OpenAI has expanded its ChatGPT Health feature to all logged-in U.S. users aged 18 and older across its Free, Go, Plus and Pro subscription tiers, broadening its push into consumer healthcare even as the company faces mounting legal and regulatory scrutiny over AI-generated medical advice.

The rollout, available on the web and iOS, comes just one day after a Florida-based pastor filed a lawsuit against OpenAI, alleging that ChatGPT provided dangerous medical guidance that nearly proved fatal after advising against consulting a doctor. The case has renewed concerns about the growing role of generative AI in healthcare and the potential risks of users relying on chatbots for medical decisions.

The move comes as companies race to position their chatbots as personalized health assistants while simultaneously emphasizing that the technology should not replace licensed healthcare professionals. OpenAI first began testing ChatGPT Health through a dedicated health hub in January, allowing users to connect personal health information from services including Apple Health, Function, MyFitnessPal, Epic, Oracle Health, One Medical and Function Health.

Initially, users were required to access the dedicated health hub for medical-related conversations. Under the expanded rollout, health information stored within the feature can now be referenced across general ChatGPT conversations, allowing users to receive personalized responses to broader lifestyle questions, such as whether a meal aligns with dietary restrictions or whether an ingredient could trigger an allergy.

The company said the change was prompted by user behavior during the testing phase.

According to OpenAI, roughly 70% of health-related conversations occurred outside the dedicated health hub, suggesting users naturally seek medical and wellness advice within everyday interactions rather than through specialized interfaces.

Demand for AI-assisted health information has also continued to accelerate.

OpenAI said ChatGPT now handles approximately 300 million health-related queries every week, up from 230 million weekly queries when the feature entered testing earlier this year. The increase highlights the growing willingness of consumers to use AI as a first stop for questions ranging from nutrition and fitness to symptoms and chronic disease management.

OpenAI said users can now draw insights from connected health information across all conversations, enabling more personalized recommendations while keeping the experience within ChatGPT’s standard interface. The company also said its latest AI models have improved significantly in medical reasoning.

According to OpenAI, GPT-5.6 Luna, the smallest model in its newest generation of AI systems, outperformed GPT-5.5 on HealthBench, an open-source benchmark the company developed to evaluate large language models on healthcare-related questions. OpenAI said it collaborates with physicians to improve the quality and safety of health responses and reiterated that users’ personal health information is not used to train its AI models, an important assurance as privacy concerns remain central to healthcare AI adoption.

Even so, the company has continued to distance itself from the role of a medical provider.

Its terms of service state that ChatGPT is “not intended for use in the diagnosis or treatment of any health condition.” The company has cited those disclaimers in responding to the recent lawsuit and has separately told The New York Times that it is continuing to strengthen safeguards around health and medicine-related responses.

As part of the nationwide rollout, OpenAI said it encourages users to verify information independently and make healthcare decisions only after consulting qualified medical professionals.

There is a growing race among major AI developers to establish themselves in digital healthcare. Google has integrated medical capabilities into its Gemini models and Search products, while Anthropic has also introduced health-related AI features aimed at improving access to medical information.

Healthcare represents one of the most commercially attractive opportunities for generative AI. The sector generates vast amounts of structured and unstructured data, faces chronic shortages of medical professionals and increasingly relies on digital tools for patient engagement, making it a natural target for AI assistants capable of synthesizing information and personalizing recommendations.

However, the industry also presents some of AI’s highest risks.

Numerous academic studies have found that large language models can produce inaccurate, misleading, or fabricated medical information, particularly when handling complex diagnoses, rare diseases, or emergency situations. Researchers have also warned that AI systems may present incorrect answers with unwarranted confidence, making it difficult for non-experts to distinguish reliable guidance from potentially dangerous misinformation.

Those concerns have prompted regulators and policymakers to scrutinize how AI companies market health-related features. The lawsuit against OpenAI is likely to add to that pressure by testing the extent to which developers can rely on disclaimers while simultaneously promoting AI tools that increasingly function as personal health assistants.

