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A RRA Perspective on AI and Machine Learning Applications in Radiology: From Experimental to Clinically Viable

Joshua Brown1, Brittany Z Dashevsky2, Dogan Polat3

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Summary
This summary is machine-generated.

Artificial intelligence (AI) and machine learning enhance radiology diagnostics, workflow, and reporting. Optimal outcomes are achieved through radiologist-AI collaboration, integrating AI as a tool to augment human expertise.

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Emerging technologies are transforming radiology.
  • Artificial intelligence (AI) and machine learning (ML) show significant potential.
  • This is the first review in a seven-part series on emerging technologies in radiology.

Purpose of the Study:

  • To examine AI and ML applications in diagnostic interpretation, workflow optimization, and report generation.
  • To review the current state and challenges of AI in radiology.
  • To highlight the transition of AI from experimental to clinical viability.

Main Methods:

  • Review of advancements in deep learning, multimodal large language models, and natural language processing.
  • Analysis of AI's impact on accuracy, efficiency, and reporting quality.
  • Evaluation of challenges such as performance variability, generalizability, and workflow integration.

Main Results:

  • AI and ML have improved accuracy, efficiency, and reporting quality in radiology.
  • Key challenges include variable performance, limited generalizability, and integration barriers.
  • Radiologist-AI collaboration yields the strongest clinical outcomes.

Conclusions:

  • AI is transitioning into a clinically viable technology for enhancing radiology practice.
  • Thoughtful implementation with appropriate oversight is crucial for successful AI integration.
  • AI is most effective when augmenting, not replacing, human expertise in radiology.