🏥 Deploying agents in healthcare and life sciences means answering for audit trails and patient safety, not just product quality. Here's what three real deployments have in common. Title: Scaling Agents in Healthcare & Life Sciences: Lessons from Madrigal Pharmaceuticals, Abridge, and Vizient URL: LangChain's analysis of the industry finds that the teams furthest along build observability and evaluation infrastructure alongside the agents themselves. Three case studies make the pattern concrete. Highlight ①💊 Madrigal Pharmaceuticals Normalized scattered data formats into a warehouse and rebuilt around Deep Agents with an orchestrator plus modular skills. New use-case development dropped from weeks to hours, and deployment shrank from months to weeks. Highlight ②🩺 Abridge As its clinical-documentation agent scaled to 250+ health systems, the team built LLM judges around quality pillars and a tiered release process. Judge creation dropped from days to hours, release cycles from 1-2 months to days, and accuracy/completeness improved by 17% and 19%. Highlight ③🏢 Vizient Replaced siloed multi-agent coordination with a supervisor-led hierarchy, separating prompts from code. That unlocked real-time diagnosis of errors and much faster onboarding of new data sources. The lesson: baking trust in from day one, instead of bolting it on later, is actually the fast path to greater agent autonomy. #AIAgents# #HealthcareAI#

🩺 For health questions, accuracy and safety matter most. On hard self-harm and suicide conversations, GPT-5 cut undesired answers by 52% versus GPT-4o. 📰 Title: Improving health intelligence in ChatGPT 🔗 URL: 💡 Overview OpenAI shared its work on improving ChatGPT's health intelligence. It frames GPT-5 as its best model yet for health questions, with a big jump on HealthBench, a benchmark scored against physician-defined criteria. 🔍 Challenges Solved In health, mistakes directly affect user safety. Plausible-but-wrong answers (hallucinations) and missed urgent situations are serious risks, so accuracy, clarity, and appropriate encouragement to seek clinical care are essential. 🛠 Methodology & Proposed Approach ・Evaluated on HealthBench, scoring responses against realistic scenarios and physician-defined criteria ・HealthBench Consensus has hard cases validated by 2+ physicians ・Built a Global Physician Network of ~300 physicians and psychologists who practiced in 60 countries to inform safety research ・Advisors reviewed 700,000+ model responses reflecting real-world use 📊 Use Cases / Results ・52% fewer undesired answers on hard self-harm and suicide conversations vs GPT-4o ・8x fewer hallucinations on hard conversations from o3 to gpt-5-thinking ・Over 50x fewer errors in potentially urgent situations vs GPT-4o Useful for understanding symptoms and lab results, judging when to see a doctor, and being nudged toward appropriate follow-up care. #ChatGPT# #HealthcareAI#

Loved talking healthcare AI with @DhruvKhullar and @brindaadhikari (and team) on the "Why Should I Trust You?" podcast (@WSITYpod). On the menu: should patients trust AI, impact on med ed, who's liable when it errs – and, inevitably, whither doctors.

10 WAYS TO INVEST IN HEALTHCARE AI • $WGS uses exome & genome sequencing to diagnose rare diseases • $PRCT provides waterjet-based robotic systems for treating enlarged prostate • $VEEV provides cloud software & data infrastructure for life sciences workflows • $DOCS provides physicians with AI-enabled clinical workflow & productivity tools • $GH uses liquid biopsy & genomic data for cancer detection & treatment selection • $ISRG powers robotic surgery through the da Vinci platform & growing software stack • $ABCL uses microfluidic screening & AI to discover antibodies for biopharma partners • $HIMS delivers telehealth prescriptions with personalized formulations & dosing at scale • $TEM combines multimodal clinical data with AI to improve diagnostics & treatment decisions • $TWST supplies synthetic DNA used in protein design and directed evolution across computational biology

Something I keep hearing from healthcare leaders is how the best strategic thinking never makes it out of the silo. Everybody's heads-down, there’s so much noise with healthcare and AI, and nobody's comparing notes. That's exactly why I’m hosting the Hospitalogy AI Retreat this November in Phoenix. 100 healthcare VP+ execs from finance, strategy, M&A and digital health, working to untangle healthcare AI and digital transformation together. If you want to be in the room, apply at

In the past few months, I've seen many new R&D lab startups now building in the medical AI space... It's exciting to see more energy in the field (although some folks are claiming slightly wild things like being the first frontier healthcare AI company) Seeing the progress has led me to reflect on our journey at @SophontAI and specifically how early we were with our conviction in the importance of a frontier medical AI lab. Back in beginning of 2025 when we started, the idea of a frontier medical AI lab was foreign to investors and researchers. The healthcare field wasn't foundation model-pilled enough, while the AI field had poor understanding of how medicine works. But I laid out a thesis for why this is needed and luckily a few amazing investors believed in it. I think a year and a half later, my thesis as a whole is starting to be vindicated and the field is picking up on it as well! Link:

Now that we've hit Peak AI Backlash, I'm resurfacing my May Substack on what happens to healthcare AI in the face of a major AI backlash. Up until now, healthcare has been the one area in which the public has been unambiguously enthusiastic about AI.

Join me and visionary thinker and investor Eric Larsen, president of TowerBrook Advisors to discuss the idea behind Eric's latest essay, Healthcare's Oppenheimer Moment: AI is the industrialization of intelligence, and healthcare — expensive, labor-heavy, and sitting on decades of dormant data — is ground zero for what happens next. Watch me pressure-test his healthcare-AI thesis in real-time. Just some of what we'll get into: • Why Eric calls revenue cycle the coding of healthcare labor automation. • How to map AI exposure by task, not job title. • Who will be the real winners in the bot war? • Where and how Eric puts his own conviction on the line. • Why a single EHR instance is table stakes, not a revenue cycle strategy. Free - register now! @TowerBrook @Hospitalogy_HC

AI tools are playing an increasingly large role in mental health care, but the governance frameworks needed to ensure they are safe and effective are lagging behind. A policy workshop, hosted by @StanfordHAI's Healthcare AI Policy Steering Committee in collaboration with @StanfordMed's AI for Mental Health Initiative, identified three critical challenges policymakers need to address: