Tatyana Mamut

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

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How do platforms like InVision by inThought Research help life science companies with product development from phase I to first year? inThought's President, Ben Weintraub explains on the podcast how Invision aggregates and analyzes vast amounts of clinical data, enabling teams to make evidence-based decisions with confidence. Decisions around: - how to design their trials? - how to communicate with regulatory agencies? - how to communicate with healthcare providers? If this is of interest, you will love the full podcast here: https://lnkd.in/eK_G272k

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The (Healthcare) Ground Beneath Our Feet… This year it is likely that over $10 billion will be invested in the digital health sector – a robust amount, no doubt, but inconsequential when compared to the amount of capital tied up in healthcare real estate assets, which is estimated to be $1.2 trillion by Jones Lang Lasalle. Of that amount, approximately $790 billion is in the hospital sector while the remaining $490 billion is in medical office assets. It is estimated that there are 48.6k facilities with 3.4 billion square feet of healthcare real estate, 42% of which are owned by REITs and another 33% held by other private investment vehicles. Much of the digital health investment is meant to make the physical healthcare infrastructure more productive: greater operating efficiencies, greater patient throughput, more relevant personalized care models with better outcomes. As the healthcare system is being pushed to be more distributed to lower cost sites of care, accelerated now by the rapid proliferation of automation and AI capabilities, the potential impact on healthcare infrastructure assets will be profound. Thoughts on what that might look like... https://lnkd.in/eTfAdUbS #digitalhealth Flare Capital Partners

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It is a very exciting day for UserCue. It’s also an exciting day for the life sciences industry, primary research and AI technology! https://lnkd.in/g_S6uSsP We are thrilled to announce a strategic partnership with LINUS, a leading life sciences strategy and innovation consultancy. Over the past year, we’ve collaborated with LINUS to develop UserCue’s advanced AI research platform, specifically designed to support rapid primary research in every area of life sciences. Combining LINUS’ domain expertise with UserCue’s primary research engine is a dream partnership that pushes the boundaries of what is possible in the world of insights with AI. My favorite part? The companies innovating on the frontier of healthcare will be the ones that benefit the most from this partnership and our research together. LINUS helps the worlds leading organizations grow and be relevant in the evolving health landscape, supporting teams with insights from a globally distributed network of experts. Now, with the power of UserCue’s platform, we are excited to expand the partnership to bring a new generation of research capabilities to these organizations. To showcase this collaboration, we will be hosting a webinar with LINUS on June 10th to highlight one of the many studies we have worked on together. Join us as we showcase our research on the impact of AI in Biopharma. A huge thank you to the LINUS team for a great year and many more to come. We are just getting started. More details in the comments! #LifeSciences #MarketResearch #AI

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Incredibly excited to share our updated Healthcare AI thesis. This is not a market map – in 4,000 words, we dive deeply into the research, products, business models, and market entry points that are reimagining healthcare with AI. More specifically, we cover: • Translating AI research into real-world products • Mapping modalities (software, copilots, agents, services, diagnostics, therapeutics) to problems in healthcare • Designing AI business models that unlock billion-dollar markets • Closing current gaps in healthcare AI infrastructure • Distinguishing AI platforms from point solutions Let us know what you think ⤵️

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At the annual Decoding Bio summit, I sat down with Brad Loncar of BiotechTV to discuss how we at Bessemer Venture Partners are thinking about where value accrues for AI in biotech. The short answer: Ultimate value accrues at the therapeutic level. Longer answer: If AI improves drug discovery along any axis (speed, efficiency, cost, new biology, novel chemistry), and becomes a near-ubiquitous tool in the discovery process, value will accrue up the stack. Vertical integration of discovery and development will be essential to capture this value, and the greatest moats will (still) be built in clinical development – trial design, biomarker identification, indication selection, and raw execution.

