Andrey Chursov, Ph.D.

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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Excited for this one. Congrats to Alex Cohen and Hello Patient on their seed round announcement. Hello Patient stands to change the communication paradigm between patients and providers forever. Their generative AI call agent technology is tackling the tremendous admin burden of patient facing comms (scheduling, follow ups, referrals, prescription refills, etc). Massive opportunity and we think Alex and his team are expertly positioned to become market leaders. Max Ventures is thrilled to co-lead the seed round with 8VC and Bling Capital A great quote below from Alex highlighting the concept of "service as a software". Healthcare is a heavily service oriented industry - so the opportunity for step change is even more profound. The startup's approach is part of a paradigm shift where AI companies are offering service as software. "I'm a big believer that the right way to do this is through a managed services business. We're not building SaaS. I believe we can do this services as software where what was traditionally a services business now has software margins because I don't have to go employ a whole bunch of people to do the services under the hood," Cohen noted. https://lnkd.in/ehNN2AmW

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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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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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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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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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🧩 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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I’m optimistic about the potential of generative AI to reduce provider burnout and improve provider-patient interactions. However, that optimism is tempered by the data that indicates the technology is not quite ready yet. Bright Ventures is interested in digital health founders who understand the realities of provider-patient interactions, and the role technology can play in strengthening clinical outcomes. Which founders and teams are getting this right? Tag them in the comments below.

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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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If you work in tech transfer this is a good read-in on how our diligence tech can assess and evaluate assets with minimal inputs. The best way to learn more is to see it live! Reach out to me directly to learn more!

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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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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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Looking beyond #VC funding for your #startup? Aakash Shah of Wyndly (YC W21) reveals how he built a multi-million dollar #healthtech company using creative capital strategies. Learn how he leveraged Y Combinator's network and alternative funding methods to revolutionize allergy treatment. In case you missed it, you can watch the #FundraisingDemystified #podcast here: https://lnkd.in/eZT63YS4

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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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🚀 Excited to partner with Noah Labs adesso ventures invests in the €3M round, led by Nina Capital and joined by DHZB Foundation as well as great angels. Current remote patient monitoring solutions often fall short, resulting in poor experiences for both patients and doctors due to inconsistent devices and software. Noah Labs aims to revolutionize this space with a patient-centric approach. Why we invested: 🫀 Team: The Noah Labs team is exceptionally strong, with deep connections to the medical field. Oliver brings invaluable founder experience from a previous MedTech venture. Marcus, the technical mind behind the AI-based voice analysis, has a research background at Harvard Medical School. Leonhard, with his medical expertise, has worked as a cardiovascular doctor and was previously head of medical at an RPM startup. 🎯 GTM: The team has developed an impressive GTM strategy and is executing it with precision. Monitoring of heart failure can be reimbursed through insurances in Germany, making it the optimal entry point for Noah Labs. Nevertheless, other diseases are already in demand and can easily be covered. 🏁 Vision: Besides using the existing RPM-tools such as ECGs and blood pressure monitors, Noah Labs follows a big vision of introducing voice-based detections of diseases using AI. Currently, several studies for their voice-based detection of heart failure are ongoing - among them a large study with reputable Mayo Clinic. This will set them apart from its competitors and create a unique value proposition. Read more here: https://lnkd.in/dkR6iCWU

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There is so much hype around AI today, it is refreshing to see examples where the technology is making a tangible real-world impact. Healthcare is one such area, where early signs are very promising. AI is helping in areas ranging from reducing errors in breast cancer screening to greatly expanding access to radiology services within emerging economies. Check out this must-listen conversation below with the CEOs of Celesta Capital companies Whiterabbit.ai and 5C Network to hear more about this exciting area of practical AI advancement.

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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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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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New post: The big stack game of LLM poker https://lnkd.in/d5mfqgwk I’m sure you read David Cahn’s excellent and provocative piece "AI's $600B Question", in which he argues that, given NVDIA’s projected Q4 2024 revenue run rate of $150B, the amount of AI revenue required to payback the enormous investment being made into NVDIA each year plus the electricity to power GPUs to train and run large language models is now $600B, and we are at least $500B in the hole on that payback. The numbers are certainly staggering… and are just going to get bigger. Unless the scaling hypothesis falters, this is a contest now of “not blinking first”. If you’re a big stack player like META, MSFT, GOOG, or any of the foundation model pure plays, you have no choice but to go all in — the prize and power of “winning” is too great. If you blink, you are left empty handed, watching someone else count your chips. It’s likely hundreds of billions will be destroyed, and trillions earned. For all of us in the startup ecosystem, among many things, it’s going to create a rolling thunder of AI opportunities. Read more here: https://lnkd.in/d5mfqgwk

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