Amir Lahav

There appears to be a "Superstar Effect" in healthcare and biomedical AI research right now. A small percentage of publications are truly novel methods contributions that introduce new capabilities or behaviors; whereas a majority of contemporary research publications are focused on implementation science to assess the real-world utility and performance of new methods. This dynamic seems different from prior eras, when the translational infrastructure for implementing novel AI methods was largely unavailable. While methods research often captures more attention and accolades, both types of research are critical – novel methods without implementation science lacks translatability; implementation science without novel methods lacks novelty. If our goal is to invent and deploy transformative AI methods in real-world healthcare and life sciences contexts, we ought to continue encouraging and rewarding stellar implementation science work alongside breakthrough methods development.

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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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Interesting cardiology subplot to Signify Research Ltd's latest imaging AI startup funding data. The 15 most-funded imaging AI companies include five cardiovascular-focused startups (HeartFlow, Inc, Cleerly, CathWorks, Keya Medical, Elucid), and three companies that place a heavy focus on cardiovascular and stroke imaging (Aidoc, Viz.ai, RapidAI). ...That's more than half of the imaging AI companies that have raised over $100M. Considering that the majority of AI vendors focus on non-cardiovascular imaging, this says a lot about where VCs have been spending their money -- closer to care, procedures, costs, and reimbursements. We'll be covering this in the next Cardiac Wire https://lnkd.in/gffPtj9J

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There is a new (substantial) market coming for video games and other forms of media that can positively impact mental health. The Digital Therapeutics Alliance has been working hard with congress to create new reimbursement pathways for media as medicine (Digital Therapeutics) - with a special focus on mental health. Reference Article: https://lnkd.in/gxWwQTks Many commercial video games have been proven to positively impact mental health. One of over 200 Reference Studies: https://lnkd.in/gSYzZKsi The Immersive Medicine Network - a collaborative ecosystem of game developers, experience designers and medical professionals has been created to help media professionals access this new market: The Immersive Medicine Network is building the following resources: - A resource website for creating media as medicine: www.immersivemedicine.org - A LinkedIn support group, the Immersive Medicine Network (IMN) where connections can be made and resources shared: https://lnkd.in/gW8M_vXN - A Podcast to help spread information (first episode coming this week) - A biweekly fireside chat, where questions can be asked and collaborative approaches developed. (Accessible through the IMN) Media holds the key to delivering vital mental and physical health interventions at scale. Now the industries must learn to combine capabilities to create the change we need to fight the #loneliness #stress #anxiety and #depression that are crippling our society. Please join us as we build this new world of Immersive Medicine, recognizing the therapeutic potential of media and bringing accessibility and affordability to mental health care. #videogames #immersivemedicine #powerofplay #stressrelief #resilience

