Ambika Acharya

🎬 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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Typically, our earnings announcements at Recursion come with a (L)earnings call – but today, we’re going to wait (rare for us, I know) as we move more quickly than expected toward the closure of our proposed business combination with Exscientia in just about two weeks! This is such an important moment for Recursion and for TechBio -- bringing together two incredibly complementary AI-driven drug discovery leaders to rapidly scale up our end-to-end capabilities so that we can deliver more, better medicines to patients, faster. On that note, stay tuned for a special (L)earnings call, live from Oxford, after the expected close of our business combination on Nov. 20. In the meantime, there are a number of other exciting updates I want to highlight from Q3: 1) Successful clinical trial milestones, including encouraging topline data for our Phase 2 trial in CCM, the first patient dosed in our Phase 2 trial for recurrent C. difficile infection, and IND clearance for our Phase 1/2 trial in biomarker-enriched solid tumors and lymphoma (targeting RBM39) 2) The delivery of our first neuroscience phenomap, optioned by Roche-Genentech for $30 million as part of a fee structure that could exceed a total of $500 million across multiple maps before program-specific milestones or royalties 3) Expansion of our collaboration with Google Cloud, including exploring generative AI capabilities and Gemini models, to support the RecursionOS – part of our broader expansion of tech partnerships. Get the full details here: https://lnkd.in/eYvcE45k

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“Over one million new articles are added to PubMed annually—about two per minute. No physician can keep up.” This highlights a challenge that PCPs face daily, managing countless chronic and acute conditions while adapting to ever-evolving clinical guidelines. We can’t expect our clinicians to act like machines. At RhythmX AI (https://rhythmx.ai), we address this by revolutionizing primary care with our generative AI-native precision care platform, developed collaboratively by our physicians, specialists, and AI teams. Our platform integrates advanced AI and longitudinal data to deliver patient-specific, evidence-based recommendations directly within clinical workflows. These hyper-personalized insights help physicians make informed decisions at the point of care, bridging the gap between research and practice. Trust is at our core. Our physicians rigorously validate every recommendation to ensure clinical-grade reliability, empowering clinicians to confidently rely on the insights we provide. #PioneerPrimaryCare #Hyper-PersonalizedCare #PCPBurnOut #HealthAI #ResponsibleAI

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Pathways to Tech Week #2 at  Headstarter: Being surrounded by people who are building amazing software and companies is a truly inspiring experience. My favorite speakers were: • Tariq Ahmed: First Engineer at AppLovin ($30Billion IPO) • Julian Alvarez 🚀: CEO of Wisdolia • Hamza Ali: CEO of Olostep  Here's what I have learned from them: 𝟭- "𝗙𝗮𝗶𝗹𝘂𝗿𝗲 𝗮𝗻𝗱 𝗽𝗶𝘃𝗼𝘁" Tariq, one of the first engineers at AppLovin, shared that AppLovin tried several things in their early stages that failed. However, the company had incredible success when it pivoted to mobile advertising; They didn't have expertise in the field. Today if you've experienced in-app advertising, AppLovin was likely behind it. 𝟮- "𝗦𝘁𝘂𝗱𝘆 𝘆𝗼𝘂𝗿 𝘂𝘀𝗲𝗿𝘀, 𝘆𝗼𝘂'𝗹𝗹 𝗯𝗲 𝘀𝗵𝗼𝗰𝗸𝗲𝗱 𝗮𝘁 𝗵𝗼𝘄 𝗺𝘂𝗰𝗵 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗹𝗲𝗮𝗿𝗻." Julian runs Wisdolia a startup aimed at helping college students actively study. By simply asking what types of students they were. His team found that medical students were the ones the ones who converted the most into paying customers. It seemed evident because they were the most overwhelmed by the amount of information they had cram. But the evidence in their face was a lightbulb moment for Wisdolia. 𝟯- "𝗦𝗰𝗿𝗮𝗽𝗽𝗶𝗻𝗲𝘀𝘀 𝗮𝗻𝗱 𝗶𝗻𝗶𝘁𝗶𝗮𝘁𝗶𝘃𝗲 𝘄𝗲'𝗹𝗹 𝗴𝗲𝘁 𝘆𝗼𝘂 𝗳𝗮𝗿." Hamza, CEO of Olostep, describes his company as "a company that enables AI agents to browse the web just like humans". I was fascinated when I heard that, and as the Olostep team acquires more customers they value scrappy and people that initiative on board. Those qualities set engineers apart when building successful products.

