EASE member Jeremy Y. Ng , who will be speaking at the Autumn Symposium on Tuesday, has just published this article in The Lancet Digital Health to better understand medical researchers' attitudes and perceptions towards the use of AI chatbots in the scientific process. This international survey investigates the extent to which researchers are familiar with AI chatbots, their perceptions of the potential benefits and limitations of their use in scientific research, and the factors that might influence their adoption. A great prelude to Jeremy’s session at our Symposium next week. https://lnkd.in/d3KNCasr
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🔑 RAG AI in Medical Research: Ensuring Accurate Data for Life-Saving Applications In the critical field of medical research, Retrieval-Augmented Generation (RAG) AI is revolutionizing how researchers access and process vast datasets. By combining advanced retrieval techniques with generative AI, RAG ensures accurate, reliable data for breakthroughs in healthcare. ✅ Key Stats: +RAG AI can process and retrieve 40% more accurate data compared to traditional methods. +Studies show it reduces research time by up to 60%, accelerating discoveries. +Improved data reliability enhances the success rate of clinical trials and life-saving treatments. 💡 Read the full article to see how RAG AI is transforming medical research and driving innovation in healthcare: https://buff.ly/4fKK2Ew #RAGAI #MedicalAI #HealthcareInnovation #AIDrivenResearch #LifeSavingTech
RAG AI in Medical Research: Enhancing Accuracy in Life-Saving Data
thinkingstack.ai
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Generative Artificial Intelligence (AI) wrote more empathetic notes to patients than their doctors did, according to a study comparing the writing qualities of both. AI scored higher than humans on empathy, understandability and positive tone. The study by NYU Grossman School of Medicine asked primary care physicians to rate AI and human draft messages on several criteria, including informational content, completeness, empathy and whether the judges would send or rewrite the draft. Healthcare systems are looking to generative AI to help clinicians handle the growing volume of patient EHR messages. https://lnkd.in/ezEvXaXx I #GenerativeAI #HealthTech #ClinicianBurden #HealthcareInnovation Fierce Healthcare
AI-generated messages to patients on par with clinicians, and can even be more empathetic, study finds
fiercehealthcare.com
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LLM shortfalls described well here in the case of medicine. Supplementary techniques, like RAG, etc need to be used to validate results, and create trustworthy answers. A medical example is well illustrated in this article. #AI #LLMs #AI4Good #MedicalAI #AIinMedicine https://lnkd.in/gQJFuTUf
LLMs Fall Short in Medical Accuracy Compared to Human Experts
hitconsultant.net
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One of the major hurdles to AI adoption in healthcare is the phenomenon of AI hallucination, which occurs when a model generates false or misleading information. To mitigate these risks, the research team set out to develop a hallucination detection framework that could be applied to LLMs tasked with generating medical summaries. Researchers from the University of Massachusetts Amherst and healthcare AI company Mendel have published a framework for hallucination detection in AI-generated medical summaries. #AI https://lnkd.in/g-U7wCwb
Framework to help detect healthcare AI hallucinations | TechTarget
techtarget.com
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Artificial intelligence (AI) is no longer the future; it is the present. It’s reshaping landscapes, altering industries, and transforming how we live and work. With its rapid advancement, AI is causing disruption—for better or worse—in every field imaginable. While it promises efficiency and growth, it also brings challenges and uncertainties that professionals and businesses must navigate. What can one do to pivot if AI is disrupting their industry? Cynthia Corsetti interviews Jan Herzhoff, President of Elsevier Health, for Authority Magazine. #AI #Health #GAI #Healthcare #Elsevier
Jan Herzhoff Of Elsevier Health: How AI Is Disrupting Our Industry, and What We Can Do About It
medium.com
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Artificial intelligence (AI) is no longer the future; it is the present. It’s reshaping landscapes, altering industries, and transforming how we live and work. With its rapid advancement, AI is causing disruption—for better or worse—in every field imaginable. While it promises efficiency and growth, it also brings challenges and uncertainties that professionals and businesses must navigate. What can one do to pivot if AI is disrupting their industry? Cynthia Corsetti interviews Jan Herzhoff, President of Elsevier Health, for Authority Magazine. #AI #Health #GAI #Healthcare #Elsevier