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Here’s your recap of last week’s health IT news 🗞️ 👇 🤖 AI Products • Emergency Services, Inc. + Augmedix Go ED: The emergency medicine group adopted the new ED-specific AI ambient scribe solution.    • Color + OpenAI: The cancer care tech company partnered with the GenAI giant to build a AI clinical decision support copilot.    • AKASA Medical Coding: The AI RCM vendor known for its AI prior auth tool AKASA Authorization Advisor released a new GenAI medical coding solution.    • Humata Health: The AI prior auth for providers vendor closed a $25M funding round led by healthcare org-backed funds The Blue Venture Fund and LRVHealth.    • Wellsheet + Concord Hospital Health System: The AI-enabled Smart EHR UI solution announced a client-reported 16.3% reduction in length of stay and 40% decrease in time spent in the EHR via its partnership with Concord Health, as well as the release of its new LLM-generated handoff summary feature. 🍎 VBC • BSIM Healthcare Services + Innovaccer: The healthcare provider—embarking on its first VBC contract—adopted the pop health data platform for analytics. 🩼 Durable Medical Equipment • Parachute Health: The DME e-prescribing and ordering solution released an update that integrates prior authorization. ⚕️Clinical Staffing • Matchwell + Indiana Hospital Association: The clinical staffing service partnered with the hospital member org to create the first state-wide resource pool for temp staffing. 🧑‍💻 Virtual and At-Home Care • Deaconess Health System + KeyCare: The Indiana-based health system adopted the Epic-based virtual care provider for around-the-clock virtual urgent care.    • UC Davis Health + Current Health: The health system launched a new RPM program for high blood pressure in partnership with Best Buy Health’s care-at-home platform. 🧠 Food for Thought: We came across a couple of good resources this past week on the current state of healthcare data information exchange: • This article in Health Affairs from Deven McGraw and Tina Grande provides a comprehensive overview of some of the patient health data abuses we've seen come to light in recent months, as well as a number of policy recommendations to improve the health information exchange landscape for all participants. https://lnkd.in/e_GEQ-JJ  (h/t Alya Sulaiman)    • This episode of Health Gorilla's InteropTalk gives a helpful update on the current state of affairs, with a particular focus on the state of trust across the health data exchange community and how the definition of "Treatment" may evolve. https://lnkd.in/e96AJG-5 (h/t Brendan Keeler) --- Reminder: you can sign up for the Elion Briefing to get the latest healthcare tech news delivered to your inbox each week 📬 👉 https://lnkd.in/e2UJ3ckP

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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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According to a new Gartner report, approximately one third of enterprise wide genAI projects will end after proof of concept evaluations conclude by end of 2025. One of the biggest barriers to generative AI adoption is the unclear business value and extremely high investment and operational costs. Healthcare already has a financial sustainability problem. GenAI has not proven its ability to help improve this problem, however it certainly can make it worse!!! https://lnkd.in/d7pZg7Fz

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Artificial intelligence (AI) revolutionizes healthcare by enhancing diagnostics, personalizing treatments, and streamlining operations. AI algorithms can analyze vast datasets, uncovering patterns that aid in early disease detection and more accurate diagnoses. This capability is particularly beneficial in medical imaging, where AI assists radiologists in identifying anomalies that might be overlooked. In drug discovery, AI expedites the identification of potential therapeutic compounds, significantly reducing the time and cost associated with bringing new drugs to market. For instance, companies like Insitro leverage machine learning to analyze extensive biological data, aiming to develop more effective treatments. Operationally, AI enhances healthcare efficiency by automating administrative tasks such as scheduling and patient record management, allowing healthcare professionals to focus more on patient care. However, the rapid integration of AI raises concerns about data privacy, algorithmic bias, and the need for transparency in AI models. Ensuring ethical AI deployment is crucial to prevent disparities in healthcare delivery. As AI continues to evolve, its potential to transform healthcare is immense. Balancing innovation with ethical considerations will be key to harnessing AI's full medical benefits.

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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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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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In this week’s The Yuan, I share my thoughts on advances in #QSM (Quantified Self Movement) and how newer technologies (like #wearables and #LLMs) can unlock the power held in #biomarkers to help us live healthier for longer. Medicine 3.0 is here! #mitoplus #mito #humanedge #docm #longevity #healthspan #healthcare #everdayhealth

