Dr. Anna Karmann

What’s happened this week in the healthcare & AI world? Here is the interesting news and research: 𝐍𝐞𝐰𝐬: 🗞 GE are leading a consortium working on synthetic data generation and their use in healthcare AI. 🗞At their investment summit the Gov: 💰 Released their 10-year industrial strategy, highlighting a growth to build a stable NHS; 💰Announced £6.3bn investment in UK data centres for AI; and 💰Stated in a speech from Starmer: we need to be “running towards” the opportunities of AI. 🗞 NHSE published lessons learned from the AI Evaluation rewards such as: 🔹 Deployment & evaluation plans should be co-produced; 🔹 Quasi-experimental methods were appropriate; and 🔹 Future programmes should more explicitly focus on impact. 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡: 👩‍💻 A study looking at using Bing (a search engine chatbot) responses to questions about common prescribed drugs in the US found 66% of responses to be potentially harmful. 👩‍💻 A survey of medical students in Kazakhstan found 95% had no AI education in medical school, and 74% received information about AI from the media. 👩‍💻 AI survey of 38 Doctors in Indonesia found socio-technical blindness and AI configuration significantly influence anxiety about AI replacing their job.

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Medical venture capital (VC) is a source of funding and resources that helps medical technology (MedTech) companies thrive and adapt to the needs of patients and healthcare providers. VC firms help MedTech companies by: Providing cash flow VC firms provide the capital needed for companies to bring their products to market. Supporting innovation VC firms invest in companies that offer innovative solutions to improve patient outcomes, reduce costs, and increase efficiency. Steering companies towards profitability VC firms help companies progress towards the most profitable exit within the timeframe they estimate for returns on investment. Influencing technology design VC firms influence technology design priorities by enforcing time and resource constraints. Some of the areas where VC is investing in MedTech include: Precision medicine VC firms are investing in companies that offer innovative solutions for precision medicine, such as artificial intelligence, genomics, and data analytics. Medical devices VC firms are investing in companies that develop advanced medical technologies and devices to improve patient care and treatment outcomes. Digital health monitoring devices VC firms are investing in companies that create wearable devices and remote monitoring technologies. AI in medical imaging VC firms are investing in companies that combine AI and medical imaging to improve diagnostic accuracy. #atheenapandian

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Healthcare AI is having its moment, with an explosion of new entrants and exceptionally high market demand for solutions from established incumbents. We are seeing this play out in the startup ecosystem and across boardrooms at Fortune 500 companies, as CEOs from within the industry and outside are eyeing healthcare AI as an immense market opportunity — from Big Tech and Big Pharma, to the very institutions that are responsible for our own care.

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M13 is thrilled to lead the Series A in RadiantGraph which is focused on personalization in healthcare. As we continue to invest in early stage companies it is abundantly clear that the biggest indicator of success is the founder and his or her ability to execute on their vision of the future. In this case, I don't think I have seen much better founder-market fit than we have with founder and CEO Anmol Madan. We believe in his vision: in order to have the best patient outcomes in healthcare we need increased personalization and AI is the breakthrough to get us there. Anmol started one of the pioneering digital health companies Ginger and then was Chief Data Scientist at Livongo and Teledoc. He has done it all: early stage, late stage, public company. And he deeply understands the nuances of the healthcare market from all angles which is critical for success. While there are many players building healthcare-specific AI capabilities, RadiantGraph is focused on outcomes: The platform is already processing personalization models for more than 3.5 million people, despite launching just last year. Excited to partner with my friend Adam D'Augelli and the great True Ventures again and Morgan Blumberg who has been deep in the healthcare market with me for many years. More on our thesis in the blog below. https://lnkd.in/evDDmC3W

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deepc closes another round of funding to accelerate its growth and solidify its role in AI deployment in the clinic !👇 Reflecting on early conversations with Dr. Franz MJ Pfister, MD, MBA, I am amazed by how his vision and yesterday's ideas have transformed into a leading radiology AI company with customers in over 30 countries ! Platforms like deepcOS offer a turnkey solution for hospitals and clinicians, easing administrative and technical burdens of AI integration. Best of luck with the next milestones, Franz and team ! 🚀 Read more on Marty Stempniak article ; link in the comment 👇 #radiology #AI #startup #funding Sofinnova Partners Bertelsmann Investments Digital Partners

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"A McKinsey survey found that 70% of payers and providers reported pursuing implementation of generative AI. Three-quarters of these same groups have increased IT investments over the last year and expect this trend to continue, a Bain & Company survey found." Every single health-focused organization I've worked with in the last ten years (at least!) was either having conversations about tech integration or already implementing it into any and all processes, from patient care to payment. As we enter a new phase in healthcare AI, one that not only addresses the demands of patient care but also streamlines healthcare delivery, it's important to identify what direction is best for your org as you ride the AI wave. #HealthcareAI #Innovation #PatientCare

