Dr Aengus Tran

Thank You @EduardMusinchi for Highlighting the important of Huro AI in the AGI Movement! We’re thrilled to see HURO AI, Inc. room-temperature quantum AI mentioned alongside groundbreaking innovators like HyperCycle, SingularityNET, and Schelling AI in the conversation about the Internet of #AGI. The Strong AI Summit and AGI Syndicate are catalyzing a movement that’s #reshapingthefuture, and we’re proud to contribute to this vision. It is a new standard for what’s possible and it's an innovation that truly matters in the context of: Scalability: By eliminating the need for expensive cryogenic setups, we make quantum computing accessible and sustainable. - Efficiency: Quantum AI accelerates decision-making and optimization, critical for building AGI. - Integration: Our tech seamlessly aligns with decentralized AI systems like Hypercycle, fostering collaboration and innovation. - The AGI Syndicate’s focus on a transparent, community-driven AGI future aligns perfectly with our mission. The idea of a Sharing Internet of AGI built on equitable principles has the potential to empower humanity and address global challenges, from health tech breakthroughs to economic resilience. As the Strong AI Summit approaches, we look forward to connecting with leaders and #visionaries to collectively push the boundaries of what’s possible. #QuantumAI #AGI #RoomTemperatureQuantum #InternetOfAGI #HuroAI #Hypercycle #StrongAISummit #Innovation #AIForGood #EmergingTech #SharingEconomy

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The shift in Series A to Series B graduation rates is a stark reminder of how the startup landscape has evolved, particularly post-2020. The "sugar rush" analogy fits well—easy access to capital led to rapid growth and inflated valuations. But as interest rates have gone up and funding has become more selective, startups are now having to return to solid business fundamentals. For MedTech and FemHealth startups, the journey from Series A to Series B was never easy, even in more favorable times. Now, with the additional pressure of financial conservatism, these companies must show not only potential but tangible progress in product-market fit, regulatory paths, and revenue generation. This is where smart money, strategic partnerships, and a clear focus on health outcomes can differentiate those startups that are essential from those just chasing the next round. With venture capital taking a closer look at impact and sustainability, founders should focus on aligning with investors who understand the long-term value of innovation in healthcare. For founders currently raising, it's about knowing that this is a marathon, not a sprint. Yes, the competition is tough, but it also means there's an opportunity for those who stay nimble, pivot intelligently, and keep an eye on profitability and growth potential without sacrificing the mission. What do you think will help startups stand out the most as they aim for Series B in today's market? 🚀 Thank you Peter Walker for your incredible insights!! #MedTech #FemHealth #VCFunding #SeriesA #SeriesB #HealthInnovation #Founders

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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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#aihealth, #aihealthcare The authors have identified four trends in digital health – (1) Interoperability, (2) Cloud, (3) Apps and personalisation, and (4) Data analytics as a service tks for sharing #Evgeny Krapivin

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🚀 𝐓𝐞𝐦𝐩𝐮𝐬 𝐢𝐦𝐩𝐫𝐞𝐬𝐬𝐞𝐬 𝐨𝐧 𝐢𝐭𝐬 𝐟𝐢𝐫𝐬𝐭 𝐭𝐫𝐚𝐝𝐢𝐧𝐠 𝐝𝐚𝐲, surging 15% and signalling strong investor interest in health tech with 𝐀𝐈 𝐩𝐨𝐭𝐞𝐧𝐭𝐢𝐚𝐥! 🚀 The remarkable debut of Tempus, a health tech company utilising AI for personalised medicine, resulted in a 15% stock increase on its initial trading day. 🌟 #HealthTech #AI #Innovation #InvestorAppetite #HealthcareRevolution #TechTrends

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Abridge and DeepScribe (two of the more established AI scribe vendors) published papers this week on the topic of performance measurement for AI scribing solutions. This is cool to see. We need more transparency and discussion about the performance of these new technologies, and we appreciate these companies helping to drive the conversation forward. Check out the post below for a few thoughts from Elion's head of research, Patrick W., on the Abridge whitepaper. For those who want to go deeper, here are the links to the whitepapers: • Abridge: https://lnkd.in/eEXtwsAy • DeepScribe: https://lnkd.in/eXYTwfcb

