Christopher Paquette

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

49

2 Comments

Someone broke down the estimated costs of a $15 Sweetgreen salad. We need these cost graphics for digital health to help future founders. Especially for tech enabled cos, the greatest cost is labor. Much of the "value created" is in curation and matching, which means the gross margins are reliant on labor arbitrage. Pay someone $X, but get paid $X+Y% per visit. The challenge with healthcare is that you often have to pick one of two business models: 1. specialized, low volume, high cost, low gross margin % 2. generalized, high volume, low cost, high gross margin % I'd argue founders should focus on gross dollars over the lifetime of the customer (like Jeff Bezos said) instead of gross margin %'s, but we'll leave that for another time. The challenge is that many tech enabled digital health cos have high cost of goods sold (labor) and moderately high product, design, and engineering budgets. We must build differentiated solutions for patients, clinicians, and clients to be out incumbents, however, we're realizing that companies are ultimately valued on the discounted value of future cash flows. And the high SG&A costs at most organizations might be inbalanced to the unit economics of the business. I'm not sure the math pencils out for everyone unfortunately. But I'm just a guy on the internet, would love to hear the thoughts from those smarter than I (you!). Comment below. --- p.s. I have no idea the accuracy of the graphic and not an investor in Sweetgreen. Cheers. Credit: David Crowther

113

27 Comments

AI-Driven Biostatistics: Revolutionizing Clinical Trials 📢 Exciting Announcement! 📢 I am thrilled to introduce my new book, "AI-Driven Biostatistics: Revolutionizing Clinical Trials", now available on Kindle! 📚✨ In the rapidly evolving landscape of clinical research, the integration of artificial intelligence (AI) is transforming how we conduct and analyze clinical trials. This book delves into the immense possibilities of AI in biostatistics, offering insights that can significantly enhance the efficiency, accuracy, and depth of clinical trial analyses. 🌟 Key Highlights of the Book: Enhanced Data Analysis: Learn how AI algorithms can process vast amounts of clinical data swiftly and precisely, uncovering patterns and trends that might be missed by traditional methods. Predictive Modeling: Discover how AI can predict outcomes and patient responses, enabling more informed decision-making and personalized treatment plans. Automated Processes: Explore the automation of repetitive tasks, such as data cleaning and preliminary analysis, allowing biostatisticians to focus on more complex, value-added activities. Real-time Monitoring: Understand how AI-powered tools can provide real-time monitoring and adaptive trial designs, improving the flexibility and responsiveness of clinical trials. Ethical and Regulatory Considerations: Gain insights into the ethical implications and regulatory landscape of using AI in clinical trials, ensuring compliance and ethical integrity. Why This Book? Comprehensive Coverage: Whether you are a seasoned biostatistician or a clinical researcher, this book provides a thorough understanding of how AI can be leveraged to improve clinical trial outcomes. Practical Insights: With real-world examples and case studies, you'll see practical applications of AI in biostatistics, making complex concepts accessible and actionable. Future-ready Skills: Equip yourself with the knowledge and skills to stay ahead in the rapidly advancing field of clinical research. Join the AI Revolution in Biostatistics! As we stand on the brink of a new era in clinical research, the potential for AI to revolutionize biostatistics is immense. This book is not just a guide but a comprehensive resource to help you harness the power of AI in your work. 📖 Get your copy today and embark on a journey to the future of clinical trials! Let's shape the future of biostatistics together! #AI #Biostatistics #ClinicalTrials #HealthcareInnovation #NewBookRelease Feel free to share your thoughts and experiences in the comments below. I look forward to engaging with you all and discussing the incredible impact of AI in our field! Thank you for your support and happy reading! 📘💡

6

1 Comment

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

50

6 Comments

🚀 **Unleashing the Power of Federated Data in Computational Biology** 🚀 Leaders in Biopharma and Biotech, it's time for a paradigm shift in how we approach data management. Fragmented data, duplicate data vendors, and persistent bottlenecks are stifling innovation. The solution? A comprehensive Federated Data Set. Here’s why: 🔍 **Tackle Fragmented Data:** - Seamless integration across diverse datasets. - Enables holistic insights by bridging the silos. - Achieves higher data consistency and reliability. 💡 **Eliminate Duplicate Vendors:** - Streamlines data sourcing processes. - Reduces redundant contracts and costs. - Simplifies vendor management while maintaining high data quality. ⏱️ **Resolve Data Bottlenecks:** - Speeds up data retrieval and analysis. - Facilitates real-time data access and sharing. - Enhances collaboration across research teams and departments. 📈 **Benefits of Federated Data in Computational Biology:** - **Holistic Insights:** Combines data from genomics, proteomics, and clinical trials for a holistic view. - **Accelerated Innovation:** Faster analysis leads to rapid hypothesis testing and drug discovery. - **Enhanced Collaboration:** Promotes cross-functional teamwork, driving efficiency and groundbreaking results. - **Regulatory Compliance:** Ensures data governance standards are met effortlessly. 🚨 **Call to Action:** - Embrace federated data strategies to stay ahead in the competitive landscape. - Partner with industry leaders to implement best practices. - Invest in technologies that streamline data integration and accessibility. Let's transform how we harness the power of data in Computational Biology. The future of biopharma and biotech innovation relies on our ability to manage and leverage data efficiently. #biopharma #biotech #computationalbiology #Innovation

