🩺 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#

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

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:

Healthcare spending per person 1973: $441 2026: $15,474 Home Price 1973: $35,500 2026: $475,400 Rent 1973: $133 2026: $2,017 Public College Tuition/Fees 1973: $514 2026: $11,950 Has your income kept up with these rising prices? Probably not. That's why so many are struggling.