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Big healthcare AI news yesterday as Tempus AI received the first-ever CPT code for an algorithm-only analysis of previously-sequenced, laboratory developed test transcriptomic data. TLDR: AI molecular phenotyping has a new business model. The PurISTSM test uses RNA profiling to identify molecular subtypes in patients with advanced pancreatic ductal adenocarcinoma, which may inform first-line therapy selection and management. While I'm skeptical that payer balance sheets are going to solve the "who pays for healthcare AI" problem, the reimbursement pipes for clinical AI are maturing. I am surprised to see more progress for usage-based vs. performance-based models given the many challenges with FFS economics. It will be interesting to see how payers assess the "medical necessity" and therefore reimbursability of clinical AI as these models come to market. https://t.co/eLe18SmihV

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6 insights on Epic’s approach to Healthcare AI from this week’s UGM: 1. AI use cases are focused on (1) streamlining documentation, charting, and coding and (2) surfacing evidence-based insights at the point of care 2. The two killer use cases so far have been (1) AI medical scribes (186 orgs have adopted) and (2) AI draft responses to patient mortal messages (150 orgs and medical groups producing 1 million drafts per month) 3. Over 100 new generative AI projects are in the works, such as using AI to draft orders/diagnoses, populate forms/flowsheets, auto-tagging drug reactions, produce patient-friendly summaries. 4. Of particularly excitement to clinicians is an AI tool that will mine the patient chart and surface relevant clinical information - being rolled out in Feb 2025 5. Cosmo, Epic’s research database with 226 million patient records, is live with a new AI tool called “Look Alikes” at 65 sites. It helps physicians identify rare diseases by finding patients with similar symptoms and connect with physicians treating them to collaborate.  6. A newer related AI tool called Best Care Choices identifies hundreds or thousands of patients similar to the one you are focused on and shows how effective different treatments were for them - to help clinicians make more data-informed treatment decisions. It’s currently being tested by 2 orgs. My thoughts? → Epic’s experience matches my conversations with CMIOs, CIOs and CDOs over the past year. AI Scribes and patient portal message drafts are the two most common Generative AI use cases health systems have been trying. → While those numbers are impressive (186 orgs doing AI scribes, 150 orgs doing patient portal message drafts) - those are likely the number of orgs who have “tried” those use cases. What would be interesting is how many are doing them at true operational scale. I know of many orgs who only have a handful of people using those tools right now. → Since Epic itself doesn’t provide AI scribes, it means 186 orgs are using a 3rd party vendor. I suspect Nuance DAX Co-pilot is far and away the majority of that 186, even though they don’t do as many press stories as the other vendors. The question is whether they’ll have staying power. → Epic is driving a huge part of the AI roadmap for health systems - which makes sense since so much of the data is in the EHR, and so much clinician time is spent in the EHR. I’ve said before that the EHR that leverages AI to improve the clinician experience the fastest will win… seems like Epic’s lead will compound as they’re moving faster on AI than the others. → The true killer AI use cases will be those that do the work that absolutely no clinician wants to do - e.g. AI-generated discharge summaries. Some clinicians still want to write their own individual patient notes and won’t use AI scribes. But I imagine very few clinicians want to review 30 days worth of in-patient notes to make a discharge summary. More details in the story below 👇

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The first Australian FHIR API standard is released this year. It enables software engineers in the healthcare industry to connect systems much easier than before. Our great engineer Shima Mehrabi has written an article summarising healthcare services in Australia and which FHIR resources apply to each category. A lot of time and thinking has gone into it with a great diagram to see it all 👌 #fhir #api #healthcare https://lnkd.in/g9sc5tz2

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Today the most important article in chatgpt and "Surveyed veterinary students in Australia find ChatGPT practical and relevant while expressing no concern about artificial intelligence replacing veterinarians" - Veterinary students in Australia find ChatGPT's output relevant and practical for their use. - The study highlights the importance of critical analysis of AI tools like ChatGPT in veterinary education. - Over 75% of surveyed students reported using ChatGPT, indicating its widespread adoption. - Students perceive ChatGPT as more useful for veterinary students than for pet owners or veterinarians. - There is a consensus among students that veterinarians should embrace AI but not be replaced by it. #AI #VeterinaryEducation #ChatGPT #ArtificialIntelligence #VeterinaryStudents

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AI's dominance in venture capital is significantly impacting medtech fundraising. With AI/ML investments reaching $77B and medtech investments down 20% from their peak, roughly half of all VC dollars are now chasing AI. This shift has led to fewer life science exits, making it harder for medtech companies to secure funding. It's crucial to assess whether your innovation is indispensable and adjust your strategy accordingly. Source: Spencer Jones PitchBook #fundraising #ai #medtech #VC #MergersandAquisitions

