James Mault, MD, FACS

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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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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The role of AI and new technology to aid cardiology “As cardiology starts to face staffing shortages and continued decreases in CMS payments, Biga believes technology advances need to be leveraged in cardiology to help improve outcomes, make workflows more efficient to do more with less, and to help cut costs.” “Many of the solutions that can help were displayed on the expo floor, including telehealth and real-time remote monitoring systems. "I think this area will experience an explosion in use," Biga said. Another major area that will likely see an explosion of use is artificial intelligence (AI). She said AI will begin playing a rapidly growing role in cardiology in the coming years to augment cardiologists and other clinical staff. And not just on the front lines of patient care, but also in the back offices where AI will play a growing role in IT systems, smoothing workflow, analytics, automated quality metrics capture and many other areas. "We must use AI to reduce administrative burdens and improve patient outcomes," Biga explained. “

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“Cardiology continues to have the second largest number of clinical artificial intelligence (AI) algorithms cleared by the U.S. Food and Drug Administration, trailing only radiology. The FDA's latest update covers all AI approvals through May 2024. There are now 122 FDA-cleared AI algorithms specific to cardiology. This makes up 14% of the 882 clinical AI algorithms now on the U.S. market. The new FDA numbers include market clearances up to March 31, 2024. The first approval of a clinical AI algorithm was in 1995. The first cardiology specific algorithm was for HeartFlow FFR-CT in 2016. That algorithm was also the first AI to be included in a cardiology guideline when the 2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain was released.” MedTech Innovator American Heart Association #epeeps Heart Rhythm Society American College of Cardiology LSI Capital Factory Company Ventures MedTech Strategist Health Wildcatters Techstars

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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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Like an unfazed young quarterback who keeps firing it downfield despite a spate of interceptions, CMMI keeps tossing around VBC models hoping something will connect. Contrary to its intent, CMMI's programs increased spending by $5.4 billion from 2011-2020 with another increase in net spending of $1.3 billion by 2030. Of the more than 50 models the Center has implemented, only 6 have generated "statistically significant" savings. Only 4 (Home Health Value-Based Purchasing, Prior Auth Model for Non-Emergent Ambulance Transport, Diabetes Prevention Program, and Pioneer ACO) were certified for expansion. CMMI points to the voluntary nature of the programs as a challenge to achieving cost savings. However, CMS hasn't done itself any favors here by aggressively ratcheting down payments and making downside risk >>> upside risk. (Notably, two upcoming programs -- TEAM and Inpatient THA/TKA PROMs are mandatory). Finding the VBC sweet spot was always going to require some trial and error. Perhaps CMMI has learned from the programs that failed or weren't chosen for expansion. TEAM doesn't seem to be all that different from CJR. Making Care Primary aims to closely integrate primary and specialty care (or, more likely, incentivize PCPs to take on more of this work). Not surprisingly, one of the criticisms is lack of provider input. The programs are too complex, too expensive to administer, and aren't transparent. CMMI should take note of several studies demonstrating the success of physician-led VBC efforts. Here are some other thoughts: - Offer financial grants in 5- or 10-year terms for any organization that designs its own VBC program and presents a reasonable and viable clinical/business model. CMMI would do itself a favor by partnering with or at least listening to innovators. - Medicare Advantage complexity and risk adjustment handwaving shouldn't be the only mechanisms by which advanced providers can profitably care for beneficiaries. That program isn't saving money either, but it's the only way many of these approaches can generate meaningful revenue (and most still aren't profitable). - Have regular meetings with those building VBC models, particularly those with strong physician leadership. Those meetings should be transparent and productive of action plans and iterative changes that increase model viability. - Be more open about what your goals and targets are. No one should be a victim of their own success, and if you keep moving the goalposts, you shouldn't be surprised when people get frustrated and drop out. I'm certain that, given fair incentives, a meaningful voice, and an equal seat at the table, healthcare innovators can design sustainable VBC programs. But there needs to be more collaboration and a willingness to listen and engage. Otherwise, you're just slowly adding to the alphabet soup of also-ran programs. #medicine #healthcare #health #vbc #healthcareinnovation #valuebasedcare

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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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New research reported by Modern Healthcare -- a predictive AI tool in an electronic health record to generate a risk assessment score informs clinicians which patients are more likely to be violent. To quote: “The development of the AI tool comes as violence against doctors and nurses is on the rise. More than 80% of nurses said they experienced some form of workplace violence in 2022 and 2023.” AI is highly useful, when deployed the right way, on many fronts, especially in ways that benefit providers – as well as patients. https://lnkd.in/gY4YjVTe

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JCHI's Christopher Longhurst emphasizes the importance of #ethicalAI development and deployment, guided by principles that ensure fairness, safety, and effectiveness during a recent convo with Tal Behar of PMWC - Precision Medicine World Conference. See more below!

