Yael Garten

📢 New Blog Post: NeurIPS Datasets & Benchmarks Track - From Art to Science in AI Evaluations we just published a new blog post that provides an update on the 2025 Datasets and Benchmarks Track, reflecting on how the new hosting and metadata requirements affected submissions and the review process. We present submission statistics, survey findings from 851 authors and 155 reviewers, and identify areas requiring continued development. This differs from our earlier post on the review process by focusing on empirical outcomes rather than procedural changes. with Konstantina Palla , Francesco Locatello , Joaquin Vanschoren , Megan Risdal https://lnkd.in/eHxQTvnh

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Our recent peer-reviewed paper on JMIR AI: The Leading Journal for Real-World AI Applications in Medicine with Mount Sinai Health System evaluates the performance Tailored #NLP Pipelines for Radiology, Pathology, and Progress Notes in #ClinicalDecisionSupport: https://lnkd.in/gcmFpxEK #ai #research #healthcareai #healthai #datascience #textmining

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🚀 Just published a practical introduction to LiteLLM — a lightweight but powerful tool to manage multiple LLMs across vendors. In many GenAI teams, costs spiral and wrappers multiply when you start working with many different LLM providers. LiteLLM (YC W23) provides: ✅ A unified OpenAI-compatible API framework ✅ A self-hosted proxy server with built-in monitoring, quotas, and API key management ✅ Out-of-the-box integration with LLM orchestration libraries such as LangChain and LlamaIndex. In this guide, I walk through a full setup — from Helm Charts to code — and share a few things I learned along the way.

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Interesting paper alert - a commentary in Nature Medicine highlights a pivotal tension in healthcare today: while #AIagents promise transformative impacts — from autonomous triage to multi-modal clinical decision support — regulatory frameworks remain built for a different era (a recurring scheme in the world of Health AI which I do not see going away anytime soon: technical progress in AI will always outpace regulatory actions). Here are the take aways from the paper: 🔹 Current state of regulation of AI agents in health and medicine: Most regulatory pathways, like the EU Medical Device Regulation and the U.S. FDA’s 510(k), are optimized for narrow, static, non-adaptive tools. Approval of AI-driven devices with broad scope and high autonomy remains rare and challenging. 🔹 The problem (why existing regulatory frameworks fall short of capturing agentic AI applications): AI agents orchestrate multiple tools, access sensitive data, and make goal-directed decisions autonomously. Their adaptive nature, coupled with non-deterministic outputs and complex multi-component architectures, pushes them outside the assumptions underpinning today’s medical device regulation. 🔹 Proposed solutions (how can we explore and experiment safely with AI agents in clinical settings in the absence of comprehensive regulatory guidance): The paper explores several emerging regulatory strategies, including: Voluntary alternative pathways → tailored approvals for high-risk AI agents Adaptive oversight → dynamic, real-world performance monitoring Regulatory sandboxes → controlled environments to experiment safely Outcome-driven evaluation → focusing on patient-centered results rather than static technical compliance 💡 The takeaway (so where does this leave us): While regulation is still playing catch-up with the risks and complexities of AI agents in healthcare, it is possible to safely experiment and sandbox their use — provided there are clear guardrails, strong oversight, and rigorous evaluation frameworks. This balanced, sandbox-first approach could enable innovation without compromising patient safety. For first hand insights on how this is all playing out in the real world right now, have a read of these two recent papers from Eric Topol, MD (https://lnkd.in/gEycp__S) and Harsha Nori (https://lnkd.in/gKNxpxsA) John Lim Christopher Bain ZongYuan 宗元 Ge 戈 Deval Mehta #AI #Healthcare #Regulation #AIagents #DigitalHealth #NatureMedicine

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Our research team just published a new research paper. This study reveals that generic LLMs (e.g., Gemini, Cluade, GPT) often miss critical nuances in complex clinical decisions, raising questions about their reliability in real-world care. We, like others, hypothesized that the physician-in-the-loop would tame LLMs; however, in our study, clinician satisfaction did not correlate with objective metrics of literature quality, suggesting that satisfaction was not anchored in evidence quality. While those models perform well on simple queries, they can “sound right but get it wrong” when patient context and complexity increase. At Medint, we’re building AI designed to strengthen, not replace, clinical judgment, keeping clinicians at the center of decisions. I want to thank Sharon Einav, MSc, MD, Daphna Idan, Itamar Ben Shitrit, Mark V. for leading this project. Full study: https://lnkd.in/dd99A6DV Press release: https://lnkd.in/dtQVcQr9

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Insightful paper in Nature Medicine on the use of LLMs by lay users to triage symptoms. It shows how variable the output can be and how small changes in prompting can affect health related responses from LLMs. To me, it highlights the importance of follow up questions to gather more context and better understand the presentation before providing recommendations. Paper here and NY article gift link.

