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Artificial intelligence (AI) and machine learning enhance nuclear medicine imaging for oncology, cardiology, and neurology. Future developments promise improved diagnostics and personalized treatments through advanced image analysis and reconstruction.

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Area of Science:

  • Nuclear Medicine
  • Artificial Intelligence
  • Medical Imaging

Background:

  • AI and machine learning are increasingly integrated into nuclear medicine imaging.
  • Applications span automated image analysis, clinical outcome correlation, and image processing.
  • This trend is expected to grow with technological advancements.

Purpose of the Study:

  • To review current trends and future developments of AI in nuclear medicine imaging.
  • To cover applications in oncology, cardiac imaging, and neuroimaging.
  • To highlight technological advancements in AI for nuclear medicine.

Main Methods:

  • Review of current literature on AI applications in nuclear medicine.
  • Categorization of AI methods by application area (oncology, cardiology, neurology) and technology.
  • Focus on both existing and potential future uses of AI.

Main Results:

  • AI facilitates tumor quantification, segmentation, and micro-metastasis detection in oncology PET imaging.
  • AI aids in automated classification and prognosis for benign cardiac diseases and neurodegenerative disorders.
  • Technological AI applications include improved PET attenuation correction, reconstruction, and denoising for ultra low-dose imaging.

Conclusions:

  • AI offers significant potential to improve diagnostic accuracy and treatment planning in nuclear medicine.
  • Further development is needed for widespread clinical adoption of AI tools.
  • Future AI applications may enable early diagnosis, personalized treatments, and broader screening via ultra low-dose PET.