Charles Onu

Presenting at the Senate as part of the Coalition for Health AI (CHAI) "Day on the Hill" wasn't on my 2024 bingo card, but I'm feeling energized by the bipartisan discussions on healthcare AI. The numbers tell a compelling story: while nearly 2,000 healthcare AI startups have received funding over the last two decades, only 1 in 20 provider-facing companies reach late-stage maturity. Beyond natural market dynamics, two critical barriers are holding back AI's full potential in healthcare: unsustainable payment models and inadequate evaluation infrastructure – only 5% of research studies on LLMs in medicine utilize real patient care data, and studies advancing our understanding of performance beyond medical licensing exam benchmarks remain anemic at best. At CHAI, we're advocating for essential foundations: • Rigorous technical model performance evaluations • Comprehensive bias assessments across diverse patient populations • Thorough pre-deployment simulations and prospective studies • Robust ongoing local monitoring The path forward demands strong reporting infrastructure, advanced healthcare AI benchmarks, and meaningful public-private partnerships to drive real-world AI deployment in healthcare. Stay tuned for CHAI's upcoming initiatives in 2025 as we expand our engagements with the startup and investment ecosystem. https://lnkd.in/enw-GQcA

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This is why HER Heard is creating a new #data lake entirely with patient generated data called patient reported outcomes. This allows women to use own data, not just flawed, mislabeled, incomplete, fake (synthetic) data for “insights.” #AI is only ever as good as the data it is fed. #Patientengagement is itself a valid business model. A byproduct of #patientcenteredcare is new patient information or data. That is our true value proposition. I had been seeing this trend and knew I could not rely solely on others’ data nor be forced to buy all the data we needed. We needed to create and own our own data lake, doing so with the appropriate patient choice, #ethics, and opt in. Women need to be the primary beneficiaries of their own data, rather than treated like widgets to move along assembly line healthcare. By engaging with the end user directly we can center her, offer her choices per her preferences, and allow her data to benefit her own #health outcomes via #personalizedmedicine.

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🚀 Exciting News! With Med, we just applied for the SPC Founder Fellowship in Fall 2024, and we highly recommend that other early-stage startups do the same. The SPC Fellowship offers an incredible opportunity to connect with a vibrant community of founders, gain valuable mentorship, and accelerate your startup’s growth. If you’re an early-stage founder looking to take your venture to the next level, don’t miss out on this chance. Check out their blog in the comments to learn more about the fellowship and how to apply. #startup #entrepreneurship #innovation #fellowship #SPC #earlystage

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Generally surprised to not see more hardware vendors in healthcare and life sciences partnering with AI-first companies. When the zone of genius of a company is hardware, it's difficult to imagine that same team achieving parity on an AI product – Apple may be the greatest example of success in this realm, and still has secured many AI capabilities via targeted M&A (Siri, Turi, Xnor.ai, Voysis, Inductiv). Given the importance of leveraged distribution models in the ossified industries of healthcare and life sciences, it seems there is untapped opportunity for more hardware manufacturers to partner and co-develop AI products with startups. Control over the way data is captured and processed is critical for building high quality training data sets, which creates an interesting feedback loop between hardware companies and potential AI-first startup partners. Interested to explore this further with folks in healthcare and life sciences hardware!

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This is a fascinating article suggesting LLMs can help providers be more compassionate in patient text messages. I know when I'm busy and over-worked I often forget to add the human touch to my own messages. At Videra we have been exploring how to help providers communicate more effectively with more compassion and empathy. #DigitalHealth #PatientCare #HealthTech

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"So much of what we do in the healthcare industry is constrained by the way we share medical records," says Salman Haque, Co-CEO at Medsender. "Unstructured data creates a lot of additional work for patients, providers, and everyone in between. Our solution transforms communication from a pain point into an advantage." "As a startup, we prioritize speed, and the ability to train and deploy new models quickly allows us to solve more problems for the healthcare professionals we work with," Tohmaz says. "If every experiment took weeks, it wouldn't be scalable. With Vertex AI, we can train, deploy, and test a model in a couple of days." Salman Haque https://lnkd.in/eSQNr2ki