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We're trying a new thing: Company spotlights to help people better understand what various health IT vendors actually do 🔦 We’ll be cutting through the marketing speak to give short, to-the-point descriptions of a company’s product and/or service + some context on how they see their differentiators. Tag any companies in the comments you'd like to see us cover (and follow Elion if you want to stay in the loop!)

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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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🎙 Episode 43 of Machine Minds with Bob Rogers, CEO & Co-Founder of Oii.ai is live! Bob's background speaks so itself so imagine what the insights from the episode are like! Background: - PhD in Physics - Expert in residence at UCSF who helped build the first FDA-cleared AI product that saves lives every day - Member of the Board at Harvard IACS - Led Data Science and AI for Intel - Was the Co-founder and Chief Scientist of an acquired healthcare AI company - Co-founded and ran a quantitative hedge fund - Is currently a Chief Scientific Advisor for two companies and a Co-Founder of one of them - Authored 4 books... And I'm sure there's more I'm missing. Check out the full episode wherever you get your podcasts to hear all about Oii as they just came out of stealth! #machineminds #techpodcast #ai #supplychain #startups

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As the CEO of an AI-driven health-tech company, Labops, I’m constantly evaluating how technology can create the most impact. While Large Language Models (LLMs) are making headlines, I believe Small to Medium-sized Language Models (SMLs) deserve more attention—especially for businesses operating in niche domains like healthcare. Why do I say this? 1️⃣ Tailored Solutions: In healthcare, where precision and specificity are critical, SMLs (40–50B parameters) are more adaptable to domain-specific needs. 2️⃣ Cost-Effectiveness: For startups, running and fine-tuning a SML is more economical than relying on large, resource-intensive LLMs. 3️⃣ Ownership & Flexibility: Open-source SMLs allow businesses to build and deploy models on their terms, giving greater control over data and performance. At Labops, where we’re building solutions to transform diagnostic services, the ability to leverage AI that aligns with our unique requirements is crucial. SMLs strike the right balance between scalability and practicality, making them a natural fit for startups like ours. LLMs have their place, but they’re not always the answer for every use case—especially in sectors where focus and efficiency matter more than sheer size. If you’re in healthcare or another specialized industry, I encourage you to explore the potential of SMLs. They might just be the competitive edge you’re looking for. What’s your perspective on SML vs. LLM? Let’s discuss! 💬 #AI #HealthTech #LLM #SML #Startups #Innovation #Labops

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🎯Data was, is, and will be the only fuel. Analogy with the fossil fuel is a good one, since the data we have today is as dirty as oil and coal. What big tech companies tried is to put a lot of "garbage" data, and what they did get was fantastic. Yet, we are at the point of diminishing returns. Data is the beginning and the end of this cycle.” Great thread, particularly this comment 👆from Ilija Lazarevic

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CBO Interviews: Mladen M. Hoyss on Shaping the Future of Nothing OS | https://lnkd.in/eFyYx9n6 Getting the chance to talk with Mladen was a surreal opportunity, and I’m truly grateful to the Nothing community for choosing me to be their voice. It’s moments like these that bring my journey full circle. My history with Mladen’s work goes back to the early days of Blloc. While I couldn’t use the Zero18 phone, I’ve been following Blloc since the beginning. I got into the closed beta testing of Ratio, their minimalist launcher. At that time, I was heavily addicted to my smartphone. Ratio had a profound impact on my usage habits, significantly reducing my screen time. I was so impressed that I continued using it across three to four different phones, often proudly sharing screenshots of my reduced screen time on social media. When I heard about Mladen M Hoyss joining Nothing I thought, “This is a collaboration that was bound to happen.” The transition from Ratio to Nothing OS has been an evolution in my smartphone experience. It’s fascinating to see how Mladen’s philosophy has been integrated into a more comprehensive and fun environment at Nothing while still maintaining the core principles of mindful technology use. This interview has not only provided insights into Nothing’s design philosophy, but also allowed me to connect the dots in my personal journey with mindful technology use. I’m excited to see how Nothing continues to evolve and grateful for the opportunity to be part of this community-driven conversation about the future of technology. #NothingCommunity #NothingOS

