Ari Caroline

Femtech is mostly bullshit. Bullshit spun by conference organizers, VCs, and corporations jumping on a conversation that is edgy but that is not turning into real-world transactions the hype would make you assume are happening. We have the data. And then there's those that do the work. Like Dr. Sioned Fôn Jones at BoobyBiome, Hannah Samano at Unfabled and Alice Pelton at The Lowdown. I had the privilege to moderate the panel Femtech Heroes: Untold Stories at Decoding the Future of Women conference this month. My goal was to energize those in the audience who wanted to start up their own Femtech companies. However, as the session progressed, we, for once, decided to tell real, raw stories instead of "focusing on the positive," as we tend to do in public speaking. Their stories about fundraising, managing real life with modest salaries, and being solo female founders were sobering, and I think some of the aspiring startup founders in the room walked out a bit less energized than when they walked in. And that may be a good thing. Our time, funds, and sanity are valuable. Think twice before you quit your job to become a Femtech founder. Again, data shows that the odds are not on our side. I ask us, Femtech founders and operators, to call on the bullshit we are hearing and getting so we can make real progress. I wish you all a good weekend with restful sleep, you know what I mean ❤️

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As the discourse shifts from models to compound AI systems / agents, we need better AI benchmarks to evaluate multi-modal and multi-step task performance, especially in healthcare and life sciences. When we wrote the first paper demonstrating ChatGPT's performance on the USMLE, we chose the US Medical Licensing Exam as a benchmark for accessibility, speed, and ease. This benchmark was never intended to represent AI model performance on real-world clinical tasks. Today, I still see so many research teams and startups using benchmarks (like the USMLE) that are ill-fitted for assessing the true clinical or scientific performance and utility of the models they are developing for real-world contexts. Benchmark development may be seen as a "less sexy" area of research, but it is of paramount importance. Years after the rise of the transformer, we still lack adequate benchmarks for so many single-step tasks in biomedicine. With compound AI systems (i.e., architectures that integrate multiple AI models to perform complex tasks) emerging, we need new benchmarks for agentic behaviors. I'd argue that developing an agent with novel capabilities without at least proposing a companion benchmark (if an industry standard does not yet exist) may hinder the adoption of said agent, especially for high-stakes workflows. Designing more benchmarks that capture/simulate real-world clinical and scientific workflows will help us mitigate the major discrepancies observed between in silico and in vivo performance and better support safe + effective deployment of AI in biomedicine. There are already brilliant people focused here, and we need more. DMs are open if you're researching or working in this area of multi-step/multi-modal benchmarking in healthcare and life sciences! #healthcare #ai #artificialintelligence #generativeai

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🚀 **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

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Drug discovery has been a hot area for startups and incumbents to apply AI/ML. I’ve always wondered what companies are doing to gain a competitive advantage. Data, of course, but how exactly? It’s always fascinating to see how different approaches are taken. One piece of data I always thought would be helpful for the drug design process is a very granular and robust understanding of patient experiences and symptoms with existing medications to better predict the interactions and behaviors of the molecular structure. What data is challenging to get or missing today but feasible and would make a huge impact in drug discovery? For more interesting conversations for AI in Pharmacy, join our sessions! We have one upcoming this Friday at 1pm ET with health system leaders from Yale New Haven Health and Cone Health. #AIinPharmacy #DrugDiscovery #Data 

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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! 📘💡

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LSI, BioscienceLA, #LAMedtechweek, and HLTH's #VIVE2024 event resulted in the latest GTM Pulse Check offering which has successfully taken off! As a result, I am continuing on last week's series of go-to-market tactics. Crafting a robust marketing plan is essential for medical device companies to thrive in today's world. By leveraging these outlined sections, organizations can optimize their marketing strategies and drive meaningful impact.This post takes a step back to outline the sections of a marketing plan. Read on 👇🏼 Looking for support in creating your marketing plan?  Let’s chat! Book here 👉🏼 https://lnkd.in/g92YA9WK _____ P.S. Enjoying my content? ⚕️Follow me for more tips on medical device commercialization and repost to share with your network. #medtech #gotomarket #medicaldevices #healthcareinnovation

