USA Law and Practice Contributed by: Nadia de la Houssaye, Andy Lee, Jason Loring and Graham Ryan, Jones Walker LLP
8. Specific Applications of Healthcare AI 8.1 Clinical Decision Support AI-based clinical decision support systems receive dif - ferent regulatory treatment depending on their specific functionality and the level of interpretation provided to healthcare providers. Other considerations include: • implementation requirements – healthcare organi - sations must establish appropriate governance frameworks for clinical decision support AI deploy - ments; • clinical evidence standards – AI systems must demonstrate clinical validity and utility through appropriate evidence generation, including retro - spective studies, prospective validation and real- world evidence collection; • user interface and workflow integration – effective clinical decision support requires user interfaces and workflow integration that enhance rather than disrupt clinical practice and provide actionable rec - ommendations at appropriate times while minimis - ing alert fatigue and cognitive burden; and • liability and professional standards – healthcare providers retain professional responsibility and accountability for clinical decisions while benefit - AI-based diagnostic tools are regulated as medical devices when they analyse patient data to provide diagnostic information or recommendations. These systems typically require clinical validation demon - strating safety and effectiveness for specific intended uses. ting from algorithmic insights. 8.2 Diagnostic Applications Different medical specialties have developed spe - cific frameworks for AI diagnostic tools that address unique validation requirements and clinical applica - tions. For example, radiology AI systems may require different validation approaches compared to pathol - ogy or cardiology applications. Successful diagnostic AI deployment requires effec - tive integration with existing clinical workflows, imag - ing systems and laboratory processes.
traditional intellectual property concepts may not adequately address ownership questions. • Healthcare provider rights: Healthcare providers using AI systems may claim ownership rights in AI-generated clinical insights that result from their patient data and clinical expertise. These owner - ship claims may conflict with AI developer intellec - tual property rights. • Patient data contributions: Patients whose data contributes to AI training and operation may have interests in AI outputs that incorporate their health information. However, existing legal frameworks provide limited protection for patient interests in AI- generated insights derived from their data. • Contractual ownership arrangements: Contracts between AI developers and healthcare organisa - tions typically must include specific contractual provisions regarding intellectual property. 7.4 Licensing and Commercialisation Two primary licensing and commercialisation models prevail in today’s healthcare AI marketplace: • Commercial licensing models – these include subscription-based software-as-a-service arrange - ments, per-use licensing and comprehensive platform licences; and • Academic-industry collaboration – academic medi - cal centres and commercial AI developers fre - quently collaborate on healthcare AI development through licensing agreements, research partner - ships and joint ventures. Specific considerations must be addressed with respect to: • open source frameworks – open source compo - nents and frameworks may impose specific licens - ing requirements and obligations; and • technology transfer processes – such processes must balance public interest in technology access with commercial development incentives.
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