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Implementing artificial intelligence in chest diagnostics for lung disease: A mixed-methods evaluation.

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Summary
This summary is machine-generated.

Artificial intelligence (AI) tools show promise for improving chest diagnostics in the NHS. However, successful implementation requires careful planning, resource allocation, and stakeholder engagement to overcome identified barriers.

Keywords:
ARTIFICIAL INTELLIGENCECHEST DIAGNOSTICSCOST-EFFECTIVENESSEFFECTIVENESSEVALUATIONIMPLEMENTATIONMIXED-METHODSRADIOLOGYRAPID

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Area of Science:

  • Radiology and Medical Imaging
  • Health Informatics
  • Artificial Intelligence in Healthcare

Background:

  • Artificial intelligence (AI) tools are being deployed to enhance diagnostic capabilities in radiology.
  • The UK's National Health Service (NHS) England invested significantly in AI for chest X-ray and CT diagnostics.
  • There is limited understanding of AI implementation, staff experiences, and effectiveness in practice.

Purpose of the Study:

  • To evaluate international evidence on AI tools in radiology.
  • To assess the implementation of AI for chest diagnostics within England.
  • To investigate methods for measuring the effectiveness and cost-effectiveness of AI in chest diagnostics.

Main Methods:

  • A 10-month mixed-methods study combining a rapid scoping review and an empirical investigation.
  • Empirical work involved staff interviews, observations, and documentary analysis across NHS trusts.
  • Analysis employed rapid assessment procedures integrating qualitative, quantitative, and health economic approaches.

Main Results:

  • A review of 114 articles revealed evidence gaps regarding real-world AI implementation and its broader impacts.
  • AI implementation for chest diagnostics varied significantly, with only 24/66 trusts having deployed tools by November 2024.
  • Key barriers included time, resources, and process navigation, while facilitators involved stakeholder engagement; data limitations impacted evaluation capacity.

Conclusions:

  • AI tools can potentially support efficient chest diagnostics, but successful implementation hinges on adequate time, resources, stakeholder engagement, and simplified governance.
  • Learning from past innovations suggests AI may not provide simple solutions as anticipated by policymakers.
  • Addressing implementation, adaptation, sustainability, and impact on care requires further investigation in phase 2.