Sachin S R

Definitely sounds promising

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GPT-4 and OpenAI o1: Transforming Diagnosis and Patient Care in Healthcare AI continues to revolutionize healthcare, with OpenAI’s GPT-4 and the new o1 series leading the charge. Here’s how these cutting-edge language models are shaping the future of medical practice: 1. Enhanced Diagnostic Accuracy With improved capabilities, GPT-4 and o1 assist in making more accurate diagnoses by analyzing complex patient histories, medical jargon, and rare conditions. The o1-preview model excels in reasoning, providing precise diagnoses and transparent explanations for its conclusions. 2. Personalized Treatment Plans By integrating vast amounts of medical literature with patient data, these models help create personalized treatment plans, anticipate potential complications, and recommend tailored interventions based on individual risk factors. 3. Streamlined Clinical Documentation Both models significantly improve clinical coding, accurately mapping patient data to ICD-10 and SNOMED codes. The o1-preview model enhances the extraction of clinical insights, leading to more precise diagnoses and condition identification. 4. Ongoing Medical Education As educational tools, GPT-4 and o1 support continuous medical learning by summarizing complex research, explaining intricate medical concepts, and offering insights into evolving treatment strategies. 5. Data Extraction and Analysis The o1-preview model sets a new standard for extracting and analyzing clinical data, offering more nuanced clinical context than previous iterations, ultimately aiding in better patient care. While these advancements are groundbreaking, it’s essential to remember that AI models are tools to augment, not replace, human expertise. Healthcare professionals must remain critical of AI outputs and understand their limitations. As o1-preview continues to evolve, with features like web browsing and file uploads still in development, responsible integration is key. The future of AI in healthcare is exciting—but how do we ensure it enhances care while preserving the human touch? Join the conversation! How do you see AI impacting your practice? #HealthcareAI #GPT4InHealthcare #MedicalInnovation #AIInMedicine #OpenAIo1

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"Saudi Fund Backs China’s AI Ambitions: Zhipu AI" What Happened?: Saudi Aramco’s venture capital fund, Prosperity7 Ventures, has made a strategic move by joining a $400 million investment round for Zhipu AI, a Chinese generative AI startup. Significance: This marks the first instance of foreign investment in China’s quest to establish a homegrown rival to OpenAI, the renowned AI research organization backed by Microsoft. Zhipu AI’s Valuation: Zhipu AI is now valued at nearly $3 billion. Why Is This Important?: It reinforces Saudi Arabia’s intention to diversify AI ecosystems way from the dominance of the United States. US-China Tech Tensions: The US currently dominates the AI market, but geopolitical tensions have intensified competition over technology. US restrictions on the AI sector and tightened export controls on high-end chips used in AI models have prompted Chinese startups like Zhipu to seek support from foreign investors. China’s Drive for Innovation: Despite these tensions, collaboration between Saudi Arabia and China continues to flourish beyond AI. Both nations share mutual interests in innovation and economic development. Implications for the AI Landscape Worldwide -Global Competition: As China invests in homegrown AI capabilities, it will increasingly compete with established players like OpenAI. This competition drives innovation and pushes the boundaries of AI research. -Diverse Perspectives: Foreign investment in Chinese AI startups brings diverse perspectives, expertise, and resources. It fosters cross-cultural collaboration and knowledge exchange. -Technological Independence: China aims to reduce reliance on foreign technology and establish its own AI ecosystem. By backing Zhipu, Saudi Arabia contributes to this goal. Remember, the AI landscape is evolving rapidly, and strategic investments like this shape its future. #Globalization #BusinessImpact #MarketExpansion #CrossBorders #Innovation #AIRevolution #GlobalConnectivity #DigitalTransformation #FutureOfWork #DataDrivenDecisions #TechInnovation #AIResearch #EmergingMarkets #SupplyChainOptimization #TalentAcquisition #AIApplications #GlobalEconomy #StrategicInvestments #AICompetitiveness #SustainableGrowth

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Twitter plans to more deeply integrate Grok ’s AI 1. Grok analysis option on profiles: Grok’s profile analysis provides a summary overview of how long a profile has been active, how many followers it has, and what it regularly #posts about 2. Grok #analysis for specific words in any post: Get more context on #trending terms or people, with #automated rundowns based on Grok’s databanks 3. A separate Grok #window, which you could keep #active as you use the #app

