USA Trends and Developments Contributed by: Nadia de la Houssaye, Andy Lee, Jason Loring and Graham Ryan, Jones Walker LLP
must be paid to protecting vulnerable populations, including paediatric patients, elderly individuals, racial and ethnic minorities, and individuals with disabilities. Professional Liability and Standards of Care The integration of AI into clinical practice has created novel questions about professional liability and stand - ards of care that existing legal frameworks struggle to address. Traditional medical malpractice analysis relies on established standards of care, but the rapid evolution of AI capabilities makes it difficult to deter - mine what constitutes appropriate use of algorithmic recommendations in clinical decision-making. Healthcare AI liability generally operates within estab - lished medical malpractice frameworks that require the establishment of four key elements: duty of care, breach of that duty, causation and damages. When AI systems are involved in patient care, determining these elements becomes more complex. While a phy - sician must exercise the skill and knowledge normally possessed by other physicians, AI integration creates uncertainty about what constitutes reasonable care. The Federation of State Medical Boards’ April 2024 recommendations to hold clinicians liable for AI tech - nology medical errors represent an attempt to clarify professional responsibilities in an era of algorithm- assisted care. However, these recommendations raise complex questions about causation, particularly when multiple factors contribute to patient outcomes and when AI systems provide recommendations that healthcare providers may accept, modify or reject based on their clinical judgment. When algorithms influence or drive medical decisions, determining responsibility for adverse outcomes presents novel legal challenges not fully addressed in existing liability frameworks. Courts must evalu - ate whether AI system recommendations served as a proximate cause of patient harm, as well as the impacts of the healthcare provider’s independent medical judgment and other contributing factors. Documentation requirements have become increas - ingly important, as healthcare providers must main - tain detailed records of AI system use, including the specific recommendations provided, clinical reason -
ing for accepting or rejecting algorithmic guidance, and any modifications made to AI-generated sugges - tions. These documentation practices are essential for defending against potential malpractice claims while ensuring that healthcare providers can demon - strate appropriate clinical judgment and professional accountability. AI-related malpractice cases may require expert witnesses with specialised knowledge of medical practice and existing AI technology capabilities and limitations. Such experts should have the experience necessary to evaluate whether healthcare provid - ers used AI systems in an appropriate manner and whether algorithmic recommendations met relevant standards. Plaintiffs in AI-related malpractice cases face challenges proving that AI system errors directly caused patient harm, particularly when healthcare providers retained decision-making authority. Market Dynamics and Investment Trends Despite regulatory uncertainties, venture capital investment in healthcare AI remains robust, with bil - lions of dollars allocated to start-ups and established companies developing innovative solutions. However, investment patterns have become more selective, focusing on solutions that demonstrate clear clinical value and regulatory compliance rather than pursuing speculative technologies without proven benefits. The American Hospital Association’s early 2025 sur - vey of digital health industry leaders revealed cautious optimism, with 81% expressing positive or cautiously optimistic outlooks for investment prospects and 79% indicating plans to pursue new investment capital over the next 12 months. This suggests continued confidence in the long-term potential of healthcare AI despite near-term regulatory and economic uncer - tainties. Clinical workflow optimisation solutions, value-based care enablement platforms and revenue cycle man - agement technologies have attracted significant funding, reflecting healthcare organisations’ focus on addressing immediate operational challenges while building foundations for more advanced AI applica - tions. The increasing integration of AI into these core healthcare functions demonstrates the technology’s
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