Cleveland Clinic leaders outline what it takes to scale AI - Scorecard - 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 Scorecard: What it takes to scale AI in health care

At a Glance

CategoryDetail
ConditionArtificial Intelligence in Health Care
Key MechanismsIntegration of AI into clinical and operational workflows, infrastructure readiness, and change management.
Target PopulationHealth systems and clinical caregivers
Care SettingHealth care organizations

Key Highlights

  • Scaling AI requires understanding existing workflows and improving them.
  • Health systems must evaluate AI applications based on strategic objectives and potential impact.
  • Enterprise readiness involves data, computing infrastructure, and applications.
  • Cybersecurity is critical as health systems become more reliant on technology.
  • Realistic expectations about AI's capabilities are essential for successful implementation.

Guideline-Based Recommendations

Diagnosis

    Management

    • Identify and prioritize AI projects based on quality improvement and patient experience.

    Monitoring & Follow-up

      Risks

      • Cybersecurity threats must be addressed as AI integration increases.

      Patient & Prescribing Data

      Patients receiving care in health systems utilizing AI.

      AI should enhance caregiver workflows rather than complicate them.

      Clinical Best Practices

      • Adopt an 'AI-first' approach to reimagine health care delivery.
      • Ensure data is AI-ready to enable effective implementation.
      • Prepare health systems for the challenges of AI integration.

      Related Resources & Content

        Original Source(s)

        Cleveland Clinic leaders outline what it takes to scale AI

        • by Matthew Solan

        • September 16, 2026

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