Brian Pepin

AI and Med Tech in Focus   AI System Developed to Identify Pathology in Tissues Engineers at Washington State University have developed an AI tool that uses images of human and animal tissue to detect abnormalities faster and more accurately than people. The AI algorithm was trained using images from past studies that involved kidney, testes, ovarian, prostate, breast and lymph node abnormalities. The new model detected disease in minutes—current processes take humans several hours and require specialized teams to check each other’s work. It was also found to be more accurate, sometimes detecting instances that humans had missed. The engineers designed the system to handle the processing load of the high-resolution gigapixel images by breaking them down into smaller tiles but still retaining the overall context of the tissue sample—akin to the zoom feature on a microscope or phone camera. Aside from getting diagnostic results more accurately and quickly, the new system also shows promise in epigenetics (the study of molecular processes that change gene behavior without changing underlying DNA). Epigenetic researchers can perform analyses and get results that used to take a year or more in just a couple of weeks. VC Investors are always adjusting their "Play Book" with a variety of options for investing. Mikal Ventures is one of those alternatives, offering curated solutions to real world problems. Reach out if you have questions on how we can be of assistance and bookmark https://mikalventures.com/ for additional insights and information.

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Excited for DCVC portfolio co Alta Resource Technologies to emerge from stealth! The design space of biology is many orders of magnitude larger than inorganic chemistry, and Alta uses it to massively reduce the cost of extracting rare earth metals. https://lnkd.in/g_q5UwRF

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🧩 One thing we've noticed with #VentureIQ technology is how well it can distill and communicate complex technical material 🧩 Whether it's a #deeptech venture, a novel #AI application, or even a patent submission, we can quickly distill the business and strategic implications for investors...and sometimes even the founders/PIs themselves! 🤯 Based on our work so far, the tech can handle a wide array of research areas and technological sectors -- anything from blockchain algorithms to vaccine development. It handles the nuance of the field, the specific terminology, and extracts the key concepts which matter for venture-building. 🤓 To demonstrate, I took a patent I helped write with Eric Laber on reinforcement learning. I ran it through #Ventureiq to build a commercial assessment (key differentiators, target markets). Then for fun I had #NotebookLM make a podcast out of it. I've uploaded the result it here (DM me if you'd like the actual report): https://lnkd.in/eiTejsny This is more than just automation or streamlining operations - it's about unlocking clarity and insight when it was previously unattainable, and the ability to do it over and over again no matter the sector or vertical. Interested in more info? DM me so we can chat.

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Reimagining Healthcare Workforce Platfforms: Turning Capital Into Labor The future isn’t just about filling shifts. It’s about scaling labor intelligently, driving better outcomes for systems, staff, and patients alike. Andreessen Horowitz and Alastair (Alex) Rampell reinforced this Vision in a recent post and it applies to the next phase of healthcare workforce evolution. Healthcare workforce marketplaces plus AI are at the tipping point of transformation, evolving into systems of record that don’t just manage labor—they optimize it. Much like EHRs centralized patient care data, these marketplaces can centralize workforce operations, aligning talent supply with clinical demand. But there’s a bigger opportunity here: turning capital into labor. Here’s what I mean. Historically, industries (including healthcare) have focused on enterprise computing tasks—streamlining data entry, compliance, and back-office functions. While valuable, this approach often stops short of transforming the real-world, repetitive processes that make up the bulk of work in healthcare. Think beyond enterprise software. The next frontier lies in leveraging embodied AI and workforce marketplaces to automate and enhance labor-driven processes, transforming capital investments into productive labor outputs. For healthcare, this means turning staffing costs into scalable, dynamic systems that deliver value far beyond filling shifts: 1. Dynamic Labor Matching: Capital invested in marketplace platforms enables real-time matching of labor supply with patient demand, replacing outdated scheduling models with predictive, automated systems. 2. Workflow Automation: From credentialing to compliance and shift bidding, embodied AI can streamline traditionally manual tasks, freeing human resources for higher-value work. 3. Labor as a Strategic Asset: Workforce marketplaces shift the focus from static staffing budgets to proactive labor deployment, ensuring that every dollar invested in labor drives outcomes and efficiency. It is about the billions of repetitive, planned tasks that AI can improve. In healthcare, that means automating everything from staffing logistics to clinical task assignments, creating a horizontal system of engagement for labor (think Zendesk) that integrates seamlessly with vertical, workflow-specific platforms (think Toast for staffing operations). The result? Hundreds of billions in untapped market potential—and a healthcare workforce model that transforms how care is delivered. By turning capital into labor through smarter marketplaces and automation, we don’t just solve staffing challenges; we redefine what’s possible in healthcare operations and ShiftMed is leading the way with health systems moving beyond tired models. #capitalintolabor #systemsofrecord #healthworkforce #aiworkforce #healthsystems #hospitals #cno #hospitalceo #healthcareinnovation

