNaw

?? Exciting News! ?? I’m thrilled to share that my latest paper, titled?“Exploring the Potential Impact of Future GPT Models in Healthcare: A Speculative Analysis”, has been published! ??? You can read it here: https://lnkd.in/g5JnCewZ. Abstract:?The anticipated release of GPT-5 by OpenAI, expected in late 2024 or early 2025, is projected to bring significant advancements over its predecessor GPT-4. If GPT-5 achieves its expected capabilities, including enhanced reasoning, multimodal interactions, greater reliability, and improved personalization, it could significantly impact the healthcare sector. This review article explores the potential future impact of GPT-5 in healthcare, examining projected applications in diagnostics, personalized medicine, drug discovery, and clinical decision support. Ethical considerations and future trends are discussed, offering a speculative overview of GPT-5’s possible influence on healthcare. This paper delves into how these advancements could revolutionize healthcare decision-making and digital transformation, ultimately aiming to improve patient outcomes and operational efficiency. A special thank you to Dr. Raouf Hajji, MD, PhD. for inspiring this paper. His insightful LinkedIn post on the topic significantly influenced my paper and is duly acknowledged in the references. I also want to thank the International Journal of Global Innovations and Solutions (IJGIS) for publishing this paper and my coauthors Nandhakumar Raju and Shaik A Kareem for their valuable contributions. Looking forward to your thoughts and discussions on this important subject! #GPT5 #GPT4 #Healthcare #ArtificialIntelligence #DiagnosticAccuracy #MultimodalInteraction #PersonalizedTreatment #MedicalResearch #PatientCommunication #ClinicalDecisionSupport #Telehealth #EthicalAI #ResponsibleAI #BigData #PredictiveAnalytics #OpenAI #PatientCare