USA Law and Practice Contributed by: Nadia de la Houssaye, Andy Lee, Jason Loring and Graham Ryan, Jones Walker LLP
1.3 Market Trends A number of market forces are affecting the develop - ment and deployment of healthcare AI solutions in the United States, including the following. • Technology company leadership: Major technol - ogy corporations are driving significant innova - tion in healthcare AI through substantial research and development investments. Companies such as Google Health, Microsoft Healthcare, Amazon Web Services and IBM Watson Health continue to develop foundational AI platforms and tools. • Healthcare provider initiatives: Large health sys - tems and academic medical centres lead health - care AI adoption through dedicated innovation centres, research partnerships and pilot pro - grammes. These organisations often serve as test - ing grounds for emerging AI technologies, internal expertise and governance frameworks that smaller healthcare providers can subsequently adopt. • Pharmaceutical industry investment: Pharmaceuti - cal companies increasingly integrate AI throughout drug development pipelines, from target iden - tification and molecular design to clinical trial optimisation and regulatory submissions. These investments aim to reduce development costs and timelines while improving success rates for new therapeutic approvals. • Notable industry collaborations: Strategic partner - ships between technology companies, academic medical centres and healthcare organisations facilitate AI development and deployment through shared expertise and resources while maintaining access to diverse patient populations for training and validation studies. The Coalition for Health AI (CHAI) represents a significant industry col - laboration focused on developing best practices and standards for healthcare AI implementation. In June 2025, The Joint Commission announced a new partnership with CHAI to help accelerate the development and adoption of best practices in order “to elevate patient safety and quality, and ultimately improve health outcomes for all”. • Investment and funding trends: Venture capital investment in healthcare AI continues, with billions of dollars allocated to start-ups and established companies developing innovative AI solutions. However, uncertainties regarding government fund -
underserved areas where specialist expertise may be limited. However, there are a number of issues that are, at least temporarily, causing some pushback to the adoption of healthcare AI solutions. • Healthcare-specific challenges: While healthcare’s expanding use of AI provides benefits, it also complicates the industry’s ability to protect patient information and remain compliant with the data privacy, security and other protections required by the Health Insurance Portability and Accountabil - ity Act (HIPAA). AI systems require access to vast amounts of sensitive patient information that must be protected against unauthorised disclosure and cyber-attacks. • Algorithmic bias and health equity concerns: Lack of equitable access to diagnosis and treat - ment may be improved through new digital health technologies, especially AI/ML, but these may also exacerbate disparities, depending on how bias is addressed. Training data quality and representa - tiveness significantly impact AI system perfor - mance across diverse patient populations. In a 2024 review of 692 AI/ML-enabled, FDA-approved medical devices, researchers found that only 3.6% of approvals reported race/ethnicity, 99.1% pro - vided no socioeconomic data and 81.6% did not report the age of study subjects. • Clinical integration and workflow disruption: Healthcare organisations face substantial chal - lenges integrating AI tools into existing clinical workflows and electronic health record systems. Technical interoperability issues, user training requirements, and change management processes require significant investment and co-ordination across multiple departments and stakeholders. • Regulatory uncertainty and liability concerns: In the United States in 2025, AI is becoming more integrated into healthcare at the same time that funding for federal and state support for medical research is becoming less certain. Likewise, the evolving regulatory landscape means that a num - ber of concerns remain unresolved for healthcare providers and AI developers regarding compliance requirements, liability allocation and professional standards.
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