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Though benchmarks for medical AI are still imperfect, there's no question that AI will continue to march towards diagnostic excellence. These trends will place greater emphasis on physician-scientists – those who advance medical knowledge via closed loop clinic/lab interfaces, which can then feed back into large-scale AI systems that serve entire populations. This was all part of the OG vision for the learning health system. source: https://lnkd.in/eh2RM72s

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A SAFE and convertible note had a baby and Wilson Sonsini Goodrich & Rosati was the midwife- out popped the D-SAFE! 👶🏽🎉 A new #climatetech funding instrument was announced by Elemental Excelerator last week. One of the company’s main goals is to support smaller, up and coming companies during the awkward “Scale Gap” phase between #VC funding and natural growth. It’s called the #DSAFE (Development Simple Agreement for Future Equity) and it aims to aid the process by giving companies a small amount of cash to overcome initial risks and expenses during the developmental stages of a product launch or company. How does it work? D-SAFE has a redemption clause meaning companies can choose to have their payments offered as a loan or convert to equity at the investor’s discretion. Funders and companies will have clear communication and expectations about where the money is going and how it will be applied to either the company or the projects at hand. Financiers and companies will work together with more #transparency, and that’s a great boon for #startups and VC alike. As of this month, Elemental has already invested using D-SAFE in eight different companies, including Dimensional Energy and Origen — both companies investing in innovative and out-of-the-box solutions to environmental issues! Seems like a win-win on the surface. What do you all think about the D-SAFE? Alfredo Coppola Momoka Ueda Tim Wagner Stephen D. Torres Olya Irzak Natsuho Toyama Noriya Tarutani Shin Ogawa

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This is just incredible. Neal Ghosh's demonstration of how #VentureIQ takes deeply technical and complicated content across a range of industrial use cases and ages/stages through an AI-generated conversational podcast (a la #notebookLM) is really mind-blowing. Check out his post, below, and if you haven't seen VentureIQ, yet, let's get you set up to learn more. You can visit VentureIQ.ai or DM any of us over here to book a demo!

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Excited to announce Frist Cressey Ventures' investment in Qualified Health and privileged to partner w/ SignalFire, Healthier Capital, Town Hall Ventures and others!   Healthcare costs are increasing at a faster rate than ever before, with the US spending nearly twice as other wealthy countries and still ranking bottom in outcomes. It's a story you've all heard - - US healthcare is known for astronomical inefficiencies, waste, workforce supply constraints, fragmented access, appalling patient experiences, and driving patients and businesses into debt.   Yes these problems present opportunities. But opportunities are limited to the system's appetite for change, desire and incentive to try something new and the innovation available. At FCV we believe we've hit the trifecta with Generative AI. Generative AI has given us truly transformational tools in the toolkit and the healthcare ecosystem is demanding to absorb its benefits. This perfect collision of supply and demand means healthcare is poised for big change.   Generative AI is everywhere, it's buzzy, it dangles hope and opportunity. But many things need to be true for the Gen AI transformation in healthcare to take hold, such as but not limited to:   💡 Patient lives and data need to remain safe: We believe in a highly regulated and human life-touching sector such as healthcare we must ensure Gen AI can drive to unrivalled savings and improvements in the quality of care WHILE not putting patient lives or data at risk.   💡 Systems need to own their utilization of Gen AI: We believe systems will use some hybrid of external partners and homegrown solutions but importantly will want full control of data provisioning and utilization as well as the ability to solve an unlimited number of nuanced issues and not cookie cutter algorithms.   💡 Costs need to be looked at on an enterprise level: We believe the speed of innovation in Gen AI means costs will continue decreasing dramatically but at an enterprise level this will still not be a small detail. Systems will want the ability to understand costs and delegate as needed for the outcomes desired at the organizational level.   Enter Qualified.   We knew when we met Justin Norden, MD, MBA, MPhil and team we were standing in front of changemakers. These individuals have lived and breathed the true application and implementation of Generative AI far before we were asking ChatGPT to write our emails. In fact, if you’ve ever driven in a Waymo you’ve benefited from past products this team has built.   Qualified Health is on a mission to enforce governance in Gen AI, allow systems to rapidly build for their needs, monitor and make decisions for all GenAI (homegrown and external) and accelerate the value the sector can glean from these new technologies.   Huge shout out to William T., Tommaso Auerbach and Jamie Kuntz for their hard work!

