John Nicol

Syndeia 3.6 — Digital Thread Graph Queries, Playlists, and Visualizations — all backed by the most comprehensive digital engineering and integrations API. #digitalengineering #digitalthreads #graph #queries #visualization #analytics

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A lot of people are talking about FHIRCast. Check out the latest episode of SIIMcast to dig a little deeper. https://lnkd.in/g8U2pGpt #ImagingIT

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The text2vec-ollama module enables Weaviate to obtain vectors using Ollama. https://lnkd.in/ei6gUnyr

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Today I'm launching the latest weekend hackathon: https://trialsight.ai. It's an #AI agent that watches for the latest updates from the #FDA on #ClinicalTrials.gov. When it finds a trial that matches your personalized criteria, it answers your questions about the trial and delivers each update as a personalized newsletter to your inbox. It's completely free and requires no account registration. Unsubscribe at any time. It's the perfect way to stay on top of the latest clinical trials registered with the FDA whether you're a patient, #lifescience industry analyst, #healthcare worker, or #pharma executive. Check it out, try the preview, and join the waitlist!

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🎉 Exciting News: CARPL.ai Receives US-FDA Clearance for Enterprise Imaging AI Platform! 🏥 CARPL - Radiology AI Platform has received 510(k) clearance from the U.S. Food and Drug Administration (FDA) for cutting-edge enterprise imaging AI platform. This milestone reaffirms their commitment to safely transforming radiology with advanced AI and automation, streamlining clinical workflows, and improving patient outcomes. Key Highlights: 🏥 FDA Clearance: The U.S. FDA has cleared CARPL.ai's web-based PACS and radiology workflow management device, making them the only dedicated AI marketplace with such clearance. 🌟 Universal AI Viewer: The platform includes a universal AI viewer, allowing radiologists to utilize multiple FDA-cleared AI applications through a single user interface, integrated seamlessly with any major PACS vendor. 🌐 Global Impact: Trusted by leading healthcare institutions globally, including Massachusetts General Hospital, University Hospitals in Cleveland, I-MED Radiology in Australia, and the Singapore Government Healthcare System. 🛠️ Comprehensive AI Marketplace: Featuring over 110 AI applications from more than 50 vendors, providing a wide array of tools for healthcare professionals. 🔒 Secure Integration: Supports various use cases such as exam prioritization, report auto-population, clinical audits, abnormality detection, segmentation, quantification, and characterization. 💡 Zero Incremental Costs for AI Pilots: Facilitates rapid scaling of AI use cases and delivers faster time-to-value for healthcare providers. ☁️ Flexible Hosting Options: Can be hosted on-premise, on private cloud, or on any major cloud provider. 👥 Keeping the human in the loop is most critical since running AI safely is paramount in clinical situations. CARPL’s universal AI viewer enables this. Expert Endorsements: "By prioritizing a secure development framework from the very beginning, CARPL has achieved a critical milestone ensuring that patient safety remains at the forefront of AI deployment in radiology," said Vidur Mahajan, MD, MBA Mahajan, CEO at CARPL.ai. “CARPL.ai’s FDA clearance will now allow AI applications that have FDA approval but do not have their own FDA-cleared viewers to get integrated into PACS, enabling complex workflows such as accept/reject/modify AI outputs and turning AI outputs on and off,” said Dr. Vijay Rao, Professor of Radiology and Senior Vice President of Enterprise Imaging at Jefferson Healthcare. 🚀 This FDA clearance opens the door for widespread adoption of AI solutions through a platform approach which simplifies the process of selection, implementation, and procurement of AI by health systems. https://lnkd.in/g7BS-TGT #FDAApproval #AIinHealthcare #RadiologyAI #HealthcareInnovation #AIMarketplace #CARPLai #PatientSafety #ClinicalAI

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When the marketing team knows how to have fun 😂 🔥 . What it looks like in practice: ➡ Identifying *specific* groups who struggle with common depression treatments ➡ Distinguishing between *types* of depression for better care (we don't say all cancer is the same, why do chronic disease patients get lumped under one umbrella?) ➡ Clarifying the *spectrum* of depression for targeted therapies When you combine deep data with physician intuition and (6x!) patented AI tools, you can do some sci-fi level science. #JustSayin' #FunWithData #MemesAndMachineLearning

