USA Law and Practice Contributed by: Nadia de la Houssaye, Andy Lee, Jason Loring and Graham Ryan, Jones Walker LLP
1. Use of Healthcare AI 1.1 Types and Applications of Healthcare AI Healthcare artificial intelligence (AI) encompasses a diverse range of technologies transforming medical practice across the United States. The number and type of approved applications continues to expand with each passing year. Over the past decade, US Food and Drug Administration (FDA) approvals of AI- and machine learning (ML)-enabled medical devices have surged, with nearly 800 such devices authorised for marketing (via 510 (k) clearance, the granting of a de novo request or pre-market approval (PMA)) just during the five-year period ending in September 2024. The spectrum of AI/ML-enabled applications is quite broad and includes the following. • Diagnostic applications: The most prevalent health - care AI applications centre on diagnostics, includ - ing imaging and disease identification. Over the past three decades, radiology devices accounted for about 76% of all AI medical device approvals. All told, these systems assist physicians in analys - ing medical images, including X-rays, magnetic resonance images (MRIs), computed tomography (CT) scans and pathology slides to detect dis - eases. • Clinical decision support systems: AI-powered clinical decision support tools provide real-time recommendations to healthcare providers dur - ing patient encounters. These systems analyse electronic health records, laboratory results and clinical guidelines to suggest treatment protocols, flag potential drug interactions and predict patient risks. The systems integrate seamlessly with exist - ing electronic health record systems to enhance clinical workflows without disrupting established practices. • Therapeutic and treatment planning: AI applica - tions increasingly support personalised treatment planning by analysing patient-specific data to recommend optimal therapeutic approaches. ML algorithms process genetic information, medical history and treatment response patterns to sug - gest individualised medication dosages, surgical approaches and rehabilitation protocols.
• Remote patient monitoring: AI-enabled remote monitoring systems utilise wearable devices, inter - net of medical things (IoMT) sensors and mobile health applications to continuously track patient vital signs, medication adherence and disease progression. These technologies enable early inter - vention for deteriorating conditions and support chronic disease management outside traditional healthcare settings. • Drug discovery and development: Pharmaceutical companies leverage AI to accelerate drug discov - ery processes through target identification, molec - ular modelling and clinical trial optimisation. ML algorithms analyse vast datasets to identify poten - tial drug candidates, predict therapeutic efficacy and optimise trial design to reduce development timelines and costs. • Administrative and operational applications: Healthcare organisations deploy AI for administra - tive functions including revenue cycle manage - ment, claims processing, scheduling optimisation and resource allocation. Natural language process - ing tools automate clinical documentation, coding and billing processes, while predictive analytics optimise staffing and inventory management. Adoption Rates and Implementation In a survey conducted by the American Medical Asso - ciation looking at changes in physician sentiment towards healthcare AI between August 2023 and November 2024, nearly three in five physicians report - ed using AI in their practices. Healthcare AI adoption varies significantly across institutions and specialties, with larger health systems and academic medical cen - tres typically leading implementation efforts. Regula - tory approval pathways, reimbursement policies and technical infrastructure capabilities influence adoption timelines across different healthcare settings. 1.2 Key Benefits and Challenges Healthcare AI delivers significant advantages, includ - ing enhanced diagnostic accuracy, improved clinical efficiency, management of workforce shortages and overall reductions in healthcare costs through opti - mised resource utilisation. AI systems, particularly when integrated with telemedicine platforms, also enhance access to specialised care, particularly in
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