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Fantastic progress Hadi Javeed and RevelAi Health team. Your aspiration to realize fully autonomous AI agents is great. Healthcare ecosystems are complex. Even after developing these agents, establishing confidence in them would take a while. Human-in-the loop, continuous monitoring, and revisions would be indispensable. You have rightly called out in your blog "By involving healthcare professionals in our iterative processes, maintaining a human-in-the-loop approach, and building strong feedback mechanisms, we aim to de-risk our systems while maximizing the value they bring." #healthcareTech #innovation #AI/ML

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Clinical AI: The paradox of a Contender and Pretender  I've been part of the advanced visualization industry since 2005, fresh out of grad school, when I began working with TeraRecon. I embarked on developing business outside the US and Canada with their advanced visualization portfolio, closely collaborating with our CEO at the time. My extensive travels made me the highest-ranking Delta Airlines passenger by 25, as TeraRecon's Aquarius was adopted in over a dozen countries, many less affluent than the US. It was mind-boggling how these countries could afford the technology, but the reason was simple: it fulfilled a need that nothing else could. Once set up in a demo environment in under 30 minutes, users couldn't imagine working without it. It was fascinating to witness how TeraRecon's Aquarius technology captured global markets, navigating the absence of reimbursement codes, and effectively competing with major vendors like Siemens, Philips, Toshiba (back then), and GE's workstations. Why did it succeed? The answer is straightforward. Aquarius technology provided an immediate workflow solution for multiple users through remote streaming technology, allowing even basic hospital computers to connect to a central server for instant access. It was exceptionally scalable, evolving from a small solution for a few users to one that could support hundreds. Across Brazil, Colombia, Portugal, Germany, Eastern Europe, India, and Asia, our technology functioned seamlessly. Its success was due to its simple installation, immediate availability, and massive scalability, even without global reimbursement codes. Fast forward to 2024. Today, we have hundreds if not thousands of AI solutions, as recently highlighted at RSNA 2024. The burning question is: who is a contender and who is a pretender? Investors and venture capitalists frequently ask me if I would invest in certain businesses. More often than not, my answer is no—not because of a lack of clinical impact or value addition, but because these solutions are not integrated into workflows and lack scalability, thereby failing to achieve economies of scale. How can technology ensure its survival? Achieving product-market fit is crucial. Consider: Can you address unmet needs while ensuring the product is simple, easily installed, integrated, and affordable? Or will it become just another option used by a few dedicated users reluctant to abandon it? If you can affirmatively answer these questions, and if the reimbursement landscape is supportive, success is almost certain. The adoption rate must grow alongside a smart commercial strategy. Before seeking multiple investment rounds and developing a large commercial force, focus on establishing solid Centers of excellence. Only then will commercial success follow! #healthcareAI #RSNA2024 #smartGTMstrategies

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The Business Value of Inference Inference is where AI moves from potential to performance. It’s the process of applying trained machine learning models to real-world data, driving actionable insights, predictions, and decisions. From enabling real-time voice recognition and chatbots to powering medical diagnostics and market analysis, inference delivers business-critical speed, accuracy, and scalability. Companies leveraging fast, efficient inference can reduce costs, accelerate decision-making, and enhance customer experiences. As AI continues to transform industries, mastering inference is essential for competitive advantage. #AI #MachineLearning #Inference #BusinessIntelligence #DigitalTransformation #Innovation #DataDriven #Automation

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Another great share from Brian R. Spisak, PhD From the article "Concepts" are crucial when it comes to "reasoning-intensive" tasks such as coding and math, where it can be difficult to specify the required concepts in a keyword-based search query. It's even harder to determine which documents might be relevant." CONCEPTS. It's all about the concepts. I learned all about Neo4j yesterday. I was surprised to learn that "graph" Neo4j doesn't automatically create the nodes, labels, relationships, or relationship definitions that power the underlying "graph data model." 🤔 I studied BPMN and UML (unified modeling language/CONCEPT modeling) over 20 years ago in graduate school under the guidance of two of the most prominent pioneers in the informatics field, Sue Bakken and Charlie Mead, MD. At that time, I didn't fully understand the significance of what I was learning. However, someone once said, "Once you learn how to abstract data into concepts and relationships to create (graph) data models, you will never see the world the same again." It's true; my brain was rewired during those two years to see the entire world as concepts/graph data models. My daughter gets annoyed when I complain under my breath about poorly designed airport processes based on flawed underlying graph data models. I've been viewing the healthcare industry, its functions, and the "data" as graph models for over 20 years. Graph data modeling is all about "metadata." Everyone uses the word metadata without understanding what it means. It's about creating the context of the data, which is often stripped away and discarded during our customary ETL processes and current relational database designs. Context enriches the data by adding back the meaning of the data when originally collected, including the who, what, when, where, how, and why of the workflow - the most atomic unit of healthcare activity. Context is necessary to determine which data is suitable for analytics, digitizing clinical practice guidelines, clinical decision support, quality measurement, AI training data, ML, GenAI, and creating the blueprint for aggregating and harmonizing disparate data sources. Creating a graph data model requires domain expertise; it's not the work of computer scientists or software engineers. It's crucial to understand how this new technology works, what it does, and what it doesn't do. If you need help with concept/graph data modeling in healthcare, feel free to send me a direct message Finally, as my dad used to say, "Don't let anyone give you a wooden nickel."😅 #HealthAI #CDS #CPG #eCQM #neo4j #BPMN+ #UML #OGDataHarmonizationQueen #AppliedInformatics

