ReviewAnnals of nuclear medicine2022
Artificial intelligence for nuclear medicine in oncology.
Review in Annals of nuclear medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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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.
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Who cites it
18 citing papers in PubMed.
- AI-Empowered Nuclear Medicine Education, Part 1: Theoretical and Ethical Foundation.Journal of nuclear medicine technology · 2026Article
- Multicenter evaluation of commercial AI for [Annals of nuclear medicine · 2026Article
- Development and validation of 3D super-resolution convolutional neural network forEJNMMI physics · 2025Article
- Applications of artificial intelligence in interventional oncology: An up-to-date review of the literature.Japanese journal of radiology · 2025Review
- Quantitative evaluation and optimization of AI policy and regulatory texts for smart healthcare.Frontiers in public health · 2025Article
- Image Synthesis in Nuclear Medicine Imaging with Deep Learning: A Review.Sensors (Basel, Switzerland) · 2024Review
- Gastric Emptying Scintigraphy Protocol Optimization Using Machine Learning for the Detection of Delayed Gastric Emptying.Diagnostics (Basel, Switzerland) · 2024Article
- Article
- Applications of artificial intelligence in dementia.Geriatrics & gerontology international · 2024Review
- Clinical application ofJapanese journal of radiology · 2024Review
- The OCDA-Net: a 3D convolutional neural network-based system for classification and staging of ovarian cancer patients using [Annals of nuclear medicine · 2023Article
- From FDG and beyond: the evolving potential of nuclear medicine.Annals of nuclear medicine · 2023Review
- Acquisition time reduction in pediatricJournal of applied clinical medical physics · 2023Article
- J-ACCESS investigation and nuclear cardiology in Japan: implications for heart failure.Annals of nuclear medicine · 2023Review
- Significance of FDG-PET standardized uptake values in predicting thyroid disease.European thyroid journal · 2023Article
- Proposal to Improve the Image Quality of Short-Acquisition Time-Dedicated Breast Positron Emission Tomography Using the Pix2pix Generative Adversarial Network.Diagnostics (Basel, Switzerland) · 2022Article
- Diagnosis of Parkinson syndrome and Lewy-body disease usingAnnals of nuclear medicine · 2022Article
- Medical Radiation Exposure Reduction in PET via Super-Resolution Deep Learning Model.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As in all other medical fields, artificial intelligence (AI) is increasingly being used in nuclear medicine for oncology. There are many articles that discuss AI from the viewpoint of nuclear medicine, but few focus on nuclear medicine from the viewpoint of AI. Nuclear medicine images are characterized by their low spatial resolution and high quantitativeness. It is noted that AI has been used since before the emergence of deep learning. AI can be divided into three categories by its purpose: (1) assisted interpretation, i.e., computer-aided detection (CADe) or computer-aided diagnosis (CADx). (2) Additional insight, i.e., AI provides information beyond the radiologist's eye, such as predicting genes and prognosis from images. It is also related to the field called radiomics/radiogenomics. (3) Augmented image, i.e., image generation tasks. To apply AI to practical use, harmonization between facilities and the possibility of black box explanations need to be resolved.
Indexed as
Identifiers
35028877What OpenQuestion 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 OpenQuestion graph.