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Radiomics in clinical radiology: advances, challenges, and future directions.

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Radiomics, extracting quantitative image features, is advancing with artificial intelligence (AI), including large language models (LLMs). These AI-driven radiomic solutions promise enhanced diagnostic and prognostic capabilities for precision medicine.

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Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Radiology
  • Quantitative Imaging Biomarkers

Background:

  • Radiomics extracts quantitative features from medical images, offering potential for improved clinical radiology.
  • The integration of artificial intelligence (AI), particularly large language models (LLMs) and agentic AI, is transforming radiomic analysis.

Purpose of the Study:

  • To review the convergence of radiomics with AI, focusing on LLMs and agentic AI.
  • To summarize developments, highlight challenges, and suggest future directions for radiomics adoption.
  • To assess the impact of AI on radiomic methods, standardization, and clinical integration.

Main Methods:

  • Extensive literature review of radiomics research.
  • Focus on validation frameworks, standardization, deep learning, LLMs, and multi-center studies.
  • Comparison of key publications on diagnostic accuracy, prognostic performance, and reproducibility.

Main Results:

  • AI, especially LLMs and agentic AI, offers significant opportunities to enhance radiomic analysis and clinical applicability.
  • Advances in standardization and validation are addressing reproducibility challenges.
  • Radiomics shows potential for improved diagnostic accuracy, prognostic performance, and workflow integration.

Conclusions:

  • Radiomics, enhanced by AI, is poised to become crucial for precision imaging.
  • Overcoming challenges in reproducibility, interpretability, and clinical integration is essential for widespread adoption.
  • Radiologists and scientists should prepare for AI-powered radiomic tools supporting clinical decision-making.