Artificial Intelligence

OpenAI pushes ChatGPT into patient health records

Published

2 months ago

August 12, 2026

OpenAI’s ChatGPT Health feature goes live

OpenAI has switched on a new Health feature inside ChatGPT that lets users link Apple Health data and medical records directly to the chatbot. Anyone logged in, aged 18 or over, can access it now on web and iOS, across the Free, Go, Plus, and Pro tiers.

The integration pulls in medications, lab results, recent visits, sleep data, and activity logs. Once synced, ChatGPT can use that context in any conversation, not just a dedicated health section. That’s a deliberate design shift, and it stems from something surprising the company found during early testing.

Why OpenAI redesigned the health experience

Earlier, OpenAI ran a pilot where users had to open a separate health area to get responses grounded in their own data. The result? More than 70 percent of health-related conversations happened elsewhere—smack in the middle of meal planning or an unrelated symptom query, not inside the dedicated space.

That insight drove the redesign. Instead of forcing users into a specific mode, ChatGPT now draws on connected health information across any conversation, provided the user has granted permission. A person planning a dinner out might get a restaurant suggestion that accounts for a logged dietary restriction. Someone asking about weekend plans might get activity suggestions adjusted for a recent injury noted in their synced records.

The Health tab in the sidebar still exists, but its role has shifted to being a management hub: connecting accounts, reviewing synced data and trends, browsing suggested prompts, and returning to past health-related chats.

What early testers of the Health feature in ChatGPT report

OpenAI published numerous accounts from its early access group, and the details are worth weighing against the company’s own framing of the tool as support rather than diagnosis.

Blake, a technical program manager, said: “The most useful part has been turning scattered medical history into something I can actually understand and explain. I have multiple overlapping issues and ChatGPT helped connect those pieces into a clear timeline, explain the medical terms in plain English, and create summaries I could share with a physical therapist or trainer.”

On the shift from disconnected records to a usable pattern, Blake added: “Instead of just seeing disconnected diagnoses, imaging results, and surgery notes, I could understand the bigger pattern. It made the information more usable and gave me better language to advocate for myself with providers and trainers.”

Reweti, a portfolio manager, pointed to longitudinal analysis as the differentiator over a standard search or a one-off doctor visit: “What I want is to infer patterns that aren’t obvious and make connections I wouldn’t have made on my own. ChatGPT can access my existing labs because I’ve connected everything, and it can look over time—that’s the big advantage. It’s like having a research analyst. It allows me to be more proactive and own more of my health journey.”

However, not every account was frictionless, and one is worth flagging given the stakes involved in surfacing clinical data through a chatbot interface. Shannon, a nurse, described finding an unexpected entry in her own chart through the tool.

“Using Health has actually reinforced something I’ve believed for a while: one of AI’s greatest strengths isn’t replacing healthcare professionals, but helping patients better understand and navigate their own health information,” explained Shannon. “Discovering an unexpected chart entry through Health really highlighted that for me. It wasn’t AI creating a problem. It helped me identify something I can now appropriately follow up on with my healthcare providers.”

That distinction—between a tool surfacing something for human follow-up versus a tool making a clinical call—is the line OpenAI needs the product to hold as usage scales.

Other testers focused less on clinical nuance and more on the practical grind of manual data wrangling the feature is meant to replace. Daniel, a consultant, connected multiple sources and found the combined view more useful than isolated chat sessions.

“It’s been great for coordinating labs and translating them into language I understand. Connecting Apple Health and MyChart makes the insights more grounded in what is happening across my life outside of just chat interactions,” said Daniel.

Kathleen, a small business owner who’d lost a decade-long fitness habit to work pressure, described a lower-stakes but still concrete use case, with the model adjusting suggestions based on activity gaps it could see directly.

“I’ll say, ‘I need something to help me move today,’ and Health can see I haven’t worked out in the last seven days and suggest starting slowly—with a walk or some stretching. It’s helping me make things manageable and get back into it in a reasonable way,” explained Kathleen.

Carlton, an operations manager, had previously resorted to exporting spreadsheets from Apple Health and uploading them manually before every chat, a workaround the new integration is designed to eliminate.

“Prior to Health, I was exporting massive spreadsheets from Apple Health and importing them into ChatGPT. Now, it feels a lot more streamlined. Being able to see years and years of my fitness journey in Health helped me see things in a different light—a bigger picture,” said Carlton.

OpenAI puts weekly health-related ChatGPT queries at north of 300 million people, covering everything from decoding a lab result to prepping for a doctor’s visit. That figure, if accurate, puts ChatGPT in a position most digital health platforms would need years and considerable marketing spend to reach.

