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  3. Clinical Ai In Radiology: Foundations, Trends, Applications, And Emerging Directions.
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  3. Clinical Ai In Radiology: Foundations, Trends, Applications, And Emerging Directions.

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Clinical AI in Radiology: Foundations, Trends, Applications, and Emerging Directions.

Iryna Hartsock1, Nikolas Koutsoubis1,2, Sabeen Ahmed1

  • 1Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA.

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|March 28, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Artificial intelligence (AI) enhances radiology through improved diagnoses and workflows. Key applications include local large language models (LLMs), multimodal AI for early cancer detection, and privacy-preserving federated learning (FL).

Keywords:
local large language modelsmultimodal artificial intelligenceradiology

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) is revolutionizing radiology by enhancing diagnoses, optimizing workflows, and boosting operational efficiency.
  • Despite AI's potential, challenges such as data privacy, clinical usability, and workflow integration persist.

Purpose of the Study:

  • This review provides context on clinical AI in radiology, discussing foundations and current trends.
  • It highlights translational applications and emerging opportunities for AI in oncology radiology.

Main Methods:

  • Review of current AI applications in radiology, including local large language model (LLM) deployment for report streamlining.
  • Description of multimodal AI frameworks for early disease detection (e.g., cachexia in pancreatic cancer).
  • Discussion of privacy-preserving federated learning (FL) and uncertainty-aware de-identification pipelines for secure data handling.

    • Main Results:

    • Local LLM deployment improves radiology report clarity and consistency.
    • Multimodal AI shows potential for early detection of conditions like pancreatic cancer-associated cachexia.
    • Federated learning and secure de-identification pipelines facilitate collaborative AI development and data sharing.

    Conclusions:

    • Successful AI translation in oncology radiology requires local deployment, multimodal reasoning, privacy preservation, and human-in-the-loop oversight.
    • Emerging AI opportunities include tumor board decision support, clinical trial matching, and quality assurance.