AI for Healthcare Industry Leaders
In this article series, our healthcare, privacy, and FDA lawyers are covering the fundamentals for what providers, physicians, hospitals, and the vendors who support them need to know about how to maximize the impact of AI in their organizations while protecting important patient data and maintaining regulatory compliance.
EXPLORE ALL ARTICLES IN THIS REPORT
AI in Healthcare: Key Legal Questions to Address Before Deployment
Healthcare AI Deployment: Compliance Through Contracting, BAAs, and Data Governance
AI in Healthcare: A Practical Checklist for Compliance and Risk Management
Additional articles in our AI in Healthcare series will be released on a rolling basis—please check back for updates.
EXECUTIVE SUMMARY
As healthcare organizations increasingly embrace artificial intelligence (AI)—moving from common use cases in ambient dictation and revenue cycle management, to wide scale adoption across sectors—it brings increased legal, regulatory, and data governance complexity. As with other infrastructure-like technologies, AI is no longer being evaluated solely on its capabilities, but on how it is implemented, governed, and sustained within complex regulatory environments. This series focuses on the legal and operational considerations required to responsibly deploy AI in healthcare settings and offers practical tips for regulatory and legal compliance.
Initial adoption of AI in healthcare has been driven by efficiency gains, enhanced analytics, and the promise of improved patient outcomes. However, as highlighted in the first articles in this series, questions remain: whether the Health Insurance Portability and Accountability Act (HIPAA) applies, whether protected health information (PHI) is implicated, and whether lawful pathways—such as treatment, payment, or healthcare operations—permit the intended use. These early determinations are not academic; they directly shape system architecture, vendor selection, and permissible data flows.
AI deployment introduces new data governance challenges, requiring clear mapping of how data enters, moves through, and exits AI systems. Contracting emerges as a central compliance mechanism, allocating responsibility, restricting data use, and addressing ownership of inputs and outputs. At the same time, organizations must align their internal practices with external expectations, ensuring transparency in how patient data is used and maintaining trust in an environment where it may not be clear how AI systems operate.
Looking ahead, the remaining articles in this series explore the broader lifecycle of AI in healthcare. These include regulatory considerations such as US Food and Drug Administration (FDA) oversight of clinical decision support and other AI tools, evolving state and federal privacy regimes, and the growing emphasis on algorithmic accountability, bias mitigation, and auditability. Additional focus areas include procurement strategy, vendor diligence, reimbursement and payment implications, cybersecurity risk, and integration with existing health information technology infrastructure.
Across all stages, a consistent theme emerges: AI in healthcare is not a one-time implementation but a continuous governance obligation. Systems must be monitored, validated, and updated as data, use cases, and regulatory expectations evolve. Organizations that approach AI with disciplined planning, rigorous contracting, and proactive compliance practices will be best positioned to capture its benefits while managing its risks.
We encourage using this series as a starting point to integrate AI into your healthcare organization. For specific use case information, reach out to one of our skilled healthcare and privacy lawyers.
