Objective:

To describe challenges in translating healthcare AI from promising technical performance into safe, useful, and sustainable clinical practice.

Approach:

• Implementation: The article synthesizes examples and reports on AI deployment across clinical settings, health systems, and countries.
• Evaluation: It discusses the need to assess AI before, during, and after deployment, including effects on clinical outcomes, workflows, costs, and health-service performance.

Key Findings:

• Retrospective metrics and reader studies do not establish clinical value unless AI is integrated into care workflows.
• Implementation requirements vary with local resources, infrastructure, care models, and workforce capacity; the same system may require different strategies across settings.
• Reported concerns include inaccurate or misleading medical advice, safety risks, performance drift, and declining clinician engagement.
• The article describes a survey in which more than 90% of US health systems had begun deploying AI tools, while no more than 53% considered them highly useful.
• Prospective real-world studies and ongoing oversight are needed to assess effects and monitor performance and emerging risks.

Interpretation:

The article argues that healthcare AI’s value depends on its contribution to care, not technical performance alone. Successful implementation also requires workflow integration, local adaptation, safety evaluation, staff and patient trust, and continued governance.

Limitations:

• The context provides no systematic review methods or pooled estimates.
• The article summarizes selected examples and reports; it does not establish the effectiveness of AI tools across healthcare settings.