ReviewJournal of nuclear medicine technology2025
The Role of Artificial Intelligence in Theranostics.
Review in Journal of nuclear medicine technology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Nanoparticulate and Hydrogel Vehicles for Stimuli-Responsive and Sustained Controlled Release of Active Pharmaceutical Ingredients.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Theranostics in Radiation Medicine: Integrating Radiopharmaceutical Therapy and External-Beam Radiotherapy.Journal of clinical medicine · 2026Review
- Theranostic Innovative Strategies for Brain Diseases: New Insights on Neurovascular Unit-Associated Pathological Changes in Neurodegenerative Disorders and Aging.International journal of molecular sciences · 2026Review
- Polymer Nanoparticles in Medical Applications-Future Directions.Nanomaterials (Basel, Switzerland) · 2026Review
- Artificial intelligence across oncologic theranostics: evidence for patient stratification, dosimetry, and adaptive radiopharmaceutical therapy.Frontiers in nuclear medicine · 2026Review
- Challenges and Opportunities in Radioligand Therapy.Journal of nuclear medicine technology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The recent reinvigoration of theranostics comes with advances in computing technology, radiochemistry, and instrumentation that synergize with developments in artificial intelligence (AI). There is a wide array of applications of AI in nuclear medicine that have translational benefits to theranostics, including attenuation correction, artifact and noise reduction, enhanced workflow, and lesion characterization, and segmentation and quantitation, among many others. For theranostics, there are potentially significant applications that could move closer to precision medicine. Perhaps the most important application is predictive dosimetry from diagnostic images to optimize therapeutic dose. There are also valuable benefits from AI-augmented radioligand design and development, preclinical imaging, and practice sustainability. Generative AI has also emerged as a powerful tool to support decision-making, information dissemination, and medical image analysis. There are, however, several ongoing challenges that must be considered pertaining to the development and application of AI tools in theranostics.
Indexed as
Identifiers
What 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.