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Over 40 #healthcare and life sciences startups and enterprises are leveraging Meta Llama 3 NIM to develop and deploy applications that accelerate digital biology, digital surgery, and #digitalhealth 🧬 Learn more about use cases for NVIDIA NIM inference microservices in the blog >

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A group of current and former employees from OpenAI and Google DeepMind are advocating for protection from retaliation when voicing concerns about the "serious risks" associated with the technologies developed by these and other companies. In a public letter signed by 13 individuals, including seven who disclosed their identities, the employees emphasized the critical role they play in holding corporations accountable in the absence of effective government oversight. Recent controversies surrounding OpenAI, including the dissolution of a safety team and staff departures, have sparked discussions about safeguarding artificial intelligence. Concerns have been raised regarding confidentiality agreements that restrict employees from expressing their apprehensions, potentially hindering the resolution of crucial issues. Jacob Hilton, a former OpenAI employee and signatory of the letter, acknowledged the company's policy change regarding nondisparagement agreements but highlighted lingering fears of retaliation for disclosure. The employees underscored the financial motives driving leading AI companies to evade oversight and the limited obligations these companies have in disclosing the true risks of their AI systems to the public. In response to these concerns, a spokesperson for OpenAI expressed pride in the company's commitment to providing safe AI systems and emphasized the importance of engaging in robust discussions about the technology's implications. The employees stressed the necessity for enhanced protections, given the significant power wielded by AI companies and the potential consequences of insufficient transparency. The call for a "Right to Warn about Advanced Artificial Intelligence" reflects the employees' dedication to fostering accountability and transparency within the AI industry, underscoring the importance of empowering individuals to speak out without fear of reprisal. https://lnkd.in/darne4Wq #openAI #deepmind #LLMs #artificialinteligence

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This is the best analysis of the Epic and Particle CareEquality network news I've read. Moving forward, one critical next step for the vitality of HIEs is abundantly clear: The HIE networks must require exchange for non treatment use cases of payment and operations and establish a robust process to hold recipients accountable to their use case attestation. This will require the collaboration and support of major players, such as Epic. I'm optimistic the parties involved can be successful in this next step by uniting around a shared goal of better supporting patients regardless of use case. #HIE #CareEquality #Commonwell #Interoperability

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Article very worth the read on AI architectures (in the widest sense) and pros / cons of vertical integration from hardware through models to Apps. Recommend the podcast also. #AI #modularization #LLM

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My latest blog written jointly with Joaquim Gromicho and Kai Kaiser “An Open Data-Driven Approach to Optimising Healthcare Facility Locations Using Python,” has been featured as an Editor’s Pick on Towards Data Science! 🎉 This work leverages open-source data and tools to tackle the challenge of calculating physical access in regions with infrequent population censuses and outdated road and health facility data. The goal is to optimize new locations for strategic long-term investment and budgeting, which is the foundation of the open-source toolkit developed by Analytics for a Better World - Public Infrastructure Service Access. #DataScience #OpenSource #Healthcare #Python #PublicInfrastructure

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At NeuroSoph, we are committed to creating AI solutions that prioritize human needs and enhance the user experience. By employing human-centered design principles, we ensure that our AI technologies are not only innovative but also intuitive and accessible to all. Human-centered design allows us to deeply understand the challenges faced by people and develop solutions that truly address their needs such as finding information on website or applying for benefits. This approach involves iterative testing and feedback, ensuring that our AI systems are user-friendly, accessible, and meeting the needs of the public. We are proud to contribute to the government sector with AI solutions that make a positive impact. Special shout out to Executive Office of Technology Services and Security Secretary Jason Snyder for his continued leadership and support in driving AI innovation within the Commonwealth of Massachusetts. For more insights on how AI and human-centered design intersect, check out this informative article and video from Government Technology https://lnkd.in/eYG9g5Ee #AI #HumanCenteredDesign #SLG #NeuroSoph #Leadership #AIInnovation

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Have you seen Inside Out 2? The film serves as a powerful reminder to embrace our emotions. In a world of automation and advancements, it’s easy to overlook the importance of empathy. Learn how #EmotionAI can bring both together. https://bit.ly/3XB1e9r

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As AI/ML workloads see increased investment, accelerated workload infrastructure is becoming a more critical part of workload deployments. Check out our recently released survey spotlight to see how common this technology has become, which workloads are most likely to see the demand for this type of infrastructure, and what suppliers can do to meet these demands.