The company is explicitly positioning Health as a support tool rather than a diagnostic one, and telling users to confirm anything important with their actual healthcare provider.

OpenAI’s AI model performance claims and how they were tested

OpenAI attributes the feature’s viability partly to newer models: GPT-5.5 Instant, available to Free users, and GPT-5.6 Sol, reserved for paid tiers.

The company says GPT-5.5 Instant made gains in recognising when urgent care might be needed and in explaining uncertainty, and that on its toughest health evaluations it performed comparably to OpenAI’s frontier Thinking models at the time. GPT-5.6 Sol is described as the company’s strongest health model so far, built for reasoning across multiple data points such as lab trends over time.

To validate these claims, OpenAI says it worked with hundreds of physicians to build health scenarios and rubrics scoring responses on accuracy, safety, communication, context awareness, completeness, and appropriate escalation to professional care. The company reports that every GPT-5.6 model outperformed GPT-5.5 on HealthBench Professional, an internal evaluation built for this purpose.

A chart included in OpenAI’s announcement shows GPT-5.6 Sol scoring higher than GPT-5.5 Instant and GPT-4o across categories including accuracy, communication, completeness, and following instructions, with physician-written responses used as a comparison baseline.

OpenAI does say physicians tested the live Health product before release specifically to assess real-world performance and safety with connected data, which is a step beyond benchmark scoring alone, though the company hasn’t published the methodology or results of that testing in detail.

Health data handling and the permission architecture

Connected medical records and Apple Health data—along with any conversations that draw on them—are excluded from foundation model training and ad targeting, according to the company, regardless of a user’s broader ChatGPT training settings. Conversations that don’t touch Health data still follow whatever training preference a user has set separately.

Access is permission-gated by default. ChatGPT asks before using connected health data to personalise a response, though users can switch to “always allow” and turn off the prompts entirely. That setting lives in Settings > Plugins > Health and can be reversed at any time. Disconnecting a data source triggers deletion from OpenAI’s systems within 30 days, though anything already surfaced in existing chat history sticks around until the user deletes those conversations manually.

Memory creation is scoped narrowly, too. Memories can be created from health conversations but not directly from the raw connected records or Apple Health data itself. Users wanting to avoid memory creation altogether can use Temporary Chat or disable memory in settings.

OpenAI also flags a specific edge case: actions that could expose Health data through other connected plugins, such as sending a training plan built from Apple Health metrics to a running partner. The company says additional checks apply before such actions execute, and that for sensitive cases ChatGPT may ask for explicit confirmation. OpenAI states it runs red teaming exercises targeting these scenarios, though no findings or failure rates from that testing have been made public.

What happens to accuracy at the edges

The practical friction point sits with data quality. A medication can stay listed in a patient’s synced record long after they’ve stopped taking it, and OpenAI uses that exact scenario to illustrate why synced data isn’t automatically current. Its guidance to users is blunt: flag changes to ChatGPT directly and check anything important against the original source rather than trusting the sync to stay accurate on its own.

Wearable and fitness app data carries its own gaps, since availability depends on what each third-party app chooses to share through Apple Health, and OpenAI notes some proprietary scores from fitness apps may not transfer at all.

The relevant question isn’t whether ChatGPT can summarise a lab result correctly in a demonstration, it’s whether the permission model, data deletion timelines, and escalation logic hold up when a user’s synced records are three months stale and the model is asked to reason across contradictory inputs.

OpenAI’s physician testing addresses part of that concern; it doesn’t close the distance between a controlled evaluation and a user managing multiple chronic conditions with incomplete data syncing from four different apps.

For those wishing to give the feature a try, it’s live now for eligible US users through the sidebar Health menu.

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Artificial Intelligence

XDOF, three months out of stealth, is already closing in on a $1.2B Series B

Published

4 weeks ago

September 6, 2026

Social Media Admin

From stealth to unicorn talk in record time

Three months. That’s how long XDOF has been out in the open. And already, the robotics data startup is in late-stage conversations to raise a Series B at a valuation hovering around $1.2 billion, according to multiple sources familiar with the negotiations. The round would be led by 8VC.

Not bad for a company that didn’t even exist publicly until June.

XDOF was co-founded in 2024 by UC Berkeley researchers Philipp Wu (CEO) and Fred Shentu (CTO). Their origin story traces back to a research project called GELLO — a low-cost teleoperation system that lets a human operator control a robotic arm from a distance. The goal? Generate training data for robots. That work produced an influential paper in robotics and, eventually, a company.

The startup’s Series A, a $70 million round announced in June, drew participation from Thrive Capital, Andreessen Horowitz, Lux, and Spark Capital. At the time, XDOF wasn’t planning to raise again so soon. But the market had other ideas.