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I agree every single word. Yes, technology might not be the most important part, but how to streamline use cases and payment system is so crucial

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lol How is AI weaving its way deeper in to healthcare….🤔 As we navigate the evolving landscape of healthcare, the conversation around AI often gravitates towards its potential risks and downsides. However, there is also a different narrative—one where AI serves as a transformative tool for specialists making critical decisions about their patients’ and clients’ care. While some may fear that AI aims to replace healthcare professionals, the truth is far from that. AI is not about replacing the invaluable human touch that specialists and prodessionals provide, nor is it merely about speeding up processes. Instead, it’s about augmenting their capabilities, offering new perspectives, and providing a deeper understanding of each individual’s unique symptoms or scenario. AI tools like the ones we use at FirstHx empower regulated healthcare professionals and physicians with advanced insights and clarity. They offer an array of perspectives on how symptoms can be indicators or catalysts for various conditions, ultimately helping guide the professionals to make decisions around next steps in treatment or advice. This technology enables more focused and individualized care, ensuring that every action is tailored to the specific needs of the patient. By leveraging AI, healthcare professionals gain new tools that enhance their ability to serve their populations more effectively. It’s not about replacement; it’s about empowerment and improvement. AI in healthcare is about giving specialists the vision and clarity to make more informed, precise decisions, leading to better outcomes for all. Check out how we are assisting professionals strive for optimal outcomes at First HX. Here is our founder Mark Benaroia, MD, MHSc, FRCPC breaking down how the company and technology came to fruition in his latest appearance on the Maker’s Bar Podcast. https://lnkd.in/grna7gqf #AIinHealthcare #HealthTech #Innovation #PatientCare #HealthcareProfessionals #FirstHx #FutureOfHealthcare #mentalhealth #behaviouralhealth #AI #physician #psychology #BCBA #socialwork #medical

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Reflow Medical, Inc. has successfully completed enrollment in the DEEPER REVEAL clinical trial (NCT05358353) to evaluate the Reflow Spur™ Stent - an innovative device designed to provide stent-like results while leaving no permanent metal behind. The study enrolled 130 patients across 35 clinical centers in the United States, highlighting the significant unmet need for effective treatment options for critical limb-threatening ischemia (CLTI). The Spur Stent is the first Retrievable Scaffold Therapy (RST) featuring a retrievable stent with radially self-expanding spikes and is designed to treat diseased vessel walls without leaving any permanent implants. Lead Principal Investigators, Dr. S. Jay Mathews and Dr. Mahmood K. Razavi, expressed excitement about the potential benefits of the device based on promising clinical data from previous trials. The DEEPER REVEAL study aims to assess the efficacy and safety of the Spur Stent in treating vascular lesions in the infrapopliteal arteries below the knee. The study's findings will support the company's efforts to obtain U.S. approval for the device. Reflow Medical, Inc. is committed to providing innovative solutions for cardiovascular disease treatment. Their portfolio includes products for treating both coronary and peripheral vascular diseases, empowering physicians to offer effective and less invasive care to patients. Teo Jimenez Isa R. John Fulkerson

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Synthesis of AI in Healthcare Fireside chat Dear fellow pathfinders, gamechangers, and trailblazers. Here is a brief synopsis of this highly relevant discussion topic I attended at the HeAL conference in Las Vegas last week. How AI is changing our outlook on modern day healthcare Ian Slade, CEO | HPMA; Ravi Tenneti, Senior Vice President; Eddy Pham, CEO | Infortum.io The fireside chat explored how AI transforms modern healthcare. Here are the take aways: As AI becomes a more prominent aspect of healthcare applications and operations, critical thinking for the individual provider who is using the AI is even more important. AI is a tool that can be employed in multiple use cases to solve complex data aggregation and formatting of such data to assist humans in making decisions based on this complex data. It is a tool of augmented intelligence. But clinical judgment and the understanding of basic ethical principles in healthcare remains an integral and irreplaceable part of care. AI provides better predictive analytics compared to humans, but input data may be biased. Understanding the underlying data science principles to recognize these biases in the algorithm and final output is paramount. The data must then be interpreted in the context of the human experience and within our societal values. The slower speed of AI adoption in healthcare is related to the juxtaposition of high value to high risk, as we are working with real life health outcomes. Predictions are never 100%, and safer applications such as claims processing are excellent for faster adoption. Streamlining operations and resulting cost reductions lead to increased value creation. AI in healthcare is an excellent tool of augmented intelligence, but the a) critical evaluation by the human expert, b) a clear definition of a confidence levels for data outputs defined for different risk levels (treatment decisions versus claims processes), and a c) close collaboration of providers and healthcare stakeholders with data scientists are key in the successful integration of AI as a meaningful tool into clinical practice and healthcare as a whole. In the end, the human connection remains at the center of healthcare. We have a duty to protect the autonomy of our patients – the most vulnerable of all stakeholders – and through ongoing open dialogue ensure that they are able to make informed decisions about their health and receive fair, just, and unbiased care. Thank you to the discussion panel Ian Slade, Ravi Tenneti, and Eddy Pham for such an insightful talk! Katharina

