ReviewAnnals of nuclear medicine2025
Deep learning in nuclear medicine: from imaging to therapy.
Review in Annals of nuclear medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Multicenter evaluation of commercial AI for [Annals of nuclear medicine · 2026Article
- [Advances in Radiomics for Immune Checkpoint Inhibitor-related Pneumonitis of Lung Cancer].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2026Review
- Multimodal data integration in orthopedic regenerative medicine: bridging imaging, omics, and clinical data.Frontiers in cell and developmental biology · 2026Review
- Nuclear Medicine in IRAQ: From a Pioneering Past to Future Progress.Journal of multidisciplinary healthcare · 2026Review
- The Role of Artificial Intelligence in Theranostics.Journal of nuclear medicine technology · 2025Review
- Artificial intelligence for radiopharmaceutical and molecular imaging.Acta pharmaceutica Sinica. B · 2025Review
- MRI grading of lumbar disc herniation based on AFFM-YOLOv8 system.Scientific reports · 2025Article
- Innovations in clinical PET image reconstruction: advances in Bayesian penalized likelihood algorithm and deep learning.Annals of nuclear medicine · 2025Review
- Nanoradiopharmaceuticals: Design Principles, Radiolabeling Strategies, and Biomedicine Applications.Pharmaceutics · 2025Review
- Tc-99m Tetrofosmin SPECT-CT as a Guide to Core Needle Biopsy of a Giant Thymoma.Indian journal of nuclear medicine : IJNM : the official journal of the Society of Nuclear Medicine, IndiaArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
backgroundDeep learning, a leading technology in artificial intelligence (AI), has shown remarkable potential in revolutionizing nuclear medicine.
objectiveThis review presents recent advancements in deep learning applications, particularly in nuclear medicine imaging, lesion detection, and radiopharmaceutical therapy.
resultsLeveraging various neural network architectures, deep learning has significantly enhanced the accuracy of image reconstruction, lesion segmentation, and diagnosis, improving the efficiency of disease detection and treatment planning. The integration of deep learning with functional imaging techniques such as positron emission tomography (PET) and single-photon emission computed tomography (SPECT) enable more precise diagnostics, while facilitating the development of personalized treatment strategies. Despite its promising outlook, there are still some limitations and challenges, particularly in model interpretability, generalization across diverse datasets, multimodal data fusion, and the ethical and legal issues faced in its application.
conclusionAs technological advancements continue, deep learning is poised to drive substantial changes in nuclear medicine, particularly in the areas of precision healthcare, real-time treatment monitoring, and clinical decision-making. Future research will likely focus on overcoming these challenges and further enhancing model transparency, thus improving clinical applicability.
Indexed as
Identifiers
40080372What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.