Cleveland Clinic leaders outline what it takes to scale AI - Report - MDSpire
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Cleveland Clinic leaders outline what it takes to scale AI

Cleveland Clinic executive leaders discuss what health systems need for broader AI adoption. 

  • By

  • Matthew Solan

  • September 16, 2026

  • 6 min

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Clinical Report: What it takes to scale AI in health care

Overview

Scaling AI in healthcare requires careful consideration of clinical workflows, infrastructure, and change management.

Background

The integration of artificial intelligence (AI) into healthcare systems presents both opportunities and challenges. As health systems seek to improve care delivery and operational efficiency, understanding how to effectively scale AI applications becomes critical.

Data Highlights

No numerical data or trial data was provided in the source material.

Key Findings

  • Scaling AI requires understanding existing workflows and improving them through technology.
  • Change management is essential for successful AI adoption in large health systems.
  • Cleveland Clinic evaluates AI projects based on their potential impact on safety, affordability, and scalability.
  • Enterprise readiness involves assessing data, computing infrastructure, and application integration.
  • Cybersecurity is a growing concern as health systems increase reliance on AI technologies.

Clinical Implications

Healthcare organizations must prioritize infrastructure readiness when implementing AI solutions.

Conclusion

Organizations must navigate complexities to realize the potential of AI technologies.

Related Resources & Content

  1. Cleveland Clinic, Source, 2023 -- What it takes to scale AI in health care
  2. Journal of Medical Internet Research (JMIR) — Open-Source Large Language Models and AI Health Equity: A Health Service Triangle Model Perspective
  3. Kaiser Family Foundation (KFF) — AI at Scale: Does It Deliver?
  4. mdspire news — Jame Abraham, MD, FACP, on AI’s role in scaling affordable, accessible care
  5. npj Digital Medicine — Enhancing Governance of Healthcare AI with a Detailed Maturity Model Derived from Systematic Review Findings
  6. FDA Guidance on AI-Enabled Device Software Functions
  7. HTI-1 Final Rule - ONC
  8. Responsible Use of AI in Healthcare | Joint Commission
  9. Department of Health and Human Services: Nondiscrimination in Health Programs and Activities | U.S. GAO
  10. Ethics and governance of artificial intelligence for health: Guidance on large multi-modal models
  11. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action | The National Academies Press
  12. TRIPOD+AI: an updated reporting guideline for clinical prediction models | The BMJ
  13. ACR Approves First Practice Parameter for Imaging Artificial Intelligence
  14. 1 Assurance Standards Guide Coalition for Heal
  15. AMA adopts new policy aimed at ensuring transparency in AI tools | American Medical Association
  16. Computer aided detection and diagnosis of polyps in adult patients undergoing colonoscopy: a living clinical practice guideline
  17. An AI-Based OCT System to Detect Diabetic Macular Edema: A Prospective Validation and Noninferiority Randomized Clinical Trial | Artificial Intelligence | JAMA | JAMA Network
  18. Ambient AI Scribes to Reduce Administrative Burden and Professional Burnout

Original Source(s)

Cleveland Clinic leaders outline what it takes to scale AI

  • by Matthew Solan

  • September 16, 2026

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