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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

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Should you sell your practice to a VC-Backed MSO Startup? tldr: - new VC-backed startups are forming MSOs to roll up independent practices - The pitch is physicians receive early liquidity from the roll up and are granted ESOP shares that can be used for retirement - Problem is the use of ESOPs with independent practices is not a new concept and has been used with large group practices since the 70's - ESOPs do not grant full ownership and in order for the shares to be valuable for retirement, the practice needs to run the same if not better post roll up - With VC-backed startups being primarily operated by non-physicians with zero clinical or medical practice experience, the likelihood of this happening is extremely low making the entire concept high risk However, there is a viable path for continued ownership + liquidity for independent practices. Check out the article to learn more. ⤵ https://lnkd.in/gmGvTYwP

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Q1 2024 was big for Digital Health, with $1.1B in VC funding. A large portion came from major deals like $126M for Transcarent and $108.8M for Rightway. Despite some challenges in digital therapeutics and telehealth, investments in general care ($213.4M) and health benefits navigation ($140.7M) are promising. Read what’s driving the shift from direct-to-consumer to enterprise telehealth models here: https://ow.ly/uNYs50SpXGh

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AI is only as good as its input and training data, as well as the questions we ask of it. When we build new systems, it’s easy for existing limitations to elicit a suboptimal solution for a diverse population. #aihealthcare #aihealthequity #healthequity #ai #datadisparity #healthdisparities

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One key reason why healthcare technologies like AI, telemedicine, and software haven't structurally lowered costs or broadened access for the entire system after decades of experimentation (excluding pure financial engineering of payer contracts) is the limited reimagining of care delivery itself, with asynchronous telemedicine as a notable exception. In the same way that digitizing physical paper failed to exploit the capabilities of a new computing medium for word processing, hosting physician-patient conversations over video does not transform the actual delivery of care. In telemedicine, many of the inputs (e.g., clinician time) and therefore cost structures of in-person care persist, which is why we often see fee-for-service telemedicine companies struggle to achieve economies of scale (i.e., growing revenues non-linearly while growing costs linearly). Until we deconstruct the components of a medical appointment – from intake and triage, to chart review, history gathering, physical examination, diagnosis, treatment, and follow-up – we will continue to be underwhelmed by the disconnect between the transformative potential of these technologies and real-world patient/system impact. I’m optimistic that the emergence of reliable and performant medical AI agents will urge us to perform this deconstruction and redesign the fundamental building blocks of a “medical appointment.”

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Here’s your recap of last week’s health IT news 🗞️ 👇 🥼AI Clinician Assistant • Suki : The AI scribe announced an SDK and APIs enabling developers to integrate ambient documentation into their applications. • Apricot: The gen AI pre-seed startup focused on reducing documentation overhead for home health nurses launched. • Tampa General Hospital + Palantir Technologies: The health system and the software platform are partnering to develop an AI care coordination and decision support platform. ☎️ AI Contact Center • Mediktor + Sensely: The AI symptom triage solution acquired the conversational member engagement platform. 📋  Patient Administration • Oracle (Cerner) + Loyal: The EHR now offers integration with the patient engagement solution through its Healthcare Marketplace. • HubSpot: The CRM and marketing platform announced it will now offer HIPAA support and sensitive data tools. 🏥 Provider and Practice Administration • LeanTaaS: The AI-enabled facility management solution released updates including: mobile-first live view of OR schedule, predictive staffing automation, and a gen AI solution allowing decision-makers to glean staffing and scheduling insights. ⚕️ Clinical Operations • Keragon: The HIPAA-compliant automation platform launched out of stealth with a $3M funding round. 🧑‍💻 Virtual and At-Home Care • AvaSure + CLEW: The virtual care platform is partnering with the AI-powered remote patient monitoring and analytics company to improve patient outcomes. 🍎 VBC • Evolent + Machinify, Inc.: The VBC org acquired AI Authorization assets from the AI operating system platform.    • ProgenyHealth, LLC + Wildflower Health: The maternity and NICU case management solution is partnering with the women’s health VBC enabler to “help health plans fill gaps, collapse silos and integrate into existing clinical and operational workflows.”    • Health Catalyst + Carevīve: The population health data and analytics company has completed acquisition of the cancer care management solution. 🧠 Food for Thought: • This article provided an interesting look at Nebraska Medicine’s efforts to design a next-generation hospital room, including the four technologies they’ve selected to anchor their work. https://lnkd.in/em-u-a5v    • McKinsey released a survey of 200 global health system executives on their investment priorities. Among the notable findings, 88% listed AI as an area of high potential impact, yet only 40% have implemented any solution thus far and over 20% don’t have plans to invest in the next two years. https://lnkd.in/eK3CeqQ2