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Open-source models can be a strong lever for increasing gross margins at AI companies, especially those that have hefty cost structures (e.g., enterprise sales orgs that require significant customer success or implementation resources, as well forward-deployed AI services models common in healthcare and life sciences). One of many reasons to maintain a modular and flexible stack with minimal dependencies when building an AI company. More on the importance of flexibility in the AI stack: https://lnkd.in/eA2XATRk h/t Delip Rao #ai #artificialintelligence #generativeai #healthcare

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🔥Tune In to the Latest Fireside Chat with a VC in Healthech🔥 What percentage of VCs are former founders? 30% maybe more? I'd think a fair amount, yet how *exactly* does that transition happen and what perspectives change *radically* afterwards? Sigvards Krongorns shared exactly that - his journey from PwC consultant to Healtech founder, to a VC shaping the future of Healthtech startups. What we talked about: ✨ Sigvards' transition from a PwC consultant to founder to a VC ✨ What steps are necessary to bring innovative medical devices from concept to market ✨ How being a founder can prepare you for a role in venture capital ✨ What strategies help navigate the regulatory landscape in healthtech startups ✨ The right way to engage with VCs for startup funding ✨ What future trends in venture capital and Healthtech look like #HealthTech #VentureCapital #Innovation #StartupJourney #HealthcareInnovation #Entrepreneurship #FiresideChat #VentureCapitalInsights Full conversation can be found here:

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Are LLMs better at Deductive (start with a general formula/relationship and compute outputs for different inputs) or Inductive (start with examples of inputs and outputs and figure out the generalized relationship) reasoning.... The answer is they are better at Inductive reasoning... Based on this what kind of applications are they better suited for???? More details in this amazing study....https://lnkd.in/gjKYUraW

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Super thoughtful approach to org design highlighted in this week's First Round Capital Review. When I read it, I realized that simplistic advice is a poor shortcut to the more complex but useful advice, "structure follows strategy". “When you read management books about org design, they always show you these perfectly balanced charts. Symmetrical tree diagrams and curated grids that fill up to the margins. But creating a pretty looking chart is not a KPI,” says Linear’s Head of Product Nan Yu. Yu unpacks why conventional thinking around org design is slowing startups down. Instead, he pulls from Conway’s Law and lays out the three key pieces to constructing the asymmetrical “heirloom tomato” org chart. https://lnkd.in/gASyChQC

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Boston's vibrant health tech scene is fueled by world-class accelerators and incubators, empowering startups to drive digital health, biotech, and life sciences breakthroughs. Here’s a look at some key players: MassChallenge HealthTech—This accelerator helps startups transform health solutions across digital and biotech. It connects entrepreneurs with resources and mentors to drive meaningful change. LabCentral – A coworking lab space offering shared resources and lab facilities, LabCentral supports biotech and health tech startups by providing them with an innovative, collaborative environment. #Pagliuca Harvard Life Lab – Located at Harvard, this incubator offers lab and office space to promising biotech and life sciences startups, helping them scale groundbreaking health tech solutions. Richi Entrepreneurs – With a global outlook, Richi Entrepreneurs offers a unique two-week immersion for international health tech startups, connecting them with Boston’s local mentors, investors, and industry leaders. Xontogeny – Specializing in life sciences, Xontogeny partners with early-stage health tech companies, providing strategic guidance and operational support to accelerate their journey from concept to market. These incubators and accelerators are not just nurturing innovation—they’re powering the next generation of health solutions! 🌟 #BostonHealthTech #InnovationEcosystem #Biotech #HealthTech #StartupAccelerators