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AI could destroy the healthcare system 💥 The healthcare AI market is growing Rapidly. Annual growth of 36.5% is forecast between 2024 and 2032, reaching an estimated $370.14 billion in 2032. Staggering figures! But how can we ensure ethical and socially appropriate integration in a still immature ecosystem? To avoid a hasty adoption of AI that could disrupt the healthcare system, the action-research approach seems to be the perfect solution. This is because, unlike conventional approaches, it actively intervenes to solve complex problems in real-world contexts, focusing on collaboration between various stakeholders (patients, doctors, engineers, healthcare administrators). Indeed, this cyclical process — planning, action, observation, reflection — enables real-time adjustments and guarantees continuous improvement. For example, consider AI technologies already improving diagnostics in several hospitals. For successful adoption, integration needs to be supported by action-research to adjust these technologies to the evolving needs of medical teams. The challenge is to integrate this innovation into an enabling environment while developing new scientific knowledge. What's your take on this? #HECDOOD #AI #OD Beer M. (2020) . «Making a difference: Developing actionable knowledge for practice and theory » ,Journal of Applied Behavioral Science , vol.56 , no 4 , p. 506-520 Akash Anand. (2024, August 30). Artificial intelligence in healthcare market to hit USD 370.14 billion by 2032, fuelled by AI integration in diagnostics and treatment. GlobeNewswire. https://lnkd.in/eTubts9x N.B.: This post is part of my Master's in Organizational Development at HEC Montréal, where I will publish a critical reflection on a current issue each week.

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GPT2-Chatbot Returns: A Glimpse of GPT-5? The Al community is buzzing with excitement and skepticism over the mysterious return of the GPT-2 chatbot, now under the guises of 'im-a-good-gpt2-chatbot' and 'im-also-a-good-gpt2-chatbot'. Users have reported that these models exhibit capabilities that surpass current Al models like GPT-4, Gemini 1.5, and Claude, with some even suggesting they might be a GPT-4.5 variant or even a glimpse of GPT-5. However, opinions are divided, with some users pointing out that the models might be prone to hallucinations and not as advanced as initially perceived. Despite the excitement, there's no official confirmation from OpenAl regarding the nature of these new models, leaving the Al community to continue its lively debate. Fascinatingly, during my testing of it (refer to the video), the 'claude-3-haiku' model gave an inaccurate response to the age-old question "which is heavier, a ton of lead or a ton of feathers," asserting that a ton of lead weighs more. This discrepancy prompts inquiries into the model's grasp of fundamental physics principles. #AI #MachineLearning #ChatGPT #OpenAI #Chatbot #Haiku #AIModels #TechnologyNews

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>>>New #Research Published Model Complexity Reduction for #ZKML #Healthcare Applications New research has been published in #Blockchain in Health Today Platform Approaches in Healthcare open access peer reviewed journal. Authors > Sathya Krishnasamy, MS > Ilangovan G., MD The author discusses the barriers and the mitigations through specific technologies maturing to solve issues including privacy-preserving technologies, off-chain and on-chain design optimizations, and the multi-dimensional approach needed in planning and adopting these technologies. The paper discusses how one enabler, ZKML, combines two streams of technology that are merging in unique ways to address problems in privacy and the cost of inference. The authors have conceptualized the technical and operational feasibility and implemented a reference healthcare implementation using the synthetic ICHOM in the evaluation phase of a global healthcare setting for high-volume data collection, including patient-reported outcomes. Model complexity reduction is researched and reported for the ICHOM #diabetesdataset to advance the usage of ML models in global standards of healthcare data collection in network decentralized architectures for increased data protection and efficiencies. *****The deadline to submit your research, reviews, editorial, is November 1, 2024. Upload your work AND cover letter to the submission portal at https://lnkd.in/dpRQZYw

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>>>New Article The Role of #ExplainableAI and Evaluation #Frameworks for Safe & Effective Integration of #LLMs in #Healthcare - summary report of key perspectives from panel discussion at #ConV2X 2023 event, produced by Partners in Digital Health Telehealth Medicine Today journal (#THMT) journal. See https://lnkd.in/eibKNMeS Key topics covered: 1. The potential of explainable AI to facilitate transparency and trust 2. Challenges in aligning AI with variable #globalhealthcare protocols 3. The importance of evaluation via translational and #governanceframeworks tailored to healthcare contexts 4. Skepticism around overly expansive uses of LLMs for conversational interfaces 5. The need to judiciously validate LLMs, considering #risk levels Sustained advancement hinges on governance and transparent assessments to pinpoint the most beneficial applications for #emerginginnovations in #realworld #caredelivery. ***#Authors*** > Sandeep Reddy, MBBS, MSc, PhD, Deakin School of Medicine, #Australia > Alexandre Lebrun, MS, MSEE, Nabla, #France  https://lnkd.in/efq-_WpA > Adam CHEE, PhD, Saw Swee Hock School of Public Health, National University of Singapore, Singapore Moderator: Dimitrios Kalogeropoulos, PhD, Global Health & Digital Innovation Foundation, #UK ***Submission Deadline for next issue: July 1, 2024 Upload work to https://lnkd.in/es_5reh #AI #ExplainableAI #AIinHealthcare #AIIntegration #AIEcosystems #AIGovernance #Leadership #digitalhealth #healthcareinnovation #telehealth #telemedicine #virtualcare