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The Open Medical LLM Leaderboard on Hugging Face dropped last Friday (thanks Geoff Kwitko for the heads up), and it aims to track, rank and evaluate the performance of large language models (LLMs) on medical question answering tasks (see link below). It evaluates LLMs across a diverse array of medical datasets, including MedQA (USMLE), PubMedQA, MedMCQA, and subsets of MMLU related to medicine and biology. The leaderboard offers a comprehensive assessment of each model's medical knowledge and question answering capabilities. The datasets cover various aspects of medicine such as general medical knowledge, clinical knowledge, anatomy, genetics, and more. They contain multiple-choice and open-ended questions that require medical reasoning and understanding. More details on the datasets can be found in the "LLM Benchmarks Details" section in the link. Ps. Good to know that the GPT-4 model is leading the way (it is what I use on the Medical Coding and Documentation agent). Digital Human Assistants #llmhealthcare

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One of the most important concepts we introduced in our recent Healthcare AI roadmap is the notion of “Modality—Business Model—Market fit.” This concept describes how the delivery method and business model of AI in healthcare directly shape its potential to create value. There are many possible combinations of modalities and business models in healthcare. AI can be delivered as software, copilots, agents, services, diagnostics, or therapeutics, supported by business models ranging from usage-based and volumetric pricing to performance-based and shared savings arrangements. Nailing selection of modality and business model is essential for AI companies seeking large market opportunities and profitable operations. As we describe in an example with computer vision-based diabetic retinopathy screening, modality and business model selection can drive a difference in TAM by upwards of 25x, and has serious implications for overall margin structures. Read more here 👉 https://lnkd.in/eK7rjpJz

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Join me later today at the Data + AI Summit in San Francisco where I'll be speaking on Cleanlab's AI-powered data curation platform for AI and data improvement. Details below: This talk addresses the two biggest problems facing AI practitioners today: (1) reliability and (2) time/cost spent on high quality data and annotations. Curtis covers the theory and algorithms that enabled Cleanlab, the data curation platform for AI used by 100+ of the Fortune-500 companies to automate trust and improve data in their AI stack, to find and fix millions of errors in the top 10 most benchmarked ML datasets like MNIST, ImageNet, Dolly, and Amazon Reviews. Curtis shares lessons learned from ten years working with LLMs, ML, and AI solutions at companies like Google, Amazon, Meta, Oculus, and Microsoft and shares real-world industry examples of models improved, millions saved, and number of annotations needed reduced by as much as 98%. The solutions covered will be data and model-agnostic and domain-specific to arbitrary use cases enabling them to work for both current models and future models that haven't yet been invented, like GPT-6. Please stop by! Thank you to the fantastic team at Databricks whose hard work makes the conference run smoothly like Brian Dirking and Maria Pere-Perez. #dataaisummit #machinelearning #artificialintelligence #ai #ml #datacuration #datacentricai #llms #genAI #AGI

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Founders at Ai4 - Artificial Intelligence Conferences, you won't want to miss Oana O. and Ilya Kirnos in vegas! Check out this session tomorrow👇