Artificial intelligence has become one of the dominant forces shaping exchange-traded fund (ETF) investing, with investors increasingly directing capital toward funds tied to AI technologies and the infrastructure supporting their expansion, according to a new report from J.P. Morgan Asset Management.

The firm’s latest Guide to ETFs identifies AI-focused investment products as one of the five largest thematic ETF categories by assets under management, indicating that enthusiasm for artificial intelligence continues to reshape portfolio allocation despite heightened volatility across technology stocks during the second quarter.

The findings suggest investors are looking beyond individual AI companies and instead using ETFs to gain diversified exposure to a rapidly evolving industry that spans semiconductor manufacturers, cloud computing providers, software developers, power infrastructure companies and data center operators.

“Many [themes] are morphing towards AI and the ecosystem surrounding AI,” Jon Maier, J.P. Morgan Asset Management’s chief ETF strategist, said during CNBC’s ETF Edge.

According to Maier, AI investing is increasingly intersecting with other long-term investment themes, particularly infrastructure, reflecting the enormous physical buildout required to support next-generation AI systems.

“It’s all kind of feeding into the AI story … the applications, the energy [and] the AI models,” he said.

Recently, there has been a growing shift in how investors view artificial intelligence. Rather than focusing solely on software developers such as OpenAI or large technology companies, investors are now targeting the broader AI value chain, including companies supplying chips, networking equipment, electricity generation, cooling systems and data center infrastructure.

Analysts see the pattern as a reflection of the capital-intensive nature of AI development. Training and deploying frontier AI models requires massive investments in semiconductors, cloud infrastructure, specialized networking hardware and reliable energy supplies, creating investment opportunities across multiple industries.

That broader investment thesis has helped fuel demand for thematic ETFs that bundle exposure to companies positioned to benefit from AI adoption without requiring investors to select individual winners in an increasingly competitive market.

The report also points to a structural shift in how investors are accessing financial markets. J.P. Morgan found that overall inflows into mutual funds have slowed considerably in recent years while ETFs continue attracting growing amounts of investor capital.

“That’s only going to continue,” Maier said, noting that the firm’s research showed mutual funds have experienced net outflows over the past several years.

The migration reflects a long-running transformation in the asset management industry, where investors have favored ETFs for their lower costs, greater transparency, trading flexibility and tax efficiency. Unlike traditional mutual funds, ETFs generally allow investors to buy and sell shares throughout the trading day like individual stocks. They also tend to generate fewer taxable events because of their creation and redemption mechanism, making them particularly attractive to long-term investors.

Maier said the tax advantages have become an important factor driving ETF adoption among retail investors.

“They typically don’t pay a capital gain [tax],” he said.

By contrast, mutual fund investors can face taxable capital gains distributions even during periods when the value of their investments has declined.

“Imagine if you bought a mutual fund in 2022 and you’re down 20%, 30%, 40%, depending on what part of the market you bought, and you still got a capital gain of 6%. You’re not happy,” Maier said.

The continued shift from mutual funds to ETFs has become one of the defining trends in global asset management, with ETF assets reaching record levels as both institutional and retail investors increasingly use the products for long-term investing, tactical portfolio adjustments and thematic exposure.

AI has emerged as one of the strongest beneficiaries of that transition.

Investor demand for AI-related ETFs has accelerated alongside surging capital spending by major technology companies including Microsoft, Amazon, Alphabet and Meta, which are collectively investing hundreds of billions of dollars in AI infrastructure. Those investments have expanded the universe of publicly traded companies positioned to benefit from the AI boom, ranging from semiconductor manufacturers and cloud providers to utilities, construction firms and industrial equipment suppliers.

The broadening investment opportunity has encouraged ETF providers to launch increasingly specialized AI funds targeting segments such as generative AI, robotics, semiconductor design, data center infrastructure, cybersecurity and AI-enabled software.

At the same time, the report suggests investors are becoming more sophisticated in how they approach AI investing. Rather than concentrating solely on companies developing frontier AI models, many are seeking diversified exposure across the ecosystem that supports AI deployment, including computing hardware, energy generation and enterprise software.

That approach may also help reduce portfolio risk in an industry characterized by rapid technological change and intense competition.