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A whirlwind of activity last week at the America's Physician Groups conference. Lots of solid insights shared by health system leaders. Some notable thoughts: +++ Artificial intelligence is intelligent in different ways: By now, we all know ai has a diverse set of use cases, incorporated in different applications and tools. But there are fundamental differences in how the intelligence itself is brought to life. Examples include: - Predictive AI, used to forecast future outcomes by leveraging historical data. Shout out to UCLA’s CKD/ESRD early intervention protocol (Joycee Berin) and Diagnostic Robotics’ intelligence care post-dicharge journeys (Kira Radinsky) - Protocolized AI, used to automating a user’s administrative load by using a set of detailed, pre-defined rule sets. Thanks to ARC for sharing their experience using Elaborate 's automated outpatient lab summaries (Manish Naik) - Generative AI, used to create new content. Great overview of ambient documentation at MGB as well as Cedars’ Sinai talking through its medical ontology model powering acute triage (Caroline Goldzweig) Across system leaders and experts, there was clear agreement that the first step to implementing intelligence intelligently is defining the ‘jobs to be done’. That way, internal and external stakeholders are clear on what they're solving for when they architect the solution.  +++ Scaling AI takes time and rigorous observation: Market leaders describe a phased pilot approach that allows clinicians to build confidence and system leaders to measure ROI before deploying systemwide. Loved hearing Rebecca Mishuris outline MGB’s novel approach of piloting two solutions against one another, to pressure test implementation, workflow, and value creation. +++ Proving ROI continues to be challenging: Lots of vendors using lots of different metrics to prove value, but the majority of leaders agreed that estimating the ROI of these new tools today is part science, part art. Demonstrating the ROI of the “soft” metrics around provider burnout and retention seemed to be a big opportunity, especially for investment in more efficient primary care and PCP wellbeing. After all, PCPs drive referrals to specialty-level care (and higher reimbursements) for health systems. Appreciated Anurang Revri's overview of how working team setup can also impact ROI estimates. +++ Value-based care = tools + humanity: VBC will require not only implementing tools to support clinical documentation, risk capture, upcoding, etc, but also humanizing interactions to engage patients. Countless examples, but one that made me smile was a depression focused chat bot responding in the following interaction: - Context: post-discharge check in Bot: Hi Freida, how are you feeling today after your surgery?  Patient: I’m having a tough time, am in a lot of pain.  Bot: Well, it can’t be that bad, I’m sure it will get better with a positive mindset!    🤣 🤣 🤣 Can't wait for next go-around!

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A GREAT COLLABORATION FORMED… PRESS RELEASE New York City, May 6, 2024 Information Mediary Corp. (IMC) Corp. and Adherence Partner to Launch Revolutionary Digital MMAS Integration for Medication Adherence May 6, 2024 – New York, NY -- Information Mediary Corp (IMC) and Adherence have collaborated to introduce a digitized Morisky Medication Adherence Scale (MMAS), revolutionizing medication adherence management. This innovative solution signifies a significant advancement in precision and efficiency in patient care. The Morisky scale plays a crucial role in ensuring patients adhere to their prescribed medications, directly impacting treatment outcomes by preventing disease progression, reducing complications, and enhancing overall health outcomes. Recognized as the gold standard for assessing medication adherence, the MMAS-4 and MMAS-8 have been referenced in over 31,298 academic journals. This new partnership combines IMC’s cutting-edge dose analytics with Adherence's expertise to create a solution that integrates patient-reported MMAS data with objective adherence information. Through this collaboration, IMC will seamlessly integrate MMAS with the CertiScan Adherence Insight Platform, eCAP, and Med-ic, providing partners with a comprehensive suite of tools for advanced adherence monitoring and improved patient outcomes. A key focus of this partnership is the development of APIs for third-party platform integration. This initiative will enable industry partners to leverage the benefits of this innovative solution across various healthcare settings. Dean Brotzel CTO of IMC, emphasized, “The digital MMAS integration marks a significant milestone in medication adherence monitoring. By leveraging combined data sources, we are transforming the approach healthcare providers and patients take towards medication adherence.” For media inquiries or more information on the groundbreaking collaboration between IMC and Adherence, please contact [email protected] or [email protected] Michael Petersen the company called adherence. . Information Mediary Corp. (IMC)

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Join me on The Doctor’s Farmacy as I sit down with two incredible experts, Vijay Pande, PhD and Daisy Wolf from a16z Bio + Health, to explore how AI is set to revolutionize healthcare. We dive deep into the world of cutting-edge tech, like wearable devices and AI diagnostics, making personalized medicine more accessible and effective than ever before. I truly believe in the transformative power of AI in healthcare. The synergy between doctors, patients, and AI holds immense potential to improve the health and lives of countless individuals. That's why I co-founded Function Health —to harness this powerful relationship and bring advanced diagnostics and treatments to those who need them most. Catch the full episode here: https://lnkd.in/gbgDSDKp

