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Economic Value of AI in Radiology: A Systematic Review.

Isabel Molwitz1, Inka Ristow1, Jennifer Erley1

  • 1Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246 Hamburg, Germany.

Radiology. Artificial Intelligence
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Summary
This summary is machine-generated.

Artificial intelligence (AI) shows economic value in radiology, reducing costs in resource-intensive tasks and increasing revenue. Its effectiveness varies with task complexity and implementation, necessitating further economic evaluations.

Keywords:
Artificial IntelligenceCost-effectivenessEfficacy StudiesHealthcare EconomicsRadiologySystematic Review

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Area of Science:

  • Radiology and Healthcare Economics
  • Artificial Intelligence in Medical Imaging

Background:

  • Artificial intelligence (AI) is increasingly integrated into various aspects of the radiologic workflow.
  • Understanding the economic implications of AI adoption is crucial for healthcare systems.
  • Previous evidence on AI's economic value in radiology is fragmented.

Purpose of the Study:

  • To systematically summarize the existing evidence on the economic value of artificial intelligence (AI) across the entire radiologic workflow.
  • To identify factors influencing AI's cost-effectiveness and revenue generation in radiology.

Main Methods:

  • A comprehensive literature search was performed across PubMed, Business Source Ultimate, and EconLit from January 2010 to November 2024.
  • Studies were selected based on explicit quantification of economic outcomes, using keywords related to AI and radiology economic value.
  • Study quality was assessed using the Criteria for Health Economic Quality Evaluation.

Main Results:

  • Twenty-one studies met the inclusion criteria, primarily evaluating machine learning (48%) and computer-assisted diagnosis (33%) tools.
  • AI demonstrated economic value through cost savings and improved cost-effectiveness in resource-intensive tasks when accuracy matched human performance.
  • AI increased costs when specificity was low or with pay-per-use models, but offered value in settings with radiologist shortages and improved compliance.

Conclusions:

  • The economic value of AI in radiology is highly context-dependent, influenced by task complexity, examination volume, and the chosen implementation model.
  • AI can reduce costs via protocol optimization and increase revenue through enhanced follow-up compliance.
  • High-quality economic evaluations are essential to guide the optimal implementation of AI in radiology.