3

new post - a few thoughts on AI in healthcare: https://lnkd.in/eGjqDtaj -OpenAI and how AI healthcare companies can build defensibility -How AI will create new types of data and what they might be -Why AI in healthcare probably won’t reduce costs + an example

13

🔍 𝐅𝐢𝐧𝐝𝐢𝐧𝐠 𝐏𝐫𝐨𝐝𝐮𝐜𝐭-𝐌𝐚𝐫𝐤𝐞𝐭 𝐅𝐢𝐭: 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐟𝐫𝐨𝐦 𝐒𝐞𝐪𝐮𝐨𝐢𝐚'𝐬 𝐉𝐞𝐬𝐬 𝐋𝐞𝐞 🔍 Sequoia's Jess Lee on identifying product-market fit for early-stage startups: - Understand customers' needs deeply. - Iterate based on feedback. - Solve real problems. - Observe user engagement. - True product-market fit: Consistent user returns and recommendations. #StartupSuccess #ProductMarketFit #Entrepreneurship #CustomerFeedback #Innovation #Sequoia

5

🚀 𝐔𝐧𝐥𝐨𝐜𝐤 𝐒𝐭𝐚𝐫𝐭𝐮𝐩 𝐒𝐮𝐜𝐜𝐞𝐬𝐬 𝐰𝐢𝐭𝐡 𝐔𝐫𝐢 𝐋𝐞𝐯𝐢𝐧𝐞’𝐬 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬! 🚀 Uri Levine, co-founder of Waze and Moovit, shares invaluable lessons in his book “𝘍𝘢𝘭𝘭 𝘪𝘯 𝘓𝘰𝘷𝘦 𝘸𝘪𝘵𝘩 𝘵𝘩𝘦 𝘗𝘳𝘰𝘣𝘭𝘦𝘮, 𝘕𝘰𝘵 𝘵𝘩𝘦 𝘚𝘰𝘭𝘶𝘵𝘪𝘰𝘯.” Key takeaways include the importance of passionately embracing the problem you’re solving, the necessity of customer retention as a marker of product-market fit, and the strategic use of pitch deck slides. He also emphasises the significance of hiring and firing correctly and observing user behaviour to refine your product. These principles can transform your startup journey, ensuring resilience and growth. 🌟 #StartupSuccess #Entrepreneurship #ProductMarketFit #Innovation

5

VC funding in healthcare has been more resilient than in other industries. Here are some key takeaways from recent Carta data: 👉 At the priced seed, Series A, and Series B stages, dilution for health startups has noticeably decreased, while dilution for SaaS startups has stayed flat or increased slightly. 👉 In priced seed rounds, median dilution for healthcare companies dipped to 14.2%, compared to 20% for SaaS.  👉 The frequency of bridge rounds surged in the health sector in Q1, jumping to 44.3% from 36.9% in Q4 2023. This is the first significant increase in bridge rounds in healthcare over SaaS since early 2022. 👉 More founder-friendly terms for health startups may result from enduring VC interest since COVID. Healthcare has remained strong through economic turmoil, enabling higher valuations with smaller round sizes. What are the implications? The healthcare sector might see a rise in new ventures due to favorable investment conditions and lower dilution rates. The surge in bridge rounds suggests that certain sectors within healthcare face unique challenges that require interim financing solutions. Founders and investors in the healthcare space- what are your thoughts on this? I’d love to hear from you. If you know a founder innovating in healthcare, tag them in the comments! The Artemis Fund is especially interested in groundbreaking care tech companies. You can check out Carta’s full blog linked in the comments. If you haven’t read our latest blog on care tech, you’ll find that there too. #healthcare #caretech #fundraising

21

3 Comments

💥Google's new Med-Gemini for healthcare outperforming GPT-4 Med-Gemini is a family of multimodal AI models built upon their Gemini models, specifically designed for the healthcare industry. From what I've read, these models are built on Google's existing Gemini AI architecture, but they've been specifically designed and trained for medical and clinical applications. It can process long-form medical records and research papers, and it can output text, images and videos to help explain things. Google is claiming that Med-Gemini has even outperformed OpenAI's powerful GPT-4 model on certain medical reasoning tasks, which is pretty impressive. On standard medical benchmarks like the MedQA exam, the Med-Gemini-L 1.0 model has scored really high, beating out even Google's own previous Med-PaLM 2 model. One cool feature is that Med-Gemini integrates web search, so it can pull in reliable information from the internet to provide more nuanced and factual responses. The models aren't publicly available yet - Google is still working on improving them and making sure they adhere to responsible AI principles around privacy and fairness before they release them. But from what I've seen, Med-Gemini is shaping up to be a really powerful set of medical AI tools.

