Is Your Clinical AI Ready for the Frontlines?

A Leadership Checklist for Safe, Smart, and Scalable AI at the Point of Care

AI Isn't Coming. It's Already Here. 

Clinicians are using generative AI whether it's been sanctioned or not. From ChatGPT to open-source models, shadow AI use is rising inside hospitals. 80% of hospitals already leverage AI for care and operational workflows.¹

What is Shadow AI?

AI tools, like ChatGPT or other apps, used by staff without official approval or oversight. Often invisible to leadership, shadow AI introduces risks around data privacy, accuracy, and compliance.

Do You Know What AI Your Clinicians Are Using?

Shadow AI use is real. Leadership visibility and governance are critical. 

Your answers to these questions are critical...

• Are your clinicians using unapproved AI tools at the point of care?
• Is your organization accountable for AI governance?
• Do your tools align with current and future regulations and quality standards?
• Are you building trust? Or inviting risk?

AI's success depends less on what it can do, and more on how well it's implemented, measured, and trusted. 

Not All AI Is Built for Healthcare

When the source isn't trusted, the risk multiplies.

"The problem with AI responses is they all sound equally confident, but they are not equally reliable" - Katherine Eisenberg, MD, PhD, FAAFP, Sr. Medical Director, Dyna AI, EBSCO Clinical Decisions

What Kind of AI Are We Talking About? 

What Responsible AI Looks Like in Practice (And That Clinicians Actually Want to Use)

Must be built for clinical care from the ground up.

Trustworthy clinical AI combines generative capabilities with curated, evidence-based content. Every answer is sourced, reviewed, and vetted. No outside funding influences the content.

Key Features of Healthcare-Specific AI

• RAG architecture (Retrieval Augmented Generation)
• Built on a state-of-the-art large language model
• Expert clinician-in-the-loop review
• Transparent content sourcing
• ISO-certified security & privacy
• Health equity monitoring and user feedback loops

Key Metrics to Consider

• Usefulness, Usability and Efficacy (Retention)
• Fairness and Equity (Bias Flags)
• Safety and Reliability (Quality/Risk Rate)

Example: Dyna AI CHAI Model Card

• > 0% Dyna AI retention among new users.*
• < 0.1% of responses flagged for bias.
• > 0% of responses rated as "quality answers" by clinical reviewers.

*Percentage of unique Dyna AI web users who retained Dyna AI functionality during the first six months after Dyna AI was released. 

The 5 Must-Haves for Clinical AI

Principles for the Responsible Use of AI

Use This Checklist Before You Deploy AI in Clinical Care Settings

• Quality - Trusted, evidence-based content, developed by clinical experts.
• Security & Patient Privacy - Backed by ISO-certified security & privacy standards.
• Transparency - AI responses clearly labeled and linked to trusted clinical sources.
• Governance - Guided by clinical experts with ongoing oversight and validation.
• Equity - Designed to detect, mitigate, and monitor bias throughout the AI lifecycle. 

Move Your Clinical AI Use Forward with Responsibility and Innovation

Dyna AI combines responsible innovation with point-of-care trust.

See how it works

EBSCO Clinical Decisions | Dyna AI

Healthcare Dive - Custom content for EBSCO by studioID

1. Deloitte 2024 Health Care Outlook