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OpenAI’s ChatGPT Health Is A Private Space For Health Data

A new health mode lets the popular AI platform tap medical records and fitness apps while walling off sensitive information.

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openai's chatgpt health is a private space for health data
OpenAI

OpenAI has created ChatGPT Health, a separate space inside its chatbot platform for handling medical and wellness data. The opt-in feature starts with a small US cohort before widening out.

Health-related questions have long driven traffic to AI tools. OpenAI says over 230 million people ask ChatGPT about health or insurance each week. The new mode adds personal context to that behavior but stops short of diagnosis or treatment advice.

Users can connect records from participating US providers through b.well and link apps such as Apple Health, MyFitnessPal, Function and Weight Watchers. Some links are US-only, while Apple Health needs iOS. Once connected, ChatGPT can surface patterns in labs, summarize information ahead of a clinic visit or help map diet and exercise choices against past data.

The data sits apart from other chat information. Health has its own memories and does not spill into other conversations. Users can view or delete health memories at any time. OpenAI says this material is not used to train its models.

Security is much heavier in this section too. Health adds isolation and purpose-built encryption on top of the platform’s baseline protections. App connections require explicit permission, and disconnecting cuts the feed immediately.

“ChatGPT Health is another step toward turning ChatGPT into a personal super-assistant that can support you with information and tools to achieve your goals across any part of your life,” wrote Fidji Simo, OpenAI’s applications chief.

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Physicians had input during development, though OpenAI has not detailed how that shaped the end product. The launch follows Health Bench, a dataset released in May to test models on realistic medical cases.

While currently rooted in the US healthcare ecosystem, the approach may draw interest in the Gulf and wider MENA markets as governments push digital health records and patient portals under modernization programs. Adoption will depend on whether users trust an AI assistant with such personal material and whether it fits clinical routines.

For OpenAI, the move marks a cautious step into regulated terrain and signals a shift toward sector-specific uses of generative AI.

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ESET Bets On Specialized Security AI At 0.5% The Training Cost

ESET says NeoCortex can estimate an attacker’s next move, with a kill switch and human oversight built in to keep it from going rogue.

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eset bets on specialized security ai at 0.5% the training cost

A sign-in anomaly, a suspicious process launch and an unusual connection may look harmless alone. Together, they can describe an intrusion that is hard for security to teams to piece together.

To solve that problem, ESET announced ESET NeoCortex on November 4, 2026: a specialized foundational AI model for cybersecurity. It powers ESET LiveCortex, available within the ESET PROTECT Platform, which connects fragmented security signals and turns them into actionable incidents.

Filtering is the main selling point, with LiveCortex processing billions of raw telemetry events daily, and just 0.00111% of them contributing to incidents presented to customers. The aim is less alert fatigue and more analyst time spent on threats that matter.

NeoCortex is built on world-model architecture, learning how digital environments behave and how events relate. ESET says that lets it reconstruct missing incident context, recognize similar past cases and estimate an attacker’s likely next step – what the company calls “minus-one-day defense,” a chance to intervene before the next stage unfolds. ESET points to Black October, the week leading up to October 25, 2026, when critical security issues affecting PHP, Linux, GitLab and other key systems demanded rapid, prioritized responses, as the kind of moment where that context matters.

ESET’s internal research also suggests a specialized model like NeoCortex can be trained at up to 200 times lower cost than a large general reasoning model, while delivering more accurate, defense-focused results. The company credits optimized training, a focus on cybersecurity vocabulary, lower computing requirements and privately owned hardware.

Also Read: Dubai Says Its Deepfake Detector Needs Just 7 Seconds Of Video

“AI is accelerating both cyberattacks and cyberdefenses. With ESET NeoCortex, we are applying our AI investment to develop specialized foundational cybersecurity intelligence that remains under our control and strengthens, rather than replaces, human expertise,” said Juraj Malcho, Chief Technology Officer at ESET.

As part of the design, NeoCortex forms part of a layered architecture combining specialized models, deterministic controls, correlation logic, validation, auditability and expert human oversight. Safeguards include restricted sandboxes, read-only tools, continuous monitoring, defined usage and token limits, a kill switch and human oversight of critical response decisions. The model is also developed and self-hosted on ESET’s own infrastructure, which the company says reduces exposure to shifts among third-party AI providers, including commercial and geopolitical ones.

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