Artificial intelligence (AI) is rapidly changing and shaping how healthcare is delivered in the US and internationally. The World Economic Forum predicts that by 2030, AI will be able to access multiple data sources to identify disease patterns and support treatment and care. Healthcare systems will use AI to predict an individual’s risk of certain diseases and recommend preventative measures, while AI will also help reduce patient waiting times and improve overall efficiency in hospitals and health systems. In addition, AI technology will drastically accelerate new drug discovery and reduce the new drug-to-market timeline significantly, from years to months.

However, AI advancement brings a new set of challenges to the industry, including:

• Data quality and accessibility: one of the biggest stumbling blocks to real-world AI implementation has been a deceptively simple challenge: access to high-quality data. The compound annual growth rate (CAGR) of healthcare data was projected to reach 36% in 2025. This massive increase in healthcare data poses great opportunities. At the same time, it introduces significant challenges for healthtech companies adopting AI technology
• Algorithm bias: examples of bias include skin cancer detection algorithms performing poorly on darker skin tones due to training data primarily featuring lighter skin, chest X-ray analysis systems showing gender bias due to imbalanced training data, and algorithms used for patient risk assessment prioritising white patients over black patients when trained on healthcare cost data, which reflects historical racial disparities in access to care
• Patient privacy and data security: a 2019 study showed that AI could reidentify 99.98% of individuals in anonymised data sets using only 15 demographic attributes. This puts patient privacy at risk, even when data is anonymised
• Patient acceptance: beyond the executive level, AI implementation in healthcare affects patients and healthcare professionals in terms of human acceptance and trust issues. A 2023 study found that clinical staff may struggle to accept AI due to the need to learn new skills and take on more complex tasks
• Support of clinical staff: AI anxiety is a common term that describes some of the clinical staff’s attitudes towards AI. According to a survey, nearly half (44%) of healthcare workers in the US fear that AI could take their jobs.

These AI challenges are similar across different country boundaries. The international healthcare industry needs a global common blueprint for the safe and responsible adoption of AI technology in healthcare. The responsible use of AI in healthcare is the practice of designing and using AI systems in a way that is ethical, safe, and fair. It involves balancing patient needs with the needs of healthcare organisations.

Principles of responsible AI:

• Fairness: AI systems should be designed to be fair and unbiased
• Privacy: AI systems should protect patient data and comply with
  privacy laws
• Accountability: AI systems should be held accountable and have clear expectations for performance
• Transparency: AI systems should be transparent and explainable
• Patient-centred: AI systems should prioritise the needs of patients.

The US’s approach to governing AI began with the release of the Blueprint for an AI Bill of Rights in October 2022. As of President Trump’s second term, the US still does not have a comprehensive federal AI law. Instead, policy is driven by executive actions and a national framework that emphasises pro-innovation, limited regulation, and federal pre-emption of state rules. The administration has rolled back prior safety-focused policies, encouraged industry-led standards, and pushed Congress toward a light-touch legislative approach – but no binding, unified AI statute has been passed yet, leaving a fragmented landscape with growing state-level activity.

In addition to federal policymaking, there has been progress made by private constituencies in the US about the safe and responsible adoption of AI. The Coalition for Health AI (CHAI), a nonprofit organisation, was created in 2024 as a diverse and interdisciplinary group to build a home for people seeking to harness these new capabilities to improve lives.
AI in healthcare is still in its early stage, so collaboration among multiple stakeholders is essential to defining the values, purpose, and practices necessary to ensure that these technologies help, not harm. Its main objectives include:

• Building trust: ensuring that AI in healthcare is reliable and widely accepted and understood by patients, clinicians, and organisations
• Broad representation: engaging diverse stakeholders, including underserved communities, to ensure AI tools are developed and tested across various environments and data sets
• Public–private collaboration: working with both the public and private sectors, leveraging the strengths of each to develop effective AI standards and tools
• Transparency: promoting transparency in AI development and implementation, addressing concerns about the ‘black box’ nature of AI decision-making
• Supporting innovation: balancing the need for regulation with the need to foster innovation by developing consensus-driven standards to ultimately increase adoption of safe and effective AI.

Another exciting development is from the healthcare accreditation front.
The Joint Commission and Joint Commission International, the largest healthcare-accrediting body in the world, launched a new certification called Responsible Use of Health Data in 2024, mandating the following standards:

• Oversight structure: establish a governance structure for the use of de-identified data
• Data de-identification: comply in accordance with the Health Insurance Portability and Accountability Act (HIPAA)
• Data controls: establish data controls to protect against unauthorised reidentification of data
• Limitations on use: prohibit the misuse of data
• Algorithm validation: have processes to manage internally developed algorithms
• Patient transparency: communicate with key stakeholders about the secondary use of de-identified data.

Equally impressive is the legislation development from other countries. The AI Act is a European Union (EU) regulation on AI – the first comprehensive regulation on AI by a major regulator anywhere. The Act assigns applications of AI to separate risk categories. Applications and systems that create an unacceptable risk, such as government-run social scoring of the type used in China, are banned. High-risk applications, such as a CV-scanning tool that ranks job applicants, are subject to specific legal requirements.

In the Middle East, AI regulation is still in its infancy stage. Middle Eastern nations are making distinct choices in AI regulation to support innovation while addressing their unique priorities around security and privacy. The United Arab Emirates (UAE) and Saudi Arabia, for instance, emphasise data localisation, requiring that data generated within their borders be stored and processed locally. This strategy supports their drive for digital sovereignty and control over infrastructure, reinforcing their ambitions to establish strong AI ecosystems.

With the rapid evolution of AI technology, the regulatory frameworks for AI in the Asia-Pacific (APAC) region continue to develop quickly. Several jurisdictions in the region are moving toward AI-specific regulations, including China and South Korea. In January of 2026, Singapore launched the Model AI Governance Framework for Agentic AI at the World Economic Forum. It provides guidance for using AI systems that can plan, reason, and act autonomously, emphasising human accountability, risk mitigation, and organisational controls. For another example, Taiwan’s legislature passed the Artificial Intelligence Basic Act on 23 December 2025, and it came into force in January 2026. This is Taiwan’s first national AI law, establishing a national legal foundation for AI governance.

Key takeaways

Although the recent technical breakthroughs in AI technology have been breathtaking, its safe and responsible adoption in healthcare will take much longer. Industry stakeholders in the global healthcare communities, along with the local and regional governments, need to embrace a common blueprint and framework that is both actionable and enforceable. We will see both collaboration and competition across nations’ borders for many years to come.