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How do we translate and implement #AI in #healthcare? Here, in this latest New England Journal of Medicine AI paper, we outline our journey from "code to clinic" in Singapore, which took nearly 20 years. We first set up a national #telemedicine program in Singapore's primary care clinic system for diabetic retinopathy screening (SiDRP), and then integrated, in a phased approach, a #deeplearning algorithm (SELENA+) to this national telemedicine program. We outline the steps, the lessons learnt, and the challenges faced. We hope this case study is a model for other AI algorithms in other healthcare settings! https://lnkd.in/ge6gXtT2 Dinesh VG Wynne Hsu Mong Li Lee Steven Miller Daniel Ting Gavin Tan Haslina Hamzah Hon Tym Wong Singapore National Eye Centre EyRIS NEJM Group NEJM AI #aihealth #deeplearning #clinicalinnovation #digitalmedicine #telemedicine #diabetes #diabeticretinopathy #medicalresearch #aimedicine #translationalresearch

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The article discusses the growing importance of AI literacy in healthcare and the efforts of Flinders University to address this need. Professor Niranjan Bidargaddi emphasizes that healthcare professionals must understand AI to effectively integrate it into their work and make informed decisions. Flinders University has introduced a new postgraduate course in AI for Health and Medical Sciences, aimed at equipping healthcare workers with essential AI knowledge. The course covers various aspects of AI, including machine learning, natural language processing, and computer vision, tailored specifically for healthcare applications. The program is designed to be accessible to healthcare professionals without requiring extensive mathematical or coding skills. It focuses on practical applications of AI in healthcare, such as improving diagnoses, treatment planning, and patient care. Professor Bidargaddi stresses that AI literacy is crucial for healthcare workers to critically evaluate AI tools, understand their limitations, and effectively communicate with AI developers. The course aims to bridge the gap between healthcare professionals and AI specialists, fostering collaboration and innovation in the field. https://lnkd.in/ePEyBSW5 #healthcare #healthcareinnovation #healthcareit #healthcareai #ai #genai #generativeai #ChatGPT #llm #ml #bigdata #healthcaredata

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👏 Hats off to Adrian Aoun and the Forward Health team for their bold attempt to revolutionize primary care through consumer-driven AI. This ambitious effort underscores a crucial point not specific to them: Consumerism in healthcare lacks the clinical, technical, and regulatory backbone needed to make it viable. 🚨 The Core Issues Holding Back Consumer Health 🚨 💡 TTIC: Why We Prioritize Systemic Health Solutions Over Consumer Models. At Trustworthy Technology and Innovation in Healthcare Book Series Consortium (TTIC), we focus on developing secure, standards-driven, and integrated technologies that can thrive within healthcare’s structured environments. Consumer health might be flashy, but true transformation needs a solid foundation of governance, regulatory compliance, and seamless interoperability. The future of AI in healthcare demands an infrastructure that supports patient-centered outcomes, clinical accuracy, and secure data sharing—not consumer grade tech. We’re building that foundation at Medigram, Inc. which leads TTIC, knowing that only with a full ecosystem of clinical, technical, and regulatory support can healthcare tech deliver real, lasting impact. 🔥 Hot take: The allure of consumerism in healthcare is strong, but let’s be clear: Healthcare consumerism as a market is structurally flawed and currently lacks the core foundation needed to make it truly attractive, safe, effective, or sustainable. 🚧 Why Consumerism in Health is a Dead-End Right Now 🚧 Consumer-driven healthcare is missing the fundamental infrastructure required to truly deliver value. Here’s what’s holding it back: Clinical Oversight: High-quality healthcare can’t be automated or delivered via self-serve apps and kiosks without expert clinical involvement. The stakes are simply too high. Patient safety, diagnostic accuracy, and real-time intervention all require human oversight, not DIY tech. Rigorous Regulatory Compliance: Healthcare requires adherence to strict standards and regulations. Consumer solutions often bypass these, exposing patients and systems to security risks and ethical concerns busting trust. Without FDA-level oversight and robust privacy frameworks, consumer health tech remains untrusted. #healthtech #AI #TTIC #healthcarestandards #patientcare #infrastructure cc Apurv Gupta, MD, MPH Shaun E. Garcia Joshua Tamayo-Sarver, MD, PhD, FACEP, FAMIA Stephanie Feals Jane Sirigiri image and article context: https://lnkd.in/d9xdDCB4