Why investors are knocking on XDOF’s door

The reason for the sudden interest? Growth. Real, measurable growth.

XDOF’s annualized revenue is approaching $50 million, sources say. That kind of traction, so soon after a Series A, tends to make venture capitalists sit up and take notice. It also tends to make them pick up the phone.

“They weren’t out raising,” one person familiar with the situation told TechCrunch. “The VCs came to them.”

Terms aren’t final, and the total capital being raised remains unclear. TechCrunch couldn’t confirm whether the $1.2 billion valuation includes the new funding or sits on top of it. Both XDOF and 8VC declined to comment.

The Scale AI for physical robots

XDOF’s pitch is straightforward: it builds the data pipelines, collection tools, and annotation systems that frontier AI labs and robotics companies would rather not build themselves. Think of it as an outsourced data-supply chain for the robotics industry.

Investors describe XDOF as the Scale AI or Mercor of physical robotics — a nod to the data-labeling giants that powered the AI boom. The comparison makes sense. Large language models trained on the entire internet. Physical robots? They don’t have that luxury. There’s no massive, ready-made dataset of real-world robot interactions sitting online. That scarcity makes data collection the critical bottleneck on the road to general-purpose machines.

Wu felt that bottleneck firsthand as a PhD student. His research on how robots learn from large datasets kept hitting the same wall: “large-scale data to work with” simply didn’t exist, he told TechCrunch in June.

Building the ABC dataset

XDOF is tackling that problem head-on. The startup is partnering with UC Berkeley’s AI Research lab to release what it believes is the largest collection of high-quality robot training data ever assembled. The dataset is called ABC.

Collecting that data requires a hybrid approach. XDOF combines remote robot teleoperation with human collectors who wear sensors to record everyday tasks. Think folding clothes. Flattening boxes. The mundane, physical chores that robots still struggle to master.

The company plans to hire and train teams of data collectors around the world. Two main roles are emerging:

Teleoperators who steer robots remotely to demonstrate tasks
Egocentric operators who wear body sensors to capture natural movement data

Early traction and the competitive landscape

XDOF has already signed up 20 customers, including several frontier AI labs, according to previous statements to TechCrunch. That customer base, combined with the revenue trajectory, helps explain the valuation chatter.

But XDOF isn’t alone in this niche. Other startups chasing real-world data for robot training include Mecka AI. And the human-data platforms that started with LLMs — like Scale AI and Micro1 — are expanding beyond text and images into physical domains.

The race to build the data infrastructure for physical AI is heating up. Whoever wins it will effectively control the fuel supply for the next generation of robots. That’s a position worth paying up for.

Whether the $1.2 billion valuation holds remains to be seen. Deals at this stage can shift. But the fact that XDOF is even in this conversation — three months after emerging from stealth — says something about the demand for what it’s building.

For more on how data is shaping the future of AI, check out AI data labeling trends and robotics funding rounds in 2024.

Artificial Intelligence

New York City Pulls AI From Younger Classrooms—Here’s Why It Matters

Published

4 weeks ago

September 6, 2026

Social Media Admin

New York City Just Hit Pause on AI in Classrooms

New York City Public Schools is drawing a hard line: no generative AI for students from 2-K through eighth grade during the 2026-2027 school year. That’s over half a million kids walking into classrooms next week without access to AI-powered tools.

The district says it will remove software with student-facing AI features and block AI companion chatbots. High schoolers? They’re exempt. This isn’t a blanket ban—it’s a targeted move to decide when kids should actually start using the technology.

Mayor Zohran Mamdani put it bluntly: “The tech industry wants us to believe that AI-powered early education is not only inevitable, but necessary. We do not see it that way.”

Why NYC Is Worried About AI for Younger Students

The fear isn’t just that a kid will ask ChatGPT to write an essay. City officials want younger students to build core skills—critical thinking, creativity, communication—without leaning on AI as a crutch. They’re also pushing for stronger human connections in classrooms, not another screen.

Schools Chancellor Kamar Samuels said the city refuses to assume that innovation automatically equals more technology in front of students. It’s a deliberate slowdown, and it follows last year’s bell-to-bell cellphone ban that already limits device use during school hours.

What Happens When Kids Reach High School?

AI doesn’t vanish once students hit ninth grade. Instead, the district plans to roll out AI literacy classes twice a year. The goal? Teach teenagers how to think critically about the technology before they become dependent on it.

That’s a different approach from just saying no. It’s about timing—letting younger minds develop without AI, then giving older students the tools to question it.

The National Battle Over AI in Education

NYC’s decision sits at the center of a much bigger fight. The White House has pushed educators to embrace AI responsibly. Some teachers already use it to craft lesson plans, give feedback, or break down tough subjects. But not everyone’s on board.