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I always read Rock Health's digital health reports as they give a nice pulse feel for what is happening in the digital health funding space. Good to see funds raised in H1 2024 suggest funding in the sector seems to have stabilized at 2023 levels. Its no surprise the report emphasis the buzz around AI and healthcare. There have been clear winners in the AI healthcare space in the area of process and cost optimization and the R&D space. The impact of AI in care and treatment delivery is so far much more muted. One area I never though to look at AI was in its ability to bring together multiple healthcare pathways and digital point solutions into a more holistic seamless experience for the patient. There is a clear trend away from using stand alone digital health point solutions, and could AI address this challenge by facilitating the efficient integration of disparate processes and technology into unified care programs. I can see this happening with digital health products but this will be more tricky to do with physical care programs involving clinicians and HCPs. “AI is helping us combine multiple parts of the care journey, across all care experiences. In the future, benefits navigation, clinical guidance, and care delivery won’t be delivered by separate companies or point solutions within the healthcare ecosystem. They’ll be features that work in concert within products, and they’ll happen in under 90 seconds for a seamless patient experience.” – Glen Tullman, CEO, Transcarent and Managing Partner, 7wireVentures

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Excellent post Emily Lindemer, PhD! The industry is spending a lot of time and energy on clinical applications of AI, and understandably so given the massive potential. But the near term opportunities, for all the reasons you point out, is in administrative applications. These are far less 'sexy' use cases for AI, but represent massive cost burdens and all of that value is flowing to the payers and providers (as well as the AI companies themselves) with little to no evidence that it is, or will, trickle down to consumers.

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https://buff.ly/3USEnmB The FDA is exploring the use of AI technology to predict and detect symptoms of Parkinson's disease. By utilizing artificial intelligence and machine learning models, they aim to establish effective methods for measuring Parkinson's symptoms using devices like wearables and smartphones. As the field of digital health advances, the FDA is examining how these technologies can gather health data beyond traditional clinical settings. Several wearable technologies have already received clearance to monitor Parkinson's symptoms, highlighting the growing importance of remote health monitoring. #FDA #ParkinsonsDisease

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How many healthcare use cases will (or should) require 100% accuracy to be trustworthy and effective for patient care? Just as we’ve seen with data science initiatives over the years, this is a tricky balance that will never be perfect and never be 100%. We need to set those expectations with physicians, administrators, staff, and patients. And that unattainable goal of perfection shouldn’t be the barrier to using #AI in #healthcare. #healthcareai #genai

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Article in today’s WSJ highlights the skyrocketing cost of healthcare bills for payors and patients. 4L FWA Prevention powered by Integr8 AI can detect these schemes to incrementally ratchet up claims cost in mear real-time. Stop abusive billing schemes when thet start. Find, Fight and Prevent FWA Fast!

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"People literally talk about how AI is going to cure diseases someday, and I think this is a very meaningful first step,' said OpenAI CEO Sam Altman" "Just as the introduction of the personal computer in the 1980's changed the way we work and live, AI is on a path to completely transform our everyday lives - and OpenAI is helping to lead the way." - Stéphane Bancel, Chief Executive Officer of Moderna The impact of AI, as it pertains to science and medicine, is incredibly underappreciated at this stage. Exciting times ahead. #AI #OpenAI #Moderna #ArtificialIntelligence

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