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In case you missed it, last week the Coalition for Health AI (CHAI) released a draft consensus framework for responsible health AI. Developed with input from over 100 contributors representing a diverse network of healthcare stakeholders, this guide proposes actionable evaluation criteria throughout the AI lifecycle—from identifying use cases to deployment and monitoring. Key examples outlined in the guide include: • Predictive EHR Risk Use Case (Pediatric Asthma Exacerbation) • Imaging Diagnostic Use Case (Mammography) • Generative AI Use Case (EHR Query and Extraction) • Claims-Based Outpatient Use Case (Care Management) • Clinical Ops & Administration Use Case (Prior Authorization with Medical Coding) • Genomics Use Case (Precision Oncology with Genomic Markers) The draft framework is open for public review and comment for the next sixty days. Please use this form to submit feedback: https://lnkd.in/eYughFYC Kudos to the CHAI team led by CEO & President, Dr. Brian Anderson, MD, on this milestone. Eric Horvitz Jennifer Goldsack John Halamka, M.D., M.S. Michael Pencina Micky Tripathi Nigam Shah Suchi Saria Troy Tazbaz #healthcare #ai #artificialintelligence #generativeai https://lnkd.in/e7VRv6fD

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🔒 How do you advance AI innovation in healthcare without compromising patient privacy? Topcon Healthcare and BeeKeeperAI® have the answer! By combining Topcon’s Harmony platform with BeeKeeperAI's privacy-enhancing EscrowAITM, we’re allowing AI developers to securely compute on real-world eye data—supporting early detection and better disease management while ensuring data sovereignty. French AI company @AiScreenings is already leveraging this secure environment to validate its aiVista model in the U.S., helping bring AI-powered diagnostics to market faster. 🌟 Together, we're setting a new standard for responsible, privacy-focused AI collaboration. Read more in our Blog (https://hubs.ly/Q02TRK7M0) or stop by the Topcon booth 3426 at AAO to learn more. #HealthcareInnovation #SecureAI #PrivacyTech #Collaboration

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Leap over or add-on??? What I am referring to? So I was talking to a friend in the healthtech industry about the roll out of digital health in Asia, from the mundane (EHR) to the advanced (Digital twins & AI driven health). An interesting point of the discussion was can a less advanced healthcare system use digital tools to leap-frog its capabilities gap by using advanced digital health tools. He argued countries in SEA (excluding SG 👈 ) could use digital tools to enhance the quality of health care delivery by allowing them to jump over the development stages followed by more advanced healthcare systems. He also pointed out they don't need to deal with the burden of significant legacy healthcare IT systems. I disagreed since I believed the lack of existing capabilities would make it difficult to add more advanced capabilities to their healthcare system, so an incremental add-on model makes more sense. Also digital health tools need to work with existing healthcare infrastructure, and weakness in this foundation will make it harder for these products to have an impact. I suspect the push into the digital health space by developing countries in the SE Asia shows the answer is somewhere in-between our positions, and health systems that work at integrating digital tools into their systems will move faster through this capability and capacity growth curve. That also seems to be in line with the view of the article below!

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In the last few years the digital health space has seen a large number of non-healthcare contenders trying their hand at creating digital health led or enabled businesses. One of the most recognised is e-commerce giant Amazon. The Company's healthcare business was kicked into high gear with the acquisition One Medical, and now combines physical and digital health resources into a seamless patient offering. I've been expecting that this trend would find its way to Asia eventually, there is an obvious un-met need for access to healthcare in the region creating a sizeable market opportunity. So would you believe a bank could partner with high speed internet provider to offer digital health products to the market? Well that what the partnership between CelcomDigi and AmBank represents, with both companies collaborating to offer novel digital health products to customers in the Malaysian market. The partners will sell a range of digital health solutions to healthcare institutes. AmBank also plans to offer financial solutions to help fund the purchase of the services, so I am assuming this is some type of B2B2C play. I will be very interested to see how this plays out. Personally I'm glad to see more digital health offerings being supported in local Asian markets, although I have seen 'non-healthcare' digital health health players have a tough time creating viable businesses in this space. The US market for example is littered with non-healthcare companies that underestimated the complexity of creating and scaling businesses in the healthcare space. I wonder who else will show up in the region with a new digital health play!

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