Artificial Intelligence Regulation in the United States: Current Landscape and Implications for Rheumatology

Clinical Snapshot

Key Findings

• Effect Size: Not applicable — no quantitative effect sizes reported; review is descriptive and conceptual

• Primary Outcome: Descriptive synthesis of the current US regulatory landscape for AI in clinical settings, with specific application to rheumatology practice including imaging interpretation, longitudinal disease monitoring, and EHR-based decision support

• Nnt Or Sensitivity: Not applicable — no diagnostic accuracy, therapeutic efficacy, or prognostic data reported; the review proposes a seven-commitment framework (advancing humanity, ensuring equity, engaging impacted individuals, improving workforce well-being, monitoring performance, innovating and learning, promoting sustainability) as operational principles for trustworthy AI deployment

• Confidence Interval: Not applicable — no statistical analyses performed

Clinical Application

The conceptual framework proposed is broadly feasible as an evaluative lens for clinicians assessing AI tools, though no implementation guidance, cost estimates, or workflow integration strategies are provided. Adoption of the seven-commitment framework would require institutional governance structures and dedicated AI oversight resources that may not be available in smaller rheumatology practices. Direct regulatory applicability is limited. Australian rheumatologists operate under the Therapeutic Goods Administration (TGA) Software as a Medical Device (SaMD) framework, the Australian Digital Health Agency's National Digital Health Strategy, and RACGP guidelines on digital health — none of which are discussed in this review. The Australian Government's AI Ethics Framework (2019) and the more recent mandatory guardrails for AI in high-risk settings provide the relevant domestic regulatory context. However, the conceptual principles around AI equity, performance monitoring, and workforce well-being are transferable and align with RACGP's emphasis on patient-centred, safe digital health adoption. Australian rheumatologists considering AI procurement should consult TGA's SaMD guidance and the AIDH's clinical decision support framework rather than relying on US regulatory pathways described here. Rheumatologists, rheumatology nurses, allied health professionals, and health system administrators in settings where AI-enabled clinical tools are being evaluated, procured, or implemented. Most directly relevant to US-based practitioners operating under FDA and federal AI policy frameworks.

Abstract

Artificial intelligence (AI) is increasingly embedded in clinical tools used in rheumatology, including imaging interpretation, longitudinal disease monitoring, and electronic health record-based decision support. AI has moved from the periphery of biomedical research to an operational component of clinical care, increasingly embedded in electronic health records, imaging platforms, and decision support systems. In rheumatology, where care is longitudinal, AI systems offer substantial promise-but also poses distinct risks. Together, these commitments-advancing humanity, ensuring equity, engaging impacted individuals, improving workforce well-being, monitoring performance, innovating and learning, and promoting sustainability-operationalize trustworthy AI systems across a patient's care trajectory.

References

1. 1.Tamang, S. (2026). Artificial intelligence regulation in the United States: Current landscape and implications for rheumatology. Rheumatic Disease Clinics of North America. Advance online publication. https://doi.org/10.1016/j.rdc.2026.03.006