I used to spend 2-3 hours daily on clinic notes, skeptical of AI scribe technology. However, I've been impressed with RevMaxx, a generative AI company transforming clinical note creation by listening to physician-patient conversations and automatically generating comprehensive notes. This platform accurately captures crucial ICD-10 codes, saving valuable time ⏰. 👉 Why This Matters: - **Reducing Physician Burnout:** Administrative tasks are a significant contributor to physician burnout. Innovations like RevMaxx alleviate this burden effectively. #HealthcareInnovation #AI #PhysicianBurnout #MedicalDocumentation #HealthTech #RevMaxx
Kishlay Anand MD MS’ Post
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𝐆𝐏𝐓-𝟒’𝐬 𝐩𝐨𝐭𝐞𝐧𝐭𝐢𝐚𝐥 𝐢𝐧 𝐬𝐡𝐚𝐩𝐢𝐧𝐠 𝐭𝐡𝐞 𝐟𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐫𝐚𝐝𝐢𝐨𝐥𝐨𝐠𝐲. 🤯 In recent years, AI has been increasingly integrated into healthcare, bringing about new areas of focus and priority, such as diagnostics, treatment planning, patient engagement. While AI’s contribution in certain fields like image analysis and drug interaction is widely recognized, its potential in natural language tasks with these newer areas presents an intriguing research opportunity. Beyond radiology, GPT-4’s potential extends to translating medical reports into more empathetic and understandable formats for patients and other health professionals. This innovation could revolutionize patient engagement and education, making it easier for them and their carers to actively participate in their healthcare. Read complete blog post➡️https://lnkd.in/dcMnFTJa Follow AI Toolhouse - AI Tools Catalogue for more 🥳 #gpts #gpt4 #gptstore #aitools #ai #ml #coding #machinelearning #generatieveai
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📌 Breakthrough in Medical AI: MedVersa Model 1. Rapid Advancements: Medical AI is transforming diagnosis accuracy and patient care. 2. Current Limitations: Existing AI systems are often limited to narrow applications. 3. Introducing MedVersa: Developed by Harvard, JIPMER, and Scripps, this versatile AI model aims for flexible learning and task handling. 4. Core Innovation: Uses large language models as coordinators, integrating multimodal inputs through language and vision modules. 5. Overcoming Limitations: Combines visual and language supervision, supporting on-the-fly task specifications via language. 6. Exceptional Performance: Excels in tasks like generating radiology reports, visual question answering, anatomical detection, and image segmentation. 7. MedInterp Dataset: A diverse multimodal dataset created for comprehensive medical image interpretation. 8. Superior Results: Outperforms state-of-the-art models in nine tasks on the MedInterp dataset. 9. Radiology Reporting: Surpasses Microsoft’s MAIRA-1 21 and Google’s Med-PaLM M 13. 10. Visual Localization: Outperforms a mature object detector and excels in longitudinal studies, region of interest description, open visual QA, and chest pathology classification. #MedAI #MedVersa #HealthcareInnovation #AI #MedicalResearch #Harvard #Scripps
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👉🏼 The role of large language models in medical image processing: a narrative review 🤓 Dianzhe Tian 👇🏻 🔍 Focus on data insights: - AI, driven by LLMs, has revolutionized medical image processing. - LLMs streamline the interpretation process, traditionally characterized by manual efforts. - LLMs offer immense potential for enhancing various aspects of medical image processing. - The Transformer architecture, foundational to LLMs, is gaining prominence in this domain. 💡 Main outcomes and implications: - AI, especially LLMs, plays a pivotal role in advancing medical image processing. - LLMs enhance transfer learning efficiency and integrate multimodal data. - LLMs facilitate clinical interactivity and optimize cost-efficiency in healthcare. - Potential applications of LLMs in clinical settings have promising implications for research, practice, and policy. 📚 Field significance: - AI, particularly LLMs, has transformative potential in improving healthcare. - The continued development and implementation of AI in medical image processing will reshape the healthcare landscape for the better. 🔗: [Link to the article](https://lnkd.in/emFT29SC) 🗄️: [#AI #medicalimageprocessing #LLMs #healthcare #research]
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Last week Google released a new paper on X-ray AI system through alignment of large language models (#LLMs) and radiology vision encoders. ✅ ELIXR is short for Embeddings for Language/Image-aligned X-Rays ✅ It enables efficient training of multimodal models using routinely collected medical images and their associated text reports, and adds the ability to perform a diverse range of tasks with rich expressive outputs ✅ This approach unlocks the potential for a new generation of medical AI applications, supporting workflows including high performance zero-shot and data-efficient classification, semantic search, visual question answering (VQA), and radiology report quality assurance (QA)🚀 ✅ ELIXR has achieved state-of-the-art performance on zero-shot chest X-ray classification🔥🔥 Would be very keen to see more development on top of #EXILR in two directions: 💭Having this in API which can be ultimately given to the doctors as a real 'AI-enabled' assistant. 💭Extrapolating this work to other medical diagnosis - Heartbeat palpations is something which would be on top of my list. Kudos to Andrew Sellergren, Krish Eswaran and the whole team behind this really interesting work. Read the full paper here: https://lnkd.in/gf7ZBuDb #AI #medicaladvancements #innovation #artificialintelligence #generatieveai
