Viltis’ Post

🚀 Exciting advancements in clinical trials! A new study from Mass General Brigham shows that generative AI can significantly accelerate clinical trial enrollment for just pennies per patient. By using AI to screen electronic medical records, the accuracy and efficiency of patient identification are greatly enhanced, reducing time and costs. However, careful monitoring is essential to mitigate potential biases. This innovation holds promise for improving trial workflows and patient outcomes. Read more about this breakthrough here: https://lnkd.in/gfD3gha6 #Biotech #AI #ClinicalTrials #HealthcareInnovation #MedicalResearch

Join Graticule for an xtalks presentation on March 14 @ 1 PM EST The AI Revolution in Clinical Research: Next-Gen Precision Patient Matching Dan Housman, CTO of Graticule, will be joined by Deep 6 AI and Ochsner Health to discuss how the convergence of artificial intelligence (AI) and electronic medical record (EMR) data has brought unprecedented precision and speed to finding patients for clinical trials, real-world evidence studies and even treatments in a clinical setting. In this webinar, we explore innovative ways in which life sciences companies are using AI to find patients more precisely, prioritize patients for research based on therapy-specific criteria and collaborate with sites to quickly access EMR data. Register here: https://lnkd.in/gkSdQtP6 #RWE #AI #emr

Missed it? Catch a recording of our popular xtalks webinar from March 14: Graticule and Deep 6 AI on: The AI Revolution in Clinical Research: Next-Gen Precision Patient Matching Register to watch free): https://lnkd.in/gkSdQtP6 Dan Housman, CTO of Graticule, Deep 6 AI and Ochsner Health discuss how the convergence of artificial intelligence (AI) and electronic medical record (EMR) data has brought unprecedented precision and speed to finding patients for clinical trials, real-world evidence studies and even treatments in a clinical setting.

Large Language Models (LLMs) like Med-PaLM, MEDITRON-70B, and Med-Gemini hold significant potential to enhance clinical decision support, diagnostics, and personalised health monitoring. As an example, Med-PaLM 2 achieved an impressive 86.5% accuracy on the MedQA dataset, surpassing previous models and even untrained doctors in some cases. These AI models could assist in complex diagnoses, generate detailed medical summaries, and support clinical decision-making through tools like Aeyeconsult and RAG-based chatbots. However, challenges such as bias, hallucinations, and inconsistent performance must be addressed. As a result, no LLMs have yet received approval from major regulatory bodies like the FDA, MHRA, or EMA, highlighting the need for robust safety protocols and comprehensive regulatory frameworks. If you are interested in this topic, you will find the following research paper a great read: https://lnkd.in/emjTqzXV' #HealthTech #AIinHealthcare #MedicalInnovation #ClinicalDecisionSupport #LLMs #FutureOfMedicine #DigitalHealth #HealthcareAI #AIRegulation #MedicalAI

AI is one of the most promising technologies humans have ever developed. Yet we still need to be cautious around how we use it, especially as we apply it to healthcare. A recent study by researchers in some of the world’s most prestigious universities revealed that 43% of the AI tools so far approved by the FDA have not been rigorously validated using real patient data, raising questions about their safety and effectiveness. The study analyzed more than 500 FDA approved medical devices, and found that 226 of them lacked publicly reported clinical validation data. Even more concerning, only 22 were validated through randomized control trials, the golden standard of clinical research, and some AI tools even used images that were computer generated rather than real life patient data. To unleash AI’s potential, we need our regulating agencies to create clear standards around the approval of AI tools, and to conduct studies that accurately assess their effectiveness. The responsible use of AI is essential to building trust and enhancing patient outcomes. Failing to do so could jeopardize the very progress we aim to achieve. #ArtificialIntelligence #ClinicalResearch #FDA #PatientSafety #AI #DigitalHealth #HealthcareTechnology #MedTech #HealthCare https://lnkd.in/g32Scqi8 

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👀A fascinating dive into the world of AI in healthcare. Highlighting AI's use in speeding up diagnoses and enhancing drug development. while also delving into the ethical and safety concerns surrounding AI's integration into healthcare. 👉 Read more about the potential and pitfalls of AI in healthcare with Healthcare Leader: https://lnkd.in/eu5KqFNx #HealthcareInnovation #AIinHealthcare #DigitalHealth

Webinar next week: Join Graticule for an xtalks co-presentation on Thursday, March 14 @ 1 PM EST The AI Revolution in Clinical Research: Next-Gen Precision Patient Matching Dan Housman, CTO of Graticule, will be joined by Deep 6 AI and Ochsner Health to discuss how the convergence of artificial intelligence (AI) and electronic medical record (EMR) data has brought unprecedented precision and speed to finding patients for clinical trials, real-world evidence studies and even treatments in a clinical setting. In this webinar, we explore innovative ways in which life sciences companies are using AI to find patients more precisely, prioritize patients for research based on therapy-specific criteria and collaborate with sites to quickly access EHR data. Register here: https://lnkd.in/gkSdQtP6

AI in Healthcare: Predicting Patient Outcomes with Remarkable Accuracy 💊🧠 Researchers at Ohio State University have developed an AI model that can accurately predict treatment outcomes using a massive dataset of patient data. This breakthrough could revolutionize healthcare by: - Personalized Medicine: Tailoring treatment plans to individual patients based on their unique medical history. - Improved Decision-Making: Providing doctors with valuable insights to optimize patient care. - Reduced Costs: Potentially leading to more efficient and cost-effective healthcare delivery. This is a major advancement in the field of AI and its potential impact on healthcare is truly exciting! #AI #Healthcare #MachineLearning #MedicalResearch

Artificial intelligence in the clinical laboratory has great promise, but still relies on the human touch to keep diagnoses on track. What are your thoughts on harnessing AI for clinical diagnostics? Read here: https://buff.ly/3tJlkSp #ai #artificialintelligence #clinicallabs #clinicallaboratory #diagnostics