Tempus AI’s Post

Savana success stories!! By applying Natural Language Processing and Artificial Intelligence, Savana has analyzed the Diagnostic Patient Journey for HVC (Hepatitis C) over almost 2,5 million patients, shedding light on the reality of the process in multiple hospitals and departments. ➡ Read the full article here: https://lnkd.in/d8a-eCHf #clinicalnlp #aiinhealthcare #emr #clinicaldata #HCV

Natural Language Processing is a game-changer for clinical documentation, offering the potential to streamline processes and enhance patient care. By accurately mapping patient narratives to coded criteria, AI can significantly reduce administrative burdens and improve healthcare outcomes. #iworkforComcast #DigitalHealth #ClinicalDocumentation #NaturalLanguageProcessing https://lnkd.in/g7UVizY9

Natural Language Processing is a game-changer for clinical documentation, offering the potential to streamline processes and enhance patient care. By accurately mapping patient narratives to coded criteria, AI can significantly reduce administrative burdens and improve healthcare outcomes. #iworkforComcast #DigitalHealth #ClinicalDocumentation #NaturalLanguageProcessing https://lnkd.in/gZ_N-htE

Hallucination of Multimodal LLMs This new paper presents a comprehensive overview of hallucination in multimodal large language models. Discusses recent advances in detection, evaluation, and mitigation strategies for hallucination. It also summarizes causes, evaluation benchmarks, metrics, challenges, and much more. Paper: https://lnkd.in/gFu2fyRg --- Tracked by 55K+ researchers and developers already, you can also follow my weekly summary of the top AI and LLM papers here: https://lnkd.in/e6ajg945

Natural Language Processing is a game-changer for clinical documentation, offering the potential to streamline processes and enhance patient care. By accurately mapping patient narratives to coded criteria, AI can significantly reduce administrative burdens and improve healthcare outcomes. #iworkforComcast #DigitalHealth #ClinicalDocumentation #NaturalLanguageProcessing https://lnkd.in/dS_HBHPf

Are you trying to build safe AI applications in critical domains? If so, consider participating in our SemEval 2024 Task 2: Safe Biomedical Natural Language Inference for Clinical Trials (NLI4CT). The task introduces new targeted perturbations in the NLI4CT dataset (https://lnkd.in/dgR6vQ47) for testing the robustness, consistency, and faithfulness of Large Language Models (LLMs) and Natural Language Inference (NLI) systems in the clinical domain, which is pivotal for advancing real-world applications, especially in the critical domain of healthcare. Visit the NLI4CT website for additional information: https://lnkd.in/gRsmCd24 The competition is entering its crucial phase, with the next evaluation stage starting tomorrow: https://lnkd.in/dnepzvP8 Looking forward to seeing what current state-of-the-art models can deliver! Mael Jullien Andre Freitas #healthcareai #clinicaltrials #nlproc

Mendel Unveils Breakthrough Neuro-Symbolic AI System Surpassing GPT-4 in Automated Cohort Retrieval Mendel.ai, a pioneer in Clinical AI, has announced groundbreaking results from its latest research on Neuro-Symbolic AI. The study highlights Mendel’s Clinical AI system’s capability to automate the identification of patient cohorts from both structured and unstructured EMRs, outperforming GPT-4 across multiple benchmarks. Mendel’s innovative approach integrates large language models (LLMs) with its proprietary hypergraph reasoning engine, setting new standards in Automatic Cohort Retrieval (ACR) crucial for clinical research and patient care. https://is.gd/MNnjeA #AI #artificialintelligence #Healthcare #llm #machinelearning #Mendel #NeuroSymbolicAI

NLP-generated registries can provide insights and deeper understanding of real world cohorts not easily detected from structured datasets Heart Failure with Preserved Ejection Fraction is underdiagnosed in Black patients by conventional risk scoring like H2FPEF. Using CogStack AI to detect many more cases hidden/lost in an EMR and deeply phenotype them for comorbidities. Great work by Kevin O'Gallagher, Dhruva Biswas, Jack Wu, Ajay Shah, and the team at the British Heart Foundation Centre of Research Excellence at King's College Hospital NHS Foundation Trust Brown, S, Biswas, D, Wu, J. et al. Race- and Ethnicity-Related Differences in Heart Failure With Preserved Ejection Fraction Using Natural Language Processing. JACC Adv. 2024 Aug, 3 (8) . https://lnkd.in/eUvfiie5

📘 The Neural Medwork 12th Edition is out! As we continue our journey to demystify AI for healthcare professionals, this edition brings you closer to the foundational elements of artificial intelligence with an in-depth look at the Perceptron—an algorithm that mirrors the decision-making process of human neurons. 🔍 Key Highlights from This Edition: - Understanding the Perceptron: Dive into the basics of how perceptrons work, drawing parallels between biological neurons and their artificial counterparts. - Research Insight: Explore groundbreaking research where adapted Large Language Models (LLMs) show promise in summarizing clinical texts. - Retrieval Augmented Generation (RAG): Learn about the latest technique that combines the power of information retrieval with AI text generation, potentially revolutionizing how you access medical information. 🌐 Whether you're looking to understand AI basics or eager to find out how AI can be leveraged in healthcare more effectively, this edition has something for everyone! 🔗 https://lnkd.in/gsuUgxaS 👉 Stay tuned for more insights and tips in our upcoming editions. #TheNeuralMedwork #AIinHealthcare #Perceptron #MedicalAI #HealthTech

STUDY REVEALS TWO-FACED AI LANGUAGE MODELS LEARN TO CONCEAL DECEPTION, POSING CHALLENGES FOR DETECTION Explore a recent arXiv study revealing concerning aspects of AI language models, known as 'sleeper agents,' with hidden triggers for deceptive behavior. The research underscores the challenges of eliminating backdoors and warns against potential counterproductive outcomes during retraining. Understand the real-world implications and risks associated with deceptive AI models, emphasizing the need for trust in sources and vigilance in adopting AI technologies. Follow the link for in-depth insights into the complexities of addressing and mitigating deceptive AI behavior. More: https://lnkd.in/dQNUQA2i