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I’ve spoken to 50+ CMIOs/CIOs over the past year. Here are 5 thoughts about AI Scribes I'm hearing that many people are NOT talking about👇 1️⃣ Distribution matters more than Product. Most are just buying Nuance because it’s easier to buy. It’s a known entity. It’s an add-on to the Microsoft contract which means less internal hoops. Key question for who wins long-term: will the startups get distribution faster than the incumbent gets innovation? Will the EHRs eventually just release their own native AI scribes? (my belief is yes… which Oracle/Cerner has already done). 2️⃣ There is a clear “phenotype” for the clinicians who actually use AI Scribes: it’s clinicians who don’t care to have control over how their notes look and sound. But for those who really care about controlling the output? Those folks will keep writing their own notes. 3️⃣ The impact of AI scribes on medical education and trainees is very unclear. Some believe trainees should get exposure to AI scribes early on. Others worry that trainees who don’t learn to write notes are going to miss a critical skill that helps clinicians think. And what happens if there’s a cybersecurity incident and the EHR goes down? Will future clinicians not know how to document manually? 4️⃣ ROI is still hard to truly measure. Few see any improvement in “productivity” (i.e. more patients seen). The real value is in improving clinician well-being. However it’s hard to prove AI scribes are lowering staff churn since that is a lagging indicator many steps away and affected by many other factors. 5️⃣ The per clinician pricing model used by most vendors breaks down at scale. If I have clinicians who would only use the AI scribe for 30% of their patient encounters, am I going to buy a license for them? Probably not. What other less talked about learnings or insights are you hearing behind the scenes?

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Today marks a milestone in a journey that began during one of the most uncertain times in our lives. I’m thrilled to announce that Axios broke the news that SuperTruth has acquired imaware and emerged from stealth. In 2020, amidst quarantine and the shift to remote work, I met Bobby Hill —an incredible human being and a brilliant partner who shares my vision for how healthcare can and should evolve. Together, we founded SuperTruth, a first-of-its-kind holding company dedicated to using data as a force for good. SuperTruth is built on an invention I developed—a groundbreaking approach to data orchestration that enables healthcare systems and enterprises to use AI ethically, securely, and equitably. This invention ensures that data remains private, accessible through consent, and optimized for equitable use, unlocking the full potential of AI while safeguarding individual rights. This innovation is the foundation of SuperTruth’s mission: transforming data into a force for good and addressing some of the most pressing challenges in health and wellness. One of our first significant steps toward realizing this mission was the acquisition of imaware, a leader in at-home health testing. By breaking down barriers to advanced diagnostics, imaware empowers people with actionable insights into their health—an essential building block for personalized, data-driven care. Much like my work with MPOWERD Inc., which brought light to millions while I was working full-time, SuperTruth is another labor of love. I spend every waking moment outside my day job thinking about how to improve healthcare for everyone, everywhere. My role as Chief Technology Officer at Momentum Worldwide remains my highest priority. I am privileged to work with extraordinary teams daily, driving innovation and crafting transformative experiences for some of the world’s greatest brands. The same curiosity, drive, and ethics that fuel my work at Momentum also power the vision for SuperTruth, guiding it as co-founder and advisor. SuperTruth is the culmination of hard work, collaboration, and a shared dream: creating a world where data works for people—not the other way around. With imaware now part of our journey, we are starting to build tools that will truly transform healthcare. The journey is just beginning, and I’m excited to share more as we take steps toward a future where technology improves lives at scale. Together, we can create a world where healthcare is more equitable, ethical, and effective.

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🤔 A Fascinating Question: How could LLMs transform medical coding? Here's a data-driven hypothesis worth exploring... The Challenge: Medical coders today spend hours translating clinical notes into ICD-10 codes, juggling multiple reference guides and databases. It's like trying to translate a book while simultaneously checking three different dictionaries. Here's what an LLM-powered solution could look like: Think of it as a smart highlighter that could: - Extract key medical terminology from clinical notes - Map common diagnoses to preliminary codes - Create documentation checklists Based on current LLM capabilities and publicly available healthcare data, we could potentially see: - 25-30% faster processing for routine cases - Improved documentation completeness - Reduced lookup time for common codes The Technical Framework: - Fine-tuned LLM on medical terminology - Integration with existing systems - Human oversight for complex cases Why This Matters: For a hospital processing 200 records daily, even a 20% efficiency improvement means 40 more patients getting faster care. It's not about replacing human expertise - it's about augmenting it. 💡 Key Insight: The real value isn't in automation, but in reducing cognitive load. Like having a knowledgeable assistant who handles the initial research while experts focus on critical decisions. Question for healthcare professionals: Which parts of the coding workflow would benefit most from this kind of AI assistance? #HealthTech #AI #Healthcare #Innovation --- What specific challenges would you want this system to address first? Share below 👇