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🧪 Last week, $1.01 B was invested in AI. We are seeing a growing trend in funds going into Healthcare related AI use cases with $105m going into the sector last week alone. AI has huge potential to help in this space. Diagnosis, drug discovery, monitoring, analysis, reporting. ~~~ All of these tasks require large volumes of repetitive actions, but with a requirement for personalised repetitive actions. ~~~ The challenges for this industry are still, well, challenging. --Data privacy remains a top concern, there really isn’t any consumer data that is more sensitive. Consumers and regulators worry about personal data getting leaked, getting accessed or training wider models. --Bias. Bias occurs when training data and models are not perfectly balanced. For example, imagine an AI model missing a diagnosis for Malaria because it was trained on data from a single town in a country with no Malaria. --“It wasn’t me”. Humans and AI make mistakes, and when this results in damaged health or death - it is a tragedy. Who is liable at the end of the day? Clinicians? But they are acting on insights from AI…. This is a sensitive issue and will require very strong frameworks and regulations. --Validation. Yes the insights from AI may look correct, but are they really? Integration of AI tools will require a period of “supervised learning” where models are fine tuned based on what human clinicians observe. Clinicians are already very overworked and this period will only increase that. ~~~ A few mentions about the companies who got funding last week: --Huma - $80m, “hospital at home” offering. Huma collects patient data remotely and proactively connects patients to clinical teams. Whilst this offering supports those who need specialised care, Huma also aims to help those working in the industry - an industry very overburdened and prone to burnout. --Xellar Biosystems - $13.8m, wet lab scalable human biology and drug discovery. What is “wet lab” - well it’s a method of computation that favours pure experimental data over simulation. Human biology is still being explored and learned about, there are still so many things we don’t understand. Using real local data to compute is highly exciting here and I expect this method (and technology) to trickle down to other industries and use cases. --dotLumen - $5.5m, AI Guide Dogs for the visually impaired. “Guide dogs are great, but not scalable”. This is wearable technology that indicates safe walking paths, roads, puddles and dangers. They also recognise text allowing users to read and seek jobs they weren’t able to do previously. ~~~ These 3 examples are another showcase of AI enabling a higher range of capabilities (as opposed to merely replacing existing capabilities). AI will not replace jobs, it will create them.

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🎬 Matt Damon and 🏈 Colin Kaepernick have invested in it, and so has 💰Andreessen Horowitz... Function Health's executive team has "engineered a first-of-its-kind approach that focuses on prevention and empowerment"—very much in line with what we aimed to achieve with Carebook when we started it over six years ago. Could they be the ones to fulfill their lead investors' prophecy (link at the bottom) and Dr. Eric Topol, MD's premonitions in his books "The Patient Will See You Now" and "Deep Medicine"? They have just secured a $50 million grant. Let's hope this is not another Theranos and that we are truly witnessing the rise of the next generation of direct-to-consumer health companies. These companies will be key players in the "transition from reactive to proactive" health care that the system—and we—desperately need. What do you think? Links: 📝 Investment round press release: https://lnkd.in/eUG87qkf 🔗 Andreessen Horowitz's prophecy: https://lnkd.in/eZATCf4f

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Google's first CEO, Eric Schmidt offers powerful advice to entrepreneurs on protecting equity and building successful businesses. He stresses the importance of frugality and resourcefulness, reminding us that founders must focus on spending money wisely and efficiently. Many of the best founders, like those of Google, started with no salary and minimal luxuries, using every dollar strategically to grow their venture. His own experience—working in shared office space and using a door as a desk—serves as a reminder that success comes from grit, sacrifice, and smart financial decisions. To all the entrepreneurs trained and mentored by the Centre for Entrepreneurship Development, BRAC University, remember that building a sustainable business means being resourceful, protecting your equity, and staying focused on the long game. Your hard work and sacrifices today will shape your success tomorrow. #CED-BRAC University, #Entrepreneurship #Frugality #FoundersJourney #BRAC #BRACUniversity #StartupSuccess #EquityProtection #Leadership #GrowthMindset #FounderSacrifice #BusinessBuilding #Startups

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This is a fascinating article suggesting LLMs can help providers be more compassionate in patient text messages. I know when I'm busy and over-worked I often forget to add the human touch to my own messages. At Videra we have been exploring how to help providers communicate more effectively with more compassion and empathy. #DigitalHealth #PatientCare #HealthTech