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In a recent Wall Street Journal article, Tom Rosenblatt shares his journey of overcoming chronic pain with the assistance of Claude, an AI chatbot developed by Anthropic. After traditional medical tests yielded no answers, Claude provided personalized insights that led to effective relief strategies. This narrative exemplifies the transformative potential of AI in healthcare—a central theme in my book, Smarter Healthcare with AI. In it, I delve into the VP4 Framework: Purpose, Personalization, Partnership, and Productivity, illustrating how AI can revolutionize patient care by offering tailored solutions and fostering collaborative relationships between patients and providers. Rosenblatt’s experience underscores the importance of integrating AI thoughtfully and ethically into healthcare systems. By embracing such technologies, we can move towards a more responsive and personalized healthcare paradigm. For those interested in exploring how AI can be harnessed to enhance healthcare delivery, I invite you to read Tom’s story and explore more in Smarter Healthcare with AI. #AIinHealthcare #SmarterHealthcare #PersonalizedMedicine #HealthcareInnovation #VP4Framework

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UCSF Chair of Medicine Robert Wachter (author of "The Digital Doctor" and "A Giant Leap") on Doctronic in his Substack: "Personally, I'd have no problem using this system to refill my Lipitor." He also flags that we may be the first company to buy malpractice insurance for autonomous AI in medicine. (We are.) I'm genuinely humbled by the mention. Bob has shaped how a generation of physicians thinks about technology in medicine. Everyone should read his take on why this week marked "the moment AI moves from advising on care to actually delivering it": https://lnkd.in/eqpFgC2Y

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I read Eric Siegel's piece “The Agentic AI Hype Cycle Is Out of Control” (https://lnkd.in/e3QnzGyn) And I couldn’t agree more. Except I’d go further. We’re not just in a hype cycle. We’re in a hype economy - where narrative is the product, and analysts have become chief hype officers, turning fiction into forecast. The term 'Agentic AI' sounds profound - like we’ve entered a new phase of machine autonomy. In truth, it’s a marketing skin graft on top of GenAI - the same underlying LLMs, now re-packaged with the language of agency to extend the wave. It’s not a new technology. It’s a new label for an old wish: that AI will soon think, act, and decide for us. But autonomy isn’t a product feature - it’s a philosophy. And right now, we’re mistaking philosophical potential for technical capability. Siegel nailed it: the Gartner Hype Cycle doesn’t analyse hype - it amplifies it. By treating “inflated expectations” as an inevitable stage of progress, it turns exaggeration into a rite of passage. The message? “Don’t worry if your product can’t do what you claim - this is how innovation works” That’s not thought leadership. That’s institutionalised delusion. Hype isn’t harmless. Hype is costly. It distorts priorities, misdirects capital, and erodes trust between technologists and executives. When the crash comes (and it will) the fallout won’t hit analysts or influencers. It’ll hit the builders, the engineers, the delivery teams… the people cleaning up the mess of expectations inflated beyond physics. When companies start timing their press releases around where they should be on the curve, rather than what they’ve actually achieved - you’re no longer managing technology. You’re managing a play. We’ve normalised hype as if it’s a necessary evil. It’s not. It’s just lazy storytelling. As technologists, we don’t need more agentic AI. We need more accountable AI - systems that do what they say, leaders who admit what they don’t know, and analysts who stop selling the map as the destination.

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Happy about the recent AI news from Google DeepMind! Their new AI development, “Alpha Genome," feels like a monumental leap forward, echoing the groundbreaking work of AlphaFold. For those of us who ventured into the world of AI and its application in genomics early on – remember the excitement around the Human Genome Project in the early 2000s? And the dawn of using AI to mine vast amounts of data for the betterment of human life. Back in John McDonald’s lab, as young graduate students on a bold quest, we dreamt of connecting the dots between genetic code, gene expression, proteins, and metabolites to finally crack the code of diseases like cancer. It was a wild idea, and while we saw some success with expression profiles, the "omics" field was still finding its footing. Fast forward two and a half decades, and it’s incredible to witness how AI is finally coming with solutions to make those "wild dreams" a tangible reality! Alpha Genome promises to help researchers and problem-solvers understand exactly how changes in our DNA influence gene activity, and ultimately, our health – all on a massive and accelerated scale. The specific call-out to applications for cancer cures and treatments for rare diseases truly resonates with my early aspirations of the field. The potential benefits to our lives are immense, and I can't help but feel a surge of hope and excitement for what's to come. Just imagine the combined power of Alpha Genome and AlphaFold! This is a truly thrilling time to be witnessing such transformative advancements. Read more about this exciting breakthrough here: https://lnkd.in/g_yKkBR6 #AlphaGenome #DeepMind #AI #Genomics #CancerResearch #RareDiseases #Science #Innovation

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