OpenAI, Anthropic See Health Care as Next Big Market for AI

Healthcare is becoming a major market for AI, driven by rapid adoption of AI tools in clinical and administrative workflows. OpenAI and Anthropic are at the forefront of this shift, with new features like ChatGPT Health enabling personalized medical advice while emphasizing non-diagnostic roles. The sector's growth has been fueled by rising administrative overhead and staffing shortages, creating strong incentives to integrate AI into daily operations.

According to recent reports, healthcare organizations are prioritizing AI for automation in documentation, knowledge retrieval, and workflow coordination. These tools are increasingly embedded in systems rather than being used for surface-level tasks like chat interfaces. This shift reflects a broader trend where AI is no longer just an experimental tool but a foundational element of healthcare operations.

Healthcare AI adoption is outpacing other sectors. The OpenAI report shows that healthcare has seen an 8× increase in AI adoption year-over-year, trailing only the technology sector. This growth is driven not by access to tools but by the depth of their integration into operations.

Why Is AI Adoption Accelerating in Healthcare?

The healthcare industry is under pressure to reduce costs and improve efficiency. Chronic staffing shortages and rising administrative demands are pushing organizations to adopt AI to automate routine tasks. Unlike other industries, healthcare AI is being used to solve real-world problems like documentation and workflow optimization, where results are measurable and impactful.

Healthcare workers are also increasingly relying on AI to perform tasks that traditionally required technical skills. Non-technical staff are now able to use generative AI to code, analyze data, and manage internal knowledge bases. This skill diffusion is expanding the scope of AI's influence across the sector.

What Risks and Challenges Remain for Healthcare AI?

Despite rapid adoption, healthcare AI faces significant risks. These include data privacy concerns, regulatory hurdles, and the potential for AI to make errors in high-stakes scenarios. OpenAI has taken steps to address these concerns by separating health data from regular ChatGPT conversations and ensuring data is not used for training its models.

However, AI systems still struggle with domain-specific accuracy and the risk of hallucination. For example, a poorly designed AI tool could provide misleading advice about medical symptoms or treatment options, potentially endangering patients. These risks highlight the importance of robust governance frameworks and secure data practices in healthcare AI.

How Are CFOs and Executives Managing AI Governance Risks?

Healthcare CFOs are caught between the need to adopt AI for cost savings and the risks associated with data privacy and compliance. A recent survey found that 53% of organizations cannot remove personal data from AI models once it has been used, creating long-term exposure under regulations like GDPR and CPRA.

Moreover, 63% of organizations cannot enforce purpose limitations on AI agents, 60% lack kill-switch capabilities, and 72% have no software bill of materials (SBOM) for AI models in their environment. This means that AI systems are accessing and processing sensitive data without clear oversight or accountability.

The challenge for healthcare leaders is to balance innovation with governance. While AI can help reduce clinician burnout and improve patient outcomes, it also introduces new compliance and security costs. CFOs must ensure that AI investments align with organizational goals while minimizing risk exposure.

Healthcare AI is at a pivotal moment. The sector is moving from experimentation to operational integration, but success will depend on how well organizations manage the risks and regulatory challenges. For now, the focus remains on embedding AI into workflows and ensuring that it delivers measurable value.