ReviewJournal of clinical medicine2026
The