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This resonated with me today: "“One flaw of designers (as people) is pretending faster is always better and quick always equals efficient. A fast pace can feel good and productive short-term, but there are many long-term consequences: losing productivity, overlooking important things, and undermining diversity. It is not efficient to move so fast that we exhaust ourselves, overlook the obvious, or break everything along the way, yet we often make that assumption.” It's from Assembling Tomorrow and I highly recommend the book. https://lnkd.in/gK43NueY

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What do you think about AI in senior care? Personally, I think we have to be careful with it. When AI presents itself as human, it can be confusing for seniors who may not have strong digital literacy skills. In fact, if they think they're talking to a person and not an AI chatbot, it can be dangerous if they're having a medical emergency or issue. However, there are some helpful use cases. I think Kristen Fischer's exploration of AI here is really interesting. "Artificial intelligence may be a useful communication tool to help older adults with cancer communicate with their doctors and have more of a say in their treatment," she says. AI can provide more context or explanation about certain treatment plans and translate the patient’s needs to clinicians in situations where the patient doesn't feel understood or heard. AI will never replace professionals, but it could help patients and clinicians better understand each other. Feel free to share your thoughts on this—I'd love to know if you think AI would be an effective tool for these situations. #AI #SeniorCare #FutureOfHealthcare

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And my second investment at defy is in 🐶 zeroETL puppies 🐶... I mean PuppyGraph, which is all the beauty of a graph database, none of the data migration or continuous upkeep of a graph database. Sounds like magic? So are puppies 🐶 . Puppygraph is NOT a graph or a database. It is graph analytics engine that answers graph questions on your existing data warehouses, whether that is a Databricks datalake, Google BigQuery or postgres. Compared to SQL, it's faster, handles way more complexity, scales and has almost no upkeep. How did I get to conviction? 1. Tailwinds - 🌊 Lakehouses 🌊 have helped enterprises put all their data in one place, enabling Puppygraph to unlock insights previously unavailable. - 📚 LLMs! 📚AI and RAG cannot easily traverse fragmented data. Puppygraph's Graph RAG unlocks insights with their Knowledge Graph and spoonfeeds to the LLMs. 2. 👯 Team 👯 We have graph experts from Google, LinkedIn, Databricks, Tigergraph, and Instacart who are uniquely able to build this solution. 3. 💸 Customers 💸 . My alma mater Coinbase is a proud user. The feedback from my former colleagues was off the charts! A blockchain is very much a graph database and Puppygraph tracks down fraudulent transactions that go through mixers and helps identify patterns. Huge congrats to Weimo Liu Danfeng Xu Lei Huang Zhenni Wu Gary Hagmueller, defy.vc is proud to be an investor!!

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The most talked about AI x Medical startup Abridge is working to rescue doctors from the never-ending admin grind (which causes burnout and less patient face-time). Abridge's AI listens to patient visits and generates precise medical notes, slashing the clerical workload. Now, doctors can focus on what truly matters: patient care. Major health systems like Yale and Emory are already on board and raving, calling it "life-changing" and "magical." 🔷 A big reason --> Payback of the scribe tool is easy to measure. It supports 14 languages including Haitian Creole, Brazilian Portuguese, and Punjabi. An ongoing criticism of generative AI is that it still has few substantive business applications. This seems to quash that. Question: is a dedicated medical LLM needed to be successful in the space? Or will the huge foundational models (GPT-4o, Google, and Meta) ingest a plethora of medical notes and kill the competition with performance? We're dealing with a fast and furious pace, and today's edge may not be relevant tomorrow. Competitors include Microsoft-owned Nuance Healthcare , Ambience Healthcare, Nabla and Suki. I'm excited to continue to see this field revolutionized. 🌟 #AI #MedTech #Abridge #HealthTech

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