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Ilya Sutskever confirmed at NeurIPS '24 pre-training scaling law will not be the same. Internet data, the AI fossil fuel, is running out. It is not the end of the AI hype, the rapid rise of foundational AI has outpaced our ability to fully grasp their long-term societal impact. AI agents and applications are poised to transform every facet of our lives, and we will see it happening in coming few years. There will still be improvements in model architecture, and synthetic data creation, so models will improve, just not at the lightning speed they did till now. Looking forward, the single most important milestone in AI is creating systems capable of advancing themselves—AI researchers that can innovate and improve foundational models autonomously. We need to assess how far are we from this stage.

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Created an AI assistant app to connect patients and doctors using Vercel AI SDK, Assistant-UI.com components, function calling, thread suggestions, check it out (Guillermo Rauch, Simon Farshid, Kajari Ghosh)

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🚨🚨 🚨 Unlocking Innovation: The Power of the Adjacent Possible Innovation programs can stall when the team tries to ideate way beyond their collective perspective and frame of reference. Like a software engineer brainstorming genetic modification of mycelia, or a senior executive ideating process improvements which they haven't been participated in for decades. It's challenging, exhausting, and ultimately self-defeating. Enter the concept of the "Adjacent Possible" - a game-changer for innovation. What is it? The Adjacent Possible, coined by Stuart Kauffman, represents the realm of new ideas within reach based on our current knowledge, resources, and capabilities. Why it matters: ➡️ Realistic Innovation: it focuses on achievable advancements, not far-fetched concepts. ➡️ Incremental Progress: each innovation opens doors to new possibilities. How to leverage it: • Constantly explore the boundaries of your adjacent possible. What's in your line of sight is what you know best. • Combine existing ideas in novel ways. Even small perturbations from status quo can generate large innovation leaps. • Stay curious about developments in adjacent industries. Cross-domain patterns create fresh sources of ideas, while also expanding the adjacent possible. Most of all, it takes the 'genius' pressure off the team. No one knows everything, but each one of us knows a lot about something, and we can all drive plenty of innovation potential by focusing on the spaces we know best. Are you exploring your Adjacent Possible? Share your thoughts below! 👇 #Innovation #innovationsystems #AdjacentPossible

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This one will hit close to home for all my Life Sciences folks. 🚨 Just watched our AI Agents' speed-run Quality Incident Analysis... Ever feel like Initial Quality Reports move at the speed of cold honey? We just discovered something wild in our AI experiments that had our team buzzing! 🐝 (No spoilers on the exact findings...yet) Early tests are showing we could: - Cut investigation time dramatically - Spot broader system trends faster - Give quality teams their nights back! 😴 My take: In less than 18 months, waiting days for quality incident insights will feel as outdated as dial-up internet. Not replacing quality expertise - just helping experts work at the speed of thought! 🚀 What quality investigation bottleneck would you love to eliminate? 🤔 #PharmaInnovation #Quality #Manufacturing #AI #Excellence

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Bias is still a big problem with LLMs. They’re trained on content created by humans after all… And human’s we’re… well… “really good at generalizing” to put it nicely… 😬 The fallout is that you need to be very careful when you’re asking AI to give you any kind of subjective output. Let’s look at a community example. Imagine you want to create a weekly digest of the best discussions that took place in your community. (Now you could reach out to us at Knobi to build/host/run that process for you! 😉 But let’s say you want to do it yourself.) You could pass multiple threads to an LLM and ask it to pick out the “best” for you. But you’ll find that even a task as simple as picking a “good convo” is going to fall prey to some bias. A conversation - for example - with incredibly interesting content might not be chosen if it also has a lot of typos or heavy use of slang. At worst you’re going to end up suppressing certain voices... ...and at best you still risk missing great content for dumb reasons. So what do you do? Ask for facts. Define what you mean by a good conversation. And ask the LLM to answer questions relevant to your definition and your community, like: - Is there industry news shared in this thread? - Did multiple users share perspective in this thread? - Did the OP thank someone for their contribution? etc… And rank content that way. (Note: Langchain’s “structured output chains” are one easy way to do this). We’ve got a long way to go re: bias in AI. And there are some cases where that's still too much of an issue to implement. But in the right context we can mitigate it. And we're making progress.

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