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𝗟𝗟𝗠 𝗔𝗴𝗲𝗻𝘁𝘀 - 𝗧𝗵𝗲 𝗡𝗲𝘅𝘁 𝗘𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗼𝗳 𝗟𝗟𝗠𝘀 LLM Agents, also known as Agent AI Workflows, represent the next major evolution in Large Language Models (LLMs) like ChatGPT. These agents significantly enhance the capability of LLMs to handle complex queries, manage private data efficiently, and reduce operational costs associated with using LLM technologies. 🔍 𝗖𝘂𝗿𝗿𝗲𝗻𝘁 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝘄𝗶𝘁𝗵 𝗟𝗟𝗠𝘀: While LLMs perform exceptionally well with public data, they face limitations when users attempt to integrate their own large-scale data, primarily due to the token constraints of models like GPT-4, which can handle a maximum of 8k tokens. Traditional methods such as Retrieval Augmented Generation (RAG) fall short, especially when the data necessary to answer questions doesn't directly match the query terms. A simple question like "Find me patients who are teenagers" is very hard to answer using current methods. 🔗 𝗛𝗼𝘄 𝗟𝗟𝗠 𝗔𝗴𝗲𝗻𝘁𝘀 𝗦𝗼𝗹𝘃𝗲 𝗧𝗵𝗲𝘀𝗲: LLM Agents are designed to overcome these constraints by inversely integrating "tools" that an LLM can leverage to fetch relevant data without the need for direct data embedding in the prompt. These tools allow LLMs to dynamically retrieve data relevant to the queries posed, enabling responses to complex questions that would typically not be directly referenced in the data. 🛠️ 𝗛𝗼𝘄 𝗟𝗟𝗠 𝗔𝗴𝗲𝗻𝘁𝘀 𝗪𝗼𝗿𝗸: LLM Agents employ a multi-step workflow where each query activates a specific tool based on the requirement of the question. This modular approach enables the LLM to process queries that involve multiple data points and sources, mimicking real-world problem-solving methods more closely than traditional single-step data retrieval. 💡𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲𝘀 𝗼𝗳 𝗟𝗟𝗠 𝗔𝗴𝗲𝗻𝘁𝘀: 1. 𝘋𝘢𝘵𝘢 𝘌𝘧𝘧𝘪𝘤𝘪𝘦𝘯𝘤𝘺: By minimizing the data directly processed by the LLM, costs and risks associated with private data are significantly reduced. 2. 𝘈𝘯𝘴𝘸𝘦𝘳 𝘊𝘰𝘮𝘱𝘭𝘦𝘹 𝘘𝘶𝘦𝘴𝘵𝘪𝘰𝘯𝘴: Agents can answer questions that do have the same words as the the content used to answer them. Agents can also a multi-step process to leverage multiple data sources. 3. 𝘐𝘮𝘱𝘳𝘰𝘷𝘦𝘥 𝘈𝘤𝘤𝘶𝘳𝘢𝘤𝘺 𝘢𝘯𝘥 𝘙𝘦𝘭𝘪𝘢𝘣𝘪𝘭𝘪𝘵𝘺: The ability to cross-reference and validate answers through multiple data points reduces the risk of inaccuracies and "hallucinations" common in traditional LLM outputs. Read this article to learn more about LLM Agents and how to start using them today... How are you providing your private data to the LLM today?

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🧠 An interesting article discussing the exciting advancements from Jie Gu team, Associate Professor at Northwestern University! His research is diving into AI-empowered neural processing for smarter human-machine interfaces and biomedical devices. Unlike typical wearables that rely on motion detection, AI is allowing them to decode neural signals like EEG, ECG, and EMG, giving deeper insights into our physiological and psychological activities. AI & neural processing has opened new doors in health monitoring and emotion detection. Imagine wristbands that track your gestures or EEG headcaps that let you control devices with your mind! AI algorithms are also making strides in biomedical applications, like real-time seizure detection and precise cardiac diagnoses. Prof. Gu's team is leading the charge with their cutting-edge AI chips, which are tiny and power-efficient. These innovations are transforming VR, MR, and biomedical devices, enabling real-time interaction and health monitoring. As AI continues to evolve, what future applications are you most excited about? 🤔💡 #AI #Healthcare #NeuralProcessing #Innovation

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Exploring the intricacies of implementing Retrieval Augmented Generation (RAG) use-cases in healthcare? Read my latest article that delves into the unique challenges and advanced solutions that are shaping the future of AI in healthcare front-office applications. #healthcare #ai #genai #rag

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