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I find it hard to believe that with millions of EVs entering the market and the proliferation of AI compute that PGE is actually going to have excess capacity in 2040 without any additional generation from 3rd parties. I am also curious if there are enough transmission lines and if PGE views that as someone else’s problem. Maybe they are planning on AI and ACTUAL figuring it all out! https://lnkd.in/gybsMmaw

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Open AI GPT-4o announced yesterday, fascinating. Omni modal, one mind with text, image, video 2x faster 50% cheaper 5x higher rate limits AI is moving so fast. I'm thinking about what it means for manufacturing and enterprise data. Used another of my favorite software tools Loom to record a quick 'Opinion' while watching some of their Youtube video demo, showing it reading a temperature variation chart and acting as a simple data analyst. What do you think GPT 4o and the direction implies for enterprise data stacks and manufacturing AI? My takeaway - we need to rethink data architectures from 'how do I design for crazy busy humans' to 'how do I design for AIs with near infinite time to read charts and reports and finally summarize/interact with humans'. Reflecting on how much human-work goes into defining the simplest possible red/yellow/green KPI from a mountain of data, like getting everything into MQTT for a final OEE score. We do so much to help a human make a decision spending 10 seconds or less glancing at a final graph or number. When instead imagine using those 10 seconds the human has to be able to ask/interact with an AI that can have read 1000's of graphs/reports in advance, built on rich contextualized data, just to best answer your question. Wow. https://lnkd.in/eSgZAbjx

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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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Breaking down our latest newsletter section by section, starting with our expanded focus in Deep Tech: --- We have built expertise in the robotics sector, and will continue to double (and triple) down on investing in this category, but we have been increasingly finding that robotics is naturally steering us into two exploding adjacent areas: Aerospace, and Advanced Manufacturing (which we define as industrials, semiconductors, materials science, and energy production). Stepping back, this expansion makes perfect sense. Robotics is an inherently horizontal platform - meaning it can be applied productively across multiple industries. This quality, paired with the fact that robotics has long shown the highest TRLs of any deep tech technology, makes it a kind of “gateway” to other sectors in the hard sciences. Now with the recent breakthroughs in AI for robotics, growing semiconductor demand, and the resurgence of aerospace and defense tech, we are seeing a dramatic uptake of robotics and relevant technologies in both advanced manufacturing and aerospace. As an added bonus of the expansion, many of the founder (and funder) networks across these landscapes have natural overlaps and linkages. For AlleyCorp Deep Tech this means that we can be extremely helpful, knowledgeable, and engaged across all three sectors right away, and continue to be the best-in-class investing team across these categories.  --- More in our newsletter: https://lnkd.in/ewiTuYq6

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AI in Focus  Wearable Camera Detects Injectable Drug Administration Errors Collaborative researchers from Carnegie Mellon University, University of Washington, Makerere University and Toyota Research Institute have developed a wearable camera that uses AI to detect injectable drug administration errors. The system used video data sets from cameras strapped to doctors’ foreheads in different operating rooms with different backgrounds and lighting conditions and focused on the physicians’ hands. Then using deep learning, they trained the algorithm to detect visual clues like vial and syringe shape, label color, and font size to determine what the drug was. Currently, errors happen when a wrong vial is used or the drug label in the syringe is incorrect (vial swap errors) or when the label is correct, but the clinician administers the wrong drug (syringe swap errors). Hospitals use bar codes to prevent these errors, but in high pressure situations, these safeguards sometimes are forgotten. Approximately 1.2 million patients experience errors in injectable medications annually, at a cost of about $5 billion. Using the AI camera system, vial swap errors were detected in 99.6% of cases where they occurred and syringe swap errors in 98.8% of cases.  The next step is for researchers to incorporate the new system into smart eyewear that can provide visual or auditory warnings before a drug is delivered. Further, the new system provides opportunities for automatic documentation of drug injection information and reduced manual record keeping. VC Investors are always adjusting their "Play Book" with a variety of options for investing. Mikal Ventures is one of those alternatives, offering curated solutions to real world problems. Reach out if you have questions on how we can be of assistance and bookmark https://mikalventures.com/ for additional insights and information.