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#HealthAI #Ai is revolutionizing every aspect of our lives, including healthcare. But are we ready for this change? AI is transforming medical diagnosis: systems now analyze X-rays, MRIs, and ultrasounds with unprecedented speed and accuracy, identifying critical issues in real-time. Companies like Aidoc have developed technologies that detect brain hemorrhages and tiny tumors often missed by human doctors. The advantages are clear: 1. Faster #diagnosis, potentially saving lives 2. Increased accuracy in identifying subtle patterns 3. Reduction of human error However, we must remember: 1. Humans tend to over-trust #technology, especially AI 2. AI can make mistakes, particularly with incomplete or inaccurate data 3. AI is a powerful tool but doesn't replace medical judgment (yet?) The key is combining AI capabilities with human expertise. This collaborative approach leverages the strengths of both, leading to better outcomes.

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https://tcrn.ch/3xszfxx Interesting piece in TechCrunch about what #generative AI could mean for healthcare. Some key points: ⭐ The article described the potential of using generative AI in #radiology. ⭐ Worth noting that both examples given in the radiology section aren't actually #generativeAI though (both examples are machine learning). ⭐ Despite enthusiasm from the investor community, patients and clinicians are less sure about its value ⭐ Only 53% of surveyed consumers thought it could add value in healthcare ⭐ A paper in JAMA Pediatrics found ChatGPT made errors diagnosing pediatric diseases 83% of the time Despite reservations, I think there's no doubt that generative AI could be game-changer, especially for routine, mundane tasks. Be interesting to hear what others think ! #radiology #AI

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AI has made good design more important than ever in healthcare. Why? Model performance alone doesn't guarantee clinical or financial value. A model can deliver a robust insight/output (accurate, reliable, complete, etc.), but clinician-users may struggle to use or integrate it in a real-world setting. As healthcare AI systems become compound machines, these design challenges amplify. Here are two simple examples: • AI systems that do not integrate seamlessly into EHRs, PACS, or pathology viewers may drive differential rates of utilization/adoption across sites, privileging some patients to a given algorithm without an underlying clinical rationale. • Imagine a model recommending a "next best step in care" that is not provided at a given hospital (e.g., due to lacking resources or infrastructure). Contextual and human factors can have a greater influence on whether the value of an AI intervention is realized, even when in silico model performance is compelling. There are two near-term opportunities here: 1) Healthcare AI model benchmarking should encompass "end-to-end systems," capturing nuances around system design, user experience, workflow integration, and ease of adoption. 2) It's time for AI-first design in healthcare – made possible by revisiting the unique capabilities of AI systems and their emergent behaviors to interrogate what new user experiences are possible. What are your favorite examples of AI-first design? #healthcare #ai #artificialintelligence #generativeai

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🎙️ Thrilled to share that V. Tyrone Lam, COO of GATC Health, was recently featured on the Innovation Wave Podcast! In this insightful 20’ episode with Bob McGriff and me, Ty delves into how AI-driven predictive analytics can transform our approach to disease treatment, making it more proactive and less risky. He shared compelling insights on the future of healthcare and how predictive tools can lead to earlier interventions and better patient outcomes. A must-listen for anyone interested in the cutting-edge of medical technology and AI! 👉 Check out the episode to learn more about Tyrone's vision and GATC Health's pioneering work in this exciting field. Let's discuss how these innovations are reshaping the landscape of healthcare! #HealthTech #AI #PredictiveAnalytics #Innovation

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If you're seeking a comprehensive overview of the life sciences and healthtech sectors, and investment/co-investment opportunities, our mid-year report is packed with valuable insights from our team. 📊 #LifeSciences #InvestmentOpportunities #MidYearReport

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