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It's been unique to go through medical training during the era of AI. Studying for board exams with language models, using AI literature search products to answer tough questions during rounds, and trying every agent-based patient simulator to strengthen diagnostic skills. There's a new physician stack forming around us thanks to AI. If you're interested in these topics and AI in medical education more broadly, please join me for the 10th Annual Conference, Precision Medicine 2024: Education in the AI Era at Harvard Medical School—a hybrid in-person/virtual event, on October 1st. Together with James Diao and Shivangi Goel, MD, MBA, we'll be talking about non-linear paths through medical training. Plus, you'll get to hear Dr. Isaac Kohane debate Dr. Pete Szolovits, on the topic of “Should GPT-4 Write my Thesis." Register here: https://lnkd.in/gSddDA63

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Really interesting conversation between Meghan McCarty Carino and Casey Ross about AI in senior care and the potential role that algorithms can play in determining care plans. I think using AI or algorithms in this way has benefits and challenges that come along with it. One positive effect it could have is increased personalization. Instead of giving everyone with a specific illness or medical issue the same treatment, it can help determine a more personalized solution that could improve patient outcomes. However, algorithms aren't always foolproof. In some instances, they can perpetuate bias and bad decision-making. If you're not working with unbiased, high-quality data, it could have major ramifications for a large number of patients. That's why I think if healthcare facilities use algorithms for patient care plans, humans still need to be active in developing the treatment plan. #SeniorCare #PatientCare #AIPersonalization

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In case you missed it… Timothy Nobles joined Kat McDavitt and Lisa B. on the Health Tech Talk Show to share a few hot takes on regulated data, including themes around the responsible use of sensitive data when training Large Language Models (LLM). Check out the full episode here: https://lnkd.in/gYwksYDg #HealthTech #DataPrivacy #ResponsibleAI

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AI isn't just about replacing humans—it's about empowering them. 💪 AssuranceIQ, an innovative insurance startup, is pioneering a groundbreaking approach to leveraging generative AI: 1. They're using Large Language Models (LLMs) to score customer conversations. 2. This AI-powered system structures unstructured data from interactions. 3. The goal? Enhance the performance of human sales and support teams. 4. It's not about AI vs. humans, but AI amplifying human capabilities. 5. This could revolutionize how we train and develop customer-facing teams. This use case showcases the true potential of AI in business: not as a replacement, but as a powerful tool to augment human skills and drive performance. What other innovative ways could we use AI to empower rather than replace human workers? #AIInnovation #CustomerExperience #FutureOfWork #BusinessAI #TechTrends Share your thoughts on AI augmentation in the comments below!

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🚀 Accelerating the Future of Healthcare with AI 🚀 I recently had the pleasure of tuning into an insightful episode of The Chris Voss Show featuring Lana Feng, Ph.D., CEO and co-founder of Huma.AI. 🗝 Here are some key takeaways that highlight the transformative power of AI in drug development and precision medicine: 🧠 Introduction to Huma.AI: Dr. Feng’s company, Huma.AI, leverages generative AI to automate data analysis, speeding up the development of life-saving drugs. Their mission? To reduce the time and cost involved in bringing these drugs to market. 🌟 💡 Challenges in Drug Development: Traditional drug development is a 10-year, $2.6 billion process plagued by data bottlenecks and regulatory complexities. AI can revolutionize this by handling vast amounts of unstructured data from various sources efficiently. 📊 🤖 The Role of AI: Utilizing advanced AI models like GPT-4, Huma.AI ensures data privacy, accuracy, and transparency. This leads to more reliable insights, faster clinical trial enrollments, and ultimately, better patient outcomes. 🏥 🌍 Real-world Applications: From identifying the right treatments in precision medicine to accelerating clinical trials, AI’s impact on healthcare is profound. It’s especially crucial in areas like cancer treatment and chronic disease management. 🎯 📈 Industry Recognition: Huma.AI has garnered significant recognition, being named a leader in generative AI for life sciences by Gartner. 🏆 🔮 Future Directions: The integration of AI in healthcare requires responsible usage, industry-wide collaboration, and continuous efforts to mitigate risks like data privacy issues and AI hallucination. 🤝 🌟 AI for Good: Dr. Feng emphasizes the importance of using AI responsibly to lower drug prices and improve healthcare outcomes. This approach ensures AI serves as a powerful ally in our quest for better health. ❤️ I’m excited about the potential of AI in transforming healthcare and making life-saving treatments more accessible and affordable. Let's embrace this technology for a healthier future! 🚀 #HealthcareInnovation #AI #PrecisionMedicine #DrugDevelopment #TechForGood #FutureOfHealthcare #ChrisVossShow #HumaAI #Leadership

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