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Positron Emission Tomography (PET) is a medical imaging technique that provides crucial insights into the body's physiological functions at a molecular level. It is an indispensable resource for diagnosing, staging, and monitoring various illnesses, notably cancer, neurological disorders, and cardiovascular conditions.
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Related Experiment Video

Updated: Jan 7, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Artificial intelligence for radiopharmaceutical and molecular imaging.

Jinping Tao1, Ling Liang2, Siqi Hao1,3

  • 1State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Beijing Key Laboratory of Carcinogenesis and Translational Research, NMPA Key Laboratory for Research and Evaluation of Radiopharmaceuticals (National Medical Products Administration), Department of Nuclear Medicine, Peking University Cancer Hospital & Institute, Beijing 100142, China.

Acta Pharmaceutica Sinica. B
|January 1, 2026
PubMed
Summary

Artificial intelligence (AI) is revolutionizing radiopharmaceutical development and molecular imaging for precision nuclear medicine. AI enhances drug discovery, molecular imaging analytics, and clinical translation, accelerating personalized treatments.

Keywords:
Artificial intelligenceClinical translationDeep learningImage reconstructionMolecular imagingMultimodal data fusionPrecision medicineRadiopharmaceuticals

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Area of Science:

  • Nuclear Medicine
  • Radiopharmaceutical Science
  • Artificial Intelligence

Background:

  • Precision nuclear medicine relies on radiopharmaceuticals and molecular imaging.
  • Artificial intelligence (AI) offers transformative potential in these fields.
  • Data-centric AI paradigms are driving innovation.

Purpose of the Study:

  • To review cutting-edge AI applications in radiopharmaceutical discovery and molecular imaging.
  • To investigate AI technical principles and use cases in key areas.
  • To discuss challenges and future directions for AI in clinical translation.

Main Methods:

  • Focus on Deep Learning algorithms like GNNs, GANs, and Transformer Models.
  • Integration of multi-omics data and 3D structural information.
  • Systematic investigation of AI in target identification, ligand design, and image analytics.

Main Results:

  • AI significantly improves radiopharmaceutical target affinity prediction and ligand design.
  • AI enhances molecular imaging (SPECT/PET) through low-dose reconstruction, segmentation, and quantitative analysis.
  • AI improves diagnostic efficiency and accuracy for individualized treatment.

Conclusions:

  • AI is a powerful tool for accelerating radiopharmaceutical development and molecular imaging.
  • Addressing data privacy, generalization, and ethical challenges is crucial for clinical translation.
  • Multidisciplinary integration and technological innovation will advance AI in precision medicine.