The Department of Health and Human Services recently gathered childhood experts to talk about excessive screen time. Officials have also called for tougher safeguards around social media and AI. The message? Kids are spending too much time in front of screens, and AI might make it worse.

A Bold Experiment With Zero AI

Here’s what makes NYC’s move so interesting: instead of asking how much AI younger students should use, the largest school district in the country is starting with none. For one academic year, they’re testing whether classrooms are better off with AI kept outside the door.

That’s a radical stance, and it’s not without critics. Some educators argue AI can personalize learning or help struggling students catch up. But NYC is betting that a year without AI will reveal what kids actually need—not what tech companies think they need.

What This Means for Parents and Teachers

If you’re a parent in NYC, expect changes. AI-based apps may disappear from your child’s school day. Teachers will need to plan lessons without generative AI tools. And students in grades 2-K through 8 will rely more on traditional methods—paper, pencils, and human interaction.

For teachers elsewhere, this could be a signal. NYC is the biggest district to take this stance, and its findings could shape policies nationwide. The next year will be watched closely by educators, policymakers, and tech giants alike.

What Happens Next?

The district will spend the year studying how generative AI affects students before deciding what comes next. That research could lead to a permanent ban, a partial rollout, or something entirely different.

For now, NYC is making a statement: childhood shouldn’t be an AI beta test. Whether that’s the right call or a step backward, we’ll know more in 2027. Until then, the debate over AI in schools just got a lot more interesting.

If you’re curious about how AI is shaping other areas, check out our take on AI in education trends or classroom technology policies.

Artificial Intelligence

Meta’s Muse Spark 1.3 takes on GPT-5.6 and Claude — but can it really win?

Published

4 weeks ago

September 5, 2026

Social Media Admin

Meta just fired a serious shot in the AI arms race

The company quietly unleashed Muse Spark 1.3, its most advanced AI model to date, and it’s aiming straight at the top dogs. Developers can already access and pay for the update, which Meta says represents a massive leap forward in performance.

It won’t stay confined to the developer sandbox for long. Over the coming weeks, the model will roll out across Instagram, Facebook, and the Meta AI assistant — putting it in front of billions of everyday users.

What makes Muse Spark 1.3 actually different?

Meta’s Chief AI Officer, Alexandr Wang, didn’t mince words. He called this “the biggest jump so far on model performance,” pointing to serious gains in two areas: coding and agentic tasks — the kind where the AI acts on your behalf rather than just answering questions.

But here’s the catch. Wang told Bloomberg that Muse Spark 1.3 is competitive with Anthropic’s Claude Fable 5.1 and even beats OpenAI’s GPT-5.6 Sol at coding. That’s a bold claim, especially with OpenAI’s upcoming Astra model lurking in the wings.

Take those comparisons with a grain of salt, though. Benchmarks can be gamed, and a model that crushes one test might stumble on a totally different task. Real-world performance is what actually matters.

Efficiency gains under the hood

Wang broke down a few upgrades that set Muse Spark 1.3 apart:

25% fewer tokens needed to complete the same job — meaning lower costs and faster responses
Multi-workflow handling — it can juggle several tasks at once instead of forcing separate sessions
Better context retention across long, complicated instructions
Self-awareness of limits — the model now pauses to ask for clarification before taking any irreversible action

That last point is quietly important. AI that knows when it doesn’t know is a big step toward trustworthiness, especially for agentic use cases.

Pricing stays flat, adoption explodes

Here’s something developers will appreciate: Meta isn’t raising prices. Muse Spark 1.3 costs the same as its predecessor, Muse Spark 1.2. That’s a smart move when rivals are hiking rates.

Wang told Bloomberg that adoption on Meta’s developer platform has been strong — some users are burning through trillions of tokens every week. Those numbers suggest real usage, not just hype.

What about open-source fans? Meta hasn’t decided whether it will release the model’s weights — the blueprint that lets outside developers build on top of it. The older Muse Spark 1.2 weights are still headed for release, but the new model’s future remains unclear.

The bigger picture: Meta’s spending spree continues

Meta is still pouring billions into AI infrastructure, and Muse Spark 1.3 is the clearest signal yet that the company believes it’s closing the gap with OpenAI and Anthropic. Whether that’s true or just corporate bravado will play out in the benchmarks and real-world deployments over the coming months.

For now, the model is available to developers, and the app rollout is imminent. If you’re building on Meta AI tools, this update is worth a serious look. And if you’re just a curious user, you’ll likely meet Muse Spark 1.3 in your Instagram feed sooner than you think.

One thing’s certain: the AI race just got a lot more interesting.