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8 Must-Have Tools for Medical Device Startups!   Effective collaboration is critical for success in the highly regulated medical device development world.   Here are the must-have tools for your startup to keep track of device development and evidence generation needed to get regulatory approvals for your medical device:   1. Secure Data Storage:   A secure and "access control" file storage system is crucial to protect your Intellectual Property while facilitating evidence generation and effective team collaboration.   2. Audio/Video Communication Platform   Remote work is a common work environment these days. It allows you to find the best subject matter expert anywhere on the globe. Selecting a team collaboration tool that facilitates instant messaging, video calls, and secure file sharing is a must-have.   3. Design and Prototyping Software   For medical device startups, collaborative design and prototyping software allow engineers and designers to iterate product concepts while maintaining version control. Tracking which version is ready for the testing phase is crucial for the regulatory files.   4. Product Life Cycle Management   For medical device startups navigating the complex product development lifecycle, a PLM system is important to manage Requirements, Specifications, Risks, Verification and Validation Tests, and Traceability, which are the key aspects when submitting regulatory files for market clearance.   5. Electronic Lab Notebooks   Centralizing ELN when used for documenting experiments, capturing thought processes, and tracking research progress can enable efficient collaboration. It also comes in very handy when submitting Intellectual Property applications.   6. Document Collaboration Tool   Real-time document collaboration is important when multiple stakeholders are working on draft versions of documents. This collaboration allows simultaneous edits and comments on documents without worrying about version control issues, which are needed when the document is ready to be released for regulatory purposes.   7. Quality Management System   The QMS is needed when the documents are ready to be implemented. It facilitates control of processes, ensures compliance with regulations, and centralizes final versions of product and process documents.   8. Bill of Materials Management   The BOM system is the link between the PLM system and the manufacturability of the medical device. While the PLM system provides traceability to design development, BOM provides a recipe for building the medical device. In many cases, they operate as an independent system, but when integrated, they can bring process accuracy and efficiency.     By implementing correct tools, medical device startups can accelerate product development while maintaining compliance with industry regulations.     ———   ✍️ Your comment may guide someone's action today!   Share your thoughts 👇

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The Intersection of AI and Healthcare The intersection of artificial intelligence (AI) and healthcare is one of the most exciting and transformative developments of the 21st century. It promises to redefine how we approach diagnostics, treatment, patient management, and health research. The fusion of AI with healthcare opens doors to faster, more accurate diagnoses, improved patient care, and potential breakthroughs in personalized medicine, creating a wealth of opportunity for investment and innovation. The more I understand this, the more excited I am about working in this area as a seed stage VC. AI’s ability to process and analyze vast amounts of data holds immense potential for medical research and diagnostics. Machine learning algorithms, for example, can scan millions of medical records and images, identify patterns, and learn to predict conditions—sometimes even more accurately than human professionals. We've seen this with our portfolio company, Skin Analytics (skin-analytics.com), which has assessed over 120,000 NHS patients for suspicion of skin cancer and is helping to significantly reduce diagnostic time and error. It's equally relevant in other pathways from radiology and pathology to oncology, where early and accurate detection is critical. Beyond diagnostics, AI is revolutionizing patient care through predictive analytics. By analyzing patient history, genetic information, and lifestyle factors, AI can help medical professionals foresee complications and take proactive measures, improving patient outcomes and reducing hospital readmissions. Our portfolio company, Abtrace (abtrace.com), helps GPs save huge amounts of time by using AI to review Electronic Health Records and suggesting a list of relevant actions. Remote monitoring and AI-driven virtual assistants are also making healthcare more accessible, especially for patients in remote or underserved areas. This accessibility expands the reach of healthcare, enhancing quality of life and reducing overall costs. Our portfolio company, Knok Health (knokcare.com), is helping Portugal's National Health Service reach more people, faster. As healthcare systems globally grapple with rising costs and workforce shortages, the efficiency and scalability AI offers can play a crucial role in addressing these challenges. If you are looking for seed funding for a game-changing idea at the intersection between health and AI, I'd love to hear about it. #ai #healthcare #investment #healthtech

