AI in Pharma

The Problem Being Addressed

While AI innovation in healthcare has accelerated rapidly, clear expectations for safe and responsible patient-facing AI have lagged behind. According to Raj Ratwani, PhD, Vice President of Scientific Affairs at MedStar Health Research Institute, "AI is outpacing governance and oversight," particularly in one of healthcare's highest-impact domains: direct communication with patients.

What the Standard Includes

The AI Care Standard translates high-level principles into practical, healthcare-ready requirements centered on accuracy, clarity, transparency, and accountable governance. The framework consists of 10 Core Pillars developed by the PatientAI Collaborative™, establishing operational requirements that address safety, equity, governance, and real-world usability for patients, families, clinicians, and care teams.

According to M. Bridget Duffy, M.D., co-chair of the initiative and the nation's first Chief Experience Officer at Cleveland Clinic, the standard "establishes clear guardrails for how AI should be evaluated and used in patient communication, so innovation can advance without compromising patient safety, trust, or adding to clinician burden."

Implementation Support

To facilitate adoption, the Collaborative introduced the AI Care Standard Evaluation Framework™, a questions-driven audit tool that helps organizations assess whether their patient-facing AI systems meet the standard. The framework guides a structured review of how AI-generated communication is designed, governed, monitored, and experienced by patients in real care settings.

The standard is designed for rapid adoption by health systems, digital health companies, and care delivery organizations.

Read more here.