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AI’s Impact on Science and Medicine: “From enhancing diagnostic accuracy to advancing drug discovery, AI’s role in science and medicine is expanding rapidly. The latest Stanford AI Index Report 2024 details how these innovations are paving the way to improve access, experience and outcomes. #AI #DigitalHealth

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📺 Catch our conversation with Roger Li, MD, of Moffitt Cancer Center, on the final results of #CORE001, which investigated #cretostimogene plus #pembrolizumab for #BCG-unresponsive #NMIBC with #CIS. 👉 Find the Full Video Here:

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In my recent article in Fast Company, I share thoughts on how physicians can facilitate the adoption of trusted AI solutions in treating patients. https://lnkd.in/evxfaNaG

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👉🏼 ChatGPT's Accuracy in Triage of Nephrology Patient Communications 🤓 Justin H Pham 👇🏻 https://lnkd.in/eYkqgU4h 🔍 Focus on data insights: - 📈 In the first trial, ChatGPT correctly triaged 93% of messages, demonstrating strong reliability. - 🔍 The AI system maintained consistency across trials, with a similar accuracy rate in both assessments. - ⚖️ Internal agreement between ChatGPT's responses was high, indicating robust performance and minimal variability in results. 💡 Main outcomes and implications: - 🤖 High accuracy in triaging suggests that AI could be effectively integrated into outpatient settings to streamline patient communication. - ⏱️ Potential reduction in response times for patient messages, leading to improved patient care and operational efficiency. - 🏥 Encourages further exploration of AI applications in healthcare for enhanced decision-making and resource management. 📚 Field significance: - 🔬 Highlights the role of artificial intelligence in transforming patient care pathways within nephrology and beyond. - 🌐 Sets a precedent for future studies assessing AI capabilities in various medical specialties. - 📊 Supports the development of AI-driven tools aimed at optimizing triage processes and improving clinical outcomes. 🗄️: [#AI #Triage #Nephrology #PatientCare #HealthcareInnovation #ChatGPT #DataInsights #Efficiency]

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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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Just launched my Medium blog where I explore the intersection of personal stories, healthcare innovation and real-world clinical practice – sharing insights from both the frontlines and the latest research that's shaping our field. Follow my journey on Medium [https://obmd.medium.com/ ] and join a community of forward-thinking healthcare professionals as we dive deep into evidence-based medicine, emerging technologies, personal observations, and the future of patient care. https://obmd.medium.com/

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At #ASCO2024, James Isbell, MD, MSCI, presented new data and insights using Foresight CLARITY powered by PhasED-Seq to assess minimal residual disease (#MRD) in lung #cancer patients. The data strongly suggested that ctDNA-based MRD assessment could significantly enhance the early detection of cancer recurrence and provide more precise prognostic information compared to first-generation MRD approaches. The findings also suggested that incorporating ultrasensitive MRD analysis into future adjuvant therapy #clinicaltrials could correctly identify patients who would benefit from therapy from those who wouldn't. #NSCLC #oncology #medicine #healthcare #personalizedmedicine #precisionmedicine #diagnostics

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To follow up on Dede Ainbinder recent post: If we use the current EHR data to build a AI clinical decision support tool it is unlikely to capture our clinical decision making process. EHR's are inherently designed to maximize billing and minimize risk of litigation. A clinical decision support tool based on this data will likely do the same. There is a large sunken cost fallacy here where we continue to hang on to garbage data. Just because the data gathering and storage process was capital and time intensive doesnt mean the data is valuable. To truly capture the clinical decision process we need to capture data which sits outside billing and litigation. Its hard to imagine a future where this data isnt used for billing and cannot be called upon during litigation. #EHR #healthdata #clinicaldecision

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Still buzzing from the incredible #TEDAI2024 panel on AI and Healthcare! The video of our session is finally out. Click below to watch. It was an absolute honor to share the stage with such forward-thinking leaders: Fawad Butt Monica Chmielewski Aaliya Yaqub, MD and our inspiring moderator Missy Krasner A huge shout-out to TEDAI San Francisco for putting together this fantastic event! During the discussion, we talked about how Generative AI is reshaping patient care—from early detection and personalized treatment, to making our healthcare systems more proactive and patient-focused. I’m humbled by the enthusiasm and expertise everyone brought to the table. As Founder & CEO of HOPPR, I’m excited about what’s coming next. We’re committed to turning cutting-edge AI into real-world solutions that help providers, patients, and communities thrive. Thank you to everyone who attended, asked questions, and sparked some truly dynamic conversations. Here is an article we wrote summarizing all the topics we discussed on the panel: https://lnkd.in/gEwQ3_7A If you’re as passionate as I am about the future of healthcare innovation, let me know your thoughts below. Please share this post and keep the momentum going—your comments, ideas, and personal experiences can help all of us move forward in shaping the next generation of healthcare. #GenAI #HealthcareInnovation #AIFuture #AIandHealthcare #TEDAI2024

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