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🚀 Harnessing AI to Revolutionize Neuroscience Clinical Development 🚀 ✔️In the fast-paced world of clinical research, VPs and Heads of Neuroscience face enormous challenges: long trial timelines ⏳, soaring costs 💰, and uncertain outcomes 🤔. Traditional clinical trial methods struggle to keep up with these demands. But what if there was a smarter, faster way? ✔️We're excited to share how we're harnessing the power of AI to overcome these bottlenecks and transform clinical development 🌟. ✔️ Introducing Digital Twins 🧬✨: Our novel AI models analyze vast quantities of patient-level data to create digital replicas—or "twins"—of each patient. These digital twins allow us to forecast health outcomes with unprecedented accuracy. By incorporating digital twins into clinical trials, we can accelerate research and significantly reduce costs 🚀. *Here’s how digital twins are reshaping clinical trials: 1️⃣ Shorter Timelines ⏱️: By simulating patient outcomes, digital twins allow us to predict responses and streamline the trial process, which drastically cuts down trial durations. 2️⃣ Cost Efficiency 💸: Efficient use of patient data minimizes the trial-and-error approach, reducing costs associated with prolonged trials and unsuccessful outcomes. 3️⃣ Enhanced Predictability 🔮: Utilizing AI to analyze patient data ensures more reliable results, giving researchers the confidence to move forward with potential treatments. 4️⃣ Accelerated Life-Saving Treatments 🏥: Faster, more accurate trials mean life-saving treatments can reach patients in need much sooner. *By integrating digital twins into clinical research, we are paving the way for a future where clinical trials are not only quicker and more efficient but also more precise and predictive. This is more than just a technological advancement; it's a revolutionary step towards a new era in medical research and patient care. *To all the VPs and Heads of Neuroscience out there, we're here to make your work simpler, faster, and more effective. Let's embrace this groundbreaking innovation and transform the landscape of clinical development together! 🌐🔬💡 #AI #ClinicalResearch #DigitalTwins #Neuroscience

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AI Revolution Meets Healthcare — Jivi AI and AI Fund Pave the Way for Smarter Care! The AI Fund, led by renowned AI leader Andrew Ng, has taken its first big step in India by investing in Jivi AI, an AI-driven healthcare #startup transforming the way #patientdata is analyzed and care is delivered. 🚀 With advanced #AI algorithms, Jivi AI aims to enhance #diagnosisaccuracy, automate health reports, and free up healthcare professionals to focus more on patient care. This #partnership marks a huge milestone in India's thriving #AI market! 🌐 🔗Read More - https://lnkd.in/dRyHVQUj 🖊️Written By - Dr. Jayati Dubey 🌐Stay Informed with our Latest Stories – https://lnkd.in/gMUPB2Zp Sequoia Capital, SoftBank Group Corp. #AIInHealthcare #Jivi #TechInnovation #AIInvestment #AndrewNg #SequoiaCapital #SoftBank #HealthcareTransformation #AIRevolution

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Google is considering partnerships with creators as part of its investment in the #AI arms race 🤖. According to a report from The Information, Google is developing customizable chatbots that will run on its in-house large language model, #Gemini 🌟. The report indicates that Google’s chatbot users will be able to customize the personality and appearance of their AI companions 🛠️. The product could eventually be integrated into YouTube 📺, and Google has reportedly held talks with creators about the possibility of developing chatbots based on those influencers’ likenesses 👥. That plan sounds similar to the one Meta has put into place for its AI characters. Some of the chatbots powered by Meta’s Llama LLM were built in tandem with creators like Charli D’Amelio and MrBeast 🌐. TikTok has also explored the idea of launching its own customizable chatbot product 🎉. #AI #ArtificialIntelligence #GoogleGemini #Chatbots #YouTube #Creators #Meta #LLM #TikTok #TechNews #Innovation #FutureTech #DeepLearning

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MyPOV on AI / GPT-4o: The biggest and the most fundamental disconnect between the general consumer facing use cases (that increase the hype around us) and the enterprise ones (that contribute to the 5% mentioned in the HfS article) - has been the availability of clean enterprise data with which to train the models, AND the sustained availability of this “clean” data at scale for operations (if the pilot delivered on its promises). It is reality that 99% of the effort and cost goes into establishing and cleaning the enterprise data - than on building / training the model, which is the 1% cost here. Does GPT 4o really move the needle on this front? I’ll be waiting and watching! IMO - Notwithstanding the ambitious CxOs who drive AI (irrationally maybe), true enterprise adoption numbers will go up only when the ecosystem of utilities around the LLM grows… I.e., these are the ones that are focused on getting the enterprise “AI ready” viz. solving for grunt work, like fixing / cleaning / anonymising data, business rule extraction, making data connections and pipes, connecting to existing bots etc.

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Unmet social needs could increase risk of costly surgical complications by 2-3x in some circumstances. Assessing and addressing SDOH factors is thus becoming mandatory under a variety of VBC payment models and episodic bundled payments. RevelAi Health's use of LLM-powered voice AI for specialty care management, social needs screening and closed loop social referrals with integration with Findhelp is delightfully seamless and groundbreaking at the same time - with far reaching applications. Very proud to share this simple demo and contribute to this work pioneered by Christian Pean M.D., M.S. and Hadi Javeed.

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