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Adam Keating discussing how AI is going to change manufacturing and how CoLab Software is helping them make that transition with ReviewAI

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What do bio and health applications mean for the next wave of AI -- specifically about compute? (I'm talking to you NVDA longs!) LifeX Ventures is hosting a super roundtable with some serious techbio leaders tomorrow along with Analog Century Management's Val Zlatev -- come hear Thomas Clozel Sean McClain Chetan Patel at 11am ET For the survey results based on asking 100 techbio CTOs and CEOs, join the call. Here, my own speculations after wrapping up the report: -- Amazon needs to have a GPU program on warp drive right now -- Nvidia needs to have a data center / cloud business in the works -- Edge AI chips are about to be everywhere -- in your watch, car, sneakers, thermostats, gene sequencers, spectroscopes -- All this compute needs way more data: expect a boom in sensors of all types -- Remember human genome moonshot that sequenced one person once? Now you can sequence your every cell one by one. Expect that for protein folding (AlphaFold was that moonshot) and other simulation uses, a full "twin" model of you, cell by cell -- Google has no angle here -- worse chips, worse cloud, worse models, worse phones / edge devices -- Specialty silicon for unique use-cases, lower-power, lower-memory are surely coming fast - like back in the day for mobile phones when Qualcomm, Broadcomm, Infineon, Mediatek, etc duked it out.

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AI and Med Tech in Focus   AI System Developed to Identify Pathology in Tissues Engineers at Washington State University have developed an AI tool that uses images of human and animal tissue to detect abnormalities faster and more accurately than people. The AI algorithm was trained using images from past studies that involved kidney, testes, ovarian, prostate, breast and lymph node abnormalities. The new model detected disease in minutes—current processes take humans several hours and require specialized teams to check each other’s work. It was also found to be more accurate, sometimes detecting instances that humans had missed. The engineers designed the system to handle the processing load of the high-resolution gigapixel images by breaking them down into smaller tiles but still retaining the overall context of the tissue sample—akin to the zoom feature on a microscope or phone camera. Aside from getting diagnostic results more accurately and quickly, the new system also shows promise in epigenetics (the study of molecular processes that change gene behavior without changing underlying DNA). Epigenetic researchers can perform analyses and get results that used to take a year or more in just a couple of weeks. VC Investors are always adjusting their "Play Book" with a variety of options for investing. Mikal Ventures is one of those alternatives, offering curated solutions to real world problems. Reach out if you have questions on how we can be of assistance and bookmark https://mikalventures.com/ for additional insights and information.

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Today I want to put the spotlight on Arelys Sosa, Thomas Tingelöf and Kent Moore who are the amazing founders of DREV.🔦 The problem they are tackling is that battery gigafactories, semi-conductor factories, and battery recycling plants face big challenges collecting hazardous metal micro-particles, slowing production, and they are squandering precious materials, and endangering staff. These are all individually big problems and combined this is huge.☣️ Drev has the solution to all to this with The Vault, an innovative technology powered by patent-pending dry brush technology and a revolutionary canister design, eliminates water and chemicals from the cleaning process, improves safety, and enables the adaptive reuse of valuable materials that would otherwise be thrown away with save manufactures millons. 🧹 The Drev team has the unique domain experience for solving a particular problem that we're always looking for when we are backing founders at the Norrsken Accelerator. Arelys is an industrial engineer who was part of the original team at Panasonic Energy at Tesla’s Gigafactory 1 so she has seen this problem first hand for years. Thomas has a PhD and he has been the CTO of PowerCell and VP at Volvo Cars and has massively scaled technologies before.🤩 The interest from the industry is huge and they are currently running projects with some of the leading battery manufacturers. You can read more and contact them here: https://lnkd.in/d7V-mKrx

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Hot off the pre-print server – an important publication authored by the Atropos Health team exploring the performance of LLMs in answering real-world clinical questions. LLMs excel at summarizing and contextualizing existing literature, but cannot generate de novo medical evidence today. The results of the study highlight performance gains attributable to injecting novel, relevant, and up-to-date medical evidence into LLM-powered clinical decision support systems. Brigham Hyde Nigam Shah Saurabh Gombar Read the study here: https://lnkd.in/ecuU25Pq

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Exciting to see startups aiming to use AI to discover chemicals, materials, drugs, etc. Semi companies have taken notice: AMD is investing in and Cadence is acquiring them. Semi/EDA companies have broken out by becoming critical path, whether it’s through critical software, packaging, IP, design kits from foundries, or user communities. Aside from just making it easy to make new materials, these “AI for making X” companies also need to articulate a vision as to how they can establish monopolistic positions in their industries.

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