1

Just over a year ago, Imran Mahmud, MD introduced to me what was then called, Co:Helm. I was taken by the backgrounds and stories of Abdel Mahmoud, MD, Zahid Mahmood & Tahseen Omar and moreso their grit and determination to forge ahead with their mission of reducing the cost of healthcare. They soon got into the Sequoia Arc program with the backing of another friend, George Robson. And have been staying low and building ever since. Inbetween rebrandng to Anterior (formerly Co:Helm). And even more recently announced a new round to add more fuel. https://lnkd.in/e3V4qrRz Happy to be a supporter alongside Annelie Ajami, Dorothy Chou, Adil Mohammed, Andy Young, Caroline Mogford, Christian Arno, Felix Leuschner, Freddy Kelly, Leonard Picardo, Mandeep Singh, Michael Steel, Omid Ashtari, Noor Shaker, Sultan Murad Saidov, Yuelin Li and others in the ecosystem... [Anterior is giving clinicians a second set of hands—and fixing one of the biggest pain points for patients. It is a LLM-powered co-pilot for administration, handling all the work that comes before the clinical decision-making. Where it might take a nurse several hours to track down, organize and summarize hundreds of pages of records they need, Anterior can do it in seconds, allowing clinicians to increase their output tenfold and focus on what matters most: helping patients.] #healthcare

64

There is a lot of attention to the foundation models for text but there is a revolution in many other areas driven by foundation models Drugs discovery, startup gets 1 billion in funding Weather forecasting foundation models: Extreme weather forecasting by Google, GraphCast system Prithvi, 100M NaATA/IBM model, various weather tasks on geosat images FengWu, atmospheric dynamics and predict the future land and atmosphere states at 37 vertical levels on a 0.25° latitude-longitude resolution ClimaX, UCLA, MS, 2023 FourCastNet (2022), short to medium-range global predictions at 0.25∘ resolution, extremely fast inference  W-MAE,  2023, UESTC, etc PanguWeather, 2023, Huawei Cloud CLIMATEBERT <- text processing for weather Transportation foundation models TransWorldNG by CAST Lab, S. A. C. Labs, 2023 TrafficGPT, (traffic foundation models + chatGPT), Transport policy May 2024 TengYun, CASIA etc, 2022

8

2 Comments

Solve for information asymmetries! Architecture & Civil Engineering - Layout interpretation ~other industries: Healthcare - <Prescription> interpretation Financial Products - <Subject to market risks documentation> interpretation Loans - <Fine print> interpretation There’s going to be a slew of startups commercialising this year with a value prop of simplification using AI

5

AI is not slowing down, and the scaling laws continue to hold. Claude 3.5 Sonnet is now probably the most powerful model available, but Sonnet isn't even Anthropic's biggest model—Opus is. A particularly impressive number here is 59.4% on GPQA diamond questions. GPQA is a benchmark of PhD-level questions for which you can't Google the answers, and they don't make the data available online for people to easily train on, so the contamination risk is lower. In November 2023, GPT-4 scored 38.8% on GPQA with few-shot prompting. Claude 3.5 Sonnet gets 59.4% with 0-shot prompting (while GPT-4 has improved, with GPT-4o, to 53.6%).

2

I am very excited to share the pre-print study on Atropos Health ChatRWD beta results(link to paper below). ChatRWD beta outperformed ChatGPT 3.5, Google Gemini 1.5 Pro. and Anthropic Claude 3 on answers to clinical questions when reviewed by independent physicians. OpenEvidence also outperformed the Big Tech LLMs by a wide margin. Most important to us, ChatRWD performed highest on Physician trust and Best Answer and was able to answer questions asked 94% of the time. As the Gen AI cycle continues to move at a rapid pace we firmly believe that "Trust" and "Accuracy" are going to be critical, and that publishing your performance on both is table stakes. It is also clear to me that in the quest to be able to answer 100% of questions with Clinical grade accuracy training LLMs solely on the literature will not be enough. We need to produce new evidence in the form of high-quality transparent studies using appropriate methodology. We are adding beta users post this announcement, so please sign up to try it, and we will have more to come on full launch this summer. Exciting times and hope everyone has a great July 4th. https://lnkd.in/es5gWK84 #generativeAI #LLM #Realworldevidence

215

11 Comments

#ObsessivelyEnthusiasticInvestor #UnreasonablyEnthusiasticInvestor #IrrationallyEnthusiasticInvestor at REFASHIOND Ventures: The Industrial Transformation Fund. We look for the people we believe are doing the best work in Industrial Transformation & Supply Chain Technology; Attempting the impossible. Then we go all-in on them. With obsessive and unreasonable enthusiasm. Join us. #IndustrialTransformation #SupplyChain #Innovation #Technology #Startups #VentureCapital #DemandTheImpossible #UnreasonableEnthusiasm #ObsessiveEnthusiasm #IrrationalEnthusiasm 

6

“But let's not kid ourselves: we need data engineers, SUBJECT MATTER EXPERTS, and people who UNDRSTAND #ONTOLOGIES to build the Knowledge Graphs that will feed the #AI Assistants.” If you need a healthcare data SME who understands ontologies to support your AI endeavors, DM me. I’d love to put two decades of experience in these areas to good use!

8