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The catch? "Prompt engineering" is the overlooked wildcard. Ordinary users, and yes, even medical professionals, may be clueless about crafting prompts that optimize accuracy. Should doctors loose on generative AI without a clue?. It’s a call for a reality check. AI in healthcare demands awareness. Dr. Eliot's article pushes to Navigate the AI minefield cautiously, or risk healthcare stumbling into uncharted—and potentially perilous—territory. #AIinHealthcare #MedicalSummarization #GenerativeAI #HealthTechDebate #PromptEngineering #AIControversy #HealthcareInnovation #MedicalAIrisks #TechEthics #AIinMedicine
Doctors Relying On Generative AI To Summarize Medical Notes Might Unknowingly Be Taking Big Risks
forbes.com
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Sometimes, a visit to the doctor for an X-ray is a necessity. Apart from having to endure the long queue, when you are in pain, the time until your results arrive can seem endless. Let’s take a look at how #AI can be integrated into #medical facilities to automate medical imaging for better #screening and faster results. See our new article about the AI application in #Diagnostics using technologies like #computervision, data augmentation, and #imagegeneration! Contact TechnoLynx for tailor-made AI solutions, designed specifically for your business needs!
The Synergy of AI: Screening & Diagnostics on Steroids!
technolynx.com
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Unwrapping the Power of Ambient AI: Inventing Clinical Documentation in Healthcare 🏥 AI alters healthcare by delivering ambient clinical documentation, lowering administrative loads, and improving patient care. According to a recent AMA survey, 54% of physicians are enthusiastic about AI's ability to streamline paperwork procedures. Ambient AI systems transcribe patient talks in real time without using keyboards, reducing time and boosting clinician-patient relations. However, difficulties persist in effectively recording clinical facts and coding notes. Future developments in ambient AI rely on turning language into structured, codified clinical data while considering patient context. This will allow for more advanced clinical applications while supporting reimbursement and community health programs. Healthcare industry leaders are propelling ambient AI forward with extensive clinical terminology and advanced natural language processing models, providing precise mapping to billing codes and complete diagnosis information. #AmbientAI #ClinicalDocumentation #HealthcareInnovation #ArtificialIntelligence #MedicalTechnology #DigitalTransformation #HealthTechRevolution
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Transforming healthcare with AI The impact on the workforce and organizations AI in healthcare leverages technologies like machine learning, NLP, and robotics to enhance patient care, diagnostics, and administrative tasks. Its applications range from robot-assisted surgeries to virtual nursing assistants and predictive analytics. AI improves diagnostic accuracy, personalizes treatments, and supports remote patient monitoring. Ethical considerations, such as data privacy and algorithmic bias, are crucial. The future of AI in healthcare includes advancements in personalized medicine, mental health interventions, and collaborative efforts to drive innovation. Read more: https://lnkd.in/dwwE3Fwh #rapidinnovation #AIinHealthcare #HealthTech #MedicalInnovation #AIApplications #FutureOfHealthcare
Transforming healthcare with AI The impact on the workforce and organizations
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I’m thrilled about the potential of AI in revolutionizing healthcare. 🚀 I recently came across a thought-provoking article published in Nature, titled “Large language models encode clinical knowledge,” The article by the Google team highlights the challenges involved in providing high-quality answers to medical questions. It emphasizes that medical AI requires a profound understanding of medical context, the ability to recall appropriate medical knowledge, and the skill to reason with expert information. With the advent of foundation models and Large Language Models (LLMs), there’s a compelling opportunity to reimagine the development of medical AI. This advancement could lead to a more accessible, safer, and equitable usage of AI in healthcare. However, applying LLMs to medicine is complex due to the intricate nature of the field. The research showcased in the article offers a glimpse into both the exciting possibilities and the challenges of applying these cutting-edge technologies to healthcare. #AIinHealthcare #MedicalAI #NatureArticle #HealthcareInnovation
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💡Have you ever wondered what Assistive AI and Generative AI are in healthcare? In this post, we’ll explain the key differences between the two AI models. Assistive AI refers to an AI-based technology designed to assist healthcare professionals in various tasks related to patient care, diagnosis, treatment, and administrative processes. Generative AI, on the other hand, focuses on generating new content relevant to medical research, patient care, and education. #healthcare #AI #GenerativeAI #AssistiveAI #futureofmedicine #artificialintelligence #machinelearning #AIinhealthcare #GenerativeAI #AssistiveAI
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