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There's no question that AI is beneficial in improving healthcare—AI can analyze large datasets, streamline care pathways, and bolster healthcare facilities' cybersecurity measures... However, generative AI in particular has spurred more conversation about whether it's beneficial for patients. In a recent Deloitte survey, only about half (53%) of U.S. consumers said that they thought generative AI could improve healthcare. “One of the key issues with generative AI is its inability to handle complex medical queries or emergencies... Its finite knowledge base — that is, the absence of up-to-date clinical information — and lack of human expertise make it unsuitable for providing comprehensive medical advice or treatment recommendations," says Andrew Borkowski. I'm curious—do you agree with him? Or do you think that generative AI can transform the healthcare industry for the better? #HealthcareIndustry #GenerativeAI #AIInHealthcare

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Med Tech in Focus:   Wearable Ultrasound Patch Offers Continuous Cerebral Blood Flow Monitoring Researchers at University of California San Diago have developed a postage-stamp sized ultrasound patch that can continuously monitor three-dimensional cerebral blood flow in real time. The technology is worn on the temple and is composed of silicon embedded with several layers of soft, stretchy electronics that produce and enhance ultrasound signals. Currently, a technician uses a Doppler ultrasound device on a patient’s head at specific times each day to monitor blood flow. That procedure has limits because such measurements can vary depending on the accuracy of the technician and data can fluctuate between readings.  The device is connected by cables to a computer and uses ultra-fast imaging and AI to collect and post-process data about the brain’s blood vessels. Patients who are undergoing and recovering from brain surgery could benefit from the device and it’s hoped that the continuous monitoring could tell if a patient is about to experience a stroke and provide an opportunity for timely intervention. VC Investors are always adjusting their "Play Book" with a variety of options for investing. Mikal Ventures is one of those alternatives, offering curated solutions to real world problems. Reach out if you have questions on how we can be of assistance and bookmark https://mikalventures.com/ for additional insights and information.

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Wonderful article on how, with the latest technological advancements, Theranostics is more relevant than ever. The future of healthcare is personalized medicine, and as the article states, AI is reliant on data. Nuclear medicine is very data-rich.

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While I generally talk about pivotal moments for CARPL, here is a pivotal moment for the AI ecosystem IMHO. While safety of AI training and validation is often the subject of discussion and debate, safe consumption of the outputs of those AI applications in a world of interoperability is less discussed. CARPL recently received FDA clearance for our platform and we believe that we are possibly the platform with the largest and most diverse AI marketplace out there - this puts our money (processes!) where our mouth is - safe deployment of AI, and our exponentially increasing focus on the US market. It is a feeling of immense pride for us at CARPL - Radiology AI Platform to lead the global AI platform ecosystem! #MakingAIreal #UnboxingAI #RadiologyAutomationSimplified https://lnkd.in/g-mDXJMi

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Generative artificial intelligence is infiltrating peer review process 🤖 Navigating the New Frontiers: Generative AI in the Peer Review Process As the influx of manuscript submissions grows, the peer review process in scientific research faces increasing challenges. Recent advancements have seen generative AI tools like #ChatGPT being tested to support this crucial aspect of publication. However, their integration raises several ethical and practical concerns, including confidentiality and the preservation of critical human judgment. This recent study highlighted in Critical Care explores these dynamics, demonstrating both the potential efficiencies brought by AI and the complexities it introduces. What are your thoughts on this development? Can AI truly complement the human element in peer review without compromising the integrity and thoroughness required in scholarly publishing? #PeerReview #ArtificialIntelligence #ScientificPublishing #EthicsInAI Artificial IntelliGence in Anestesia e Terapia IntensivA Elena Giovanna Bignami Marco Cascella Jonathan Montomoli Valentina Bellini

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