Jan Herzhoff Of Elsevier Health: How AI Is Disrupting Our Industry, and What We Can Do About It
medium.com
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Artificial intelligence (AI) is no longer the future; it is the present. It’s reshaping landscapes, altering industries, and transforming how we live and work. With its rapid advancement, AI is causing disruption—for better or worse—in every field imaginable. While it promises efficiency and growth, it also brings challenges and uncertainties that professionals and businesses must navigate. What can one do to pivot if AI is disrupting their industry? Cynthia Corsetti interviews Jan Herzhoff, President of Elsevier Health, for Authority Magazine. #AI #Health #GAI #Healthcare #Elsevier
Jan Herzhoff Of Elsevier Health: How AI Is Disrupting Our Industry, and What We Can Do About It
medium.com
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Artificial intelligence (AI) is no longer the future; it is the present. It’s reshaping landscapes, altering industries, and transforming how we live and work. With its rapid advancement, AI is causing disruption—for better or worse—in every field imaginable. While it promises efficiency and growth, it also brings challenges and uncertainties that professionals and businesses must navigate. What can one do to pivot if AI is disrupting their industry? Cynthia Corsetti interviews Jan Herzhoff, President of Elsevier Health, for Authority Magazine. #AI #Health #GAI #Healthcare #Elsevier
Jan Herzhoff Of Elsevier Health: How AI Is Disrupting Our Industry, and What We Can Do About It
medium.com
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A recent study published on arXiv reveals that OpenAI's o1-preview model significantly outperforms human doctors in medical reasoning tasks. The findings show that while doctors achieved only a 30% correct diagnosis rate, the AI model reached an impressive 80% accuracy. This research, conducted by teams from Harvard Medical School and Stanford University, evaluated o1-preview’s performance through five rigorous experiments, including differential diagnosis and management reasoning. Traditional evaluations of large language models (LLMs) often rely on multiple-choice questions, which do not effectively simulate real clinical scenarios. In contrast, the o1-preview model was assessed based on its ability to synthesize clinical data and provide accurate diagnoses, showcasing its potential in real-world applications. The model excelled particularly in generating differential diagnoses and demonstrated high-quality diagnostic reasoning. Despite its strengths, the study noted that o1-preview did not show significant improvements in probabilistic reasoning tasks. Nevertheless, this advancement is crucial as it opens up new avenues for accessible medical care. With AI providing high-quality diagnostic advice that is always available, it could alleviate some challenges faced by individuals seeking medical attention. Overall, this study highlights the potential of AI in enhancing clinical decision-making and improving healthcare accessibility while emphasizing the need for ongoing research to refine these technologies further. 🔗 Read more about the study here: https://lnkd.in/e9HD2j3y
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This ZDNet article discusses how GenAI has improved at answering medical questions through a technique called "retrieval-augmented generation" (RAG). This method involves large language models seeking external input while generating outputs to enhance their capabilities. RAG has shown significant performance improvements in answering medical questions, surpassing standard language models. The article further highlights the importance of RAG in the safe deployment of GenAI in clinical settings, emphasizing its role in amplifying the neural network's abilities without the need for constant re-training. Additionally, a new set of medical questions called ClinicalQA was compiled to test GenAI's performance across various medical specialties, aiming to bridge the gap between AI and medical knowledge effectively. https://lnkd.in/ecD7Nmbv #healthcare #healthcareai #healthcareit #genai #rag #bigdata #llm
How GenAI got much better at medical questions - thanks to RAG
zdnet.com
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See details of the Symposium, including programme and registration details on the EASE site https://ease.org.uk/ease-events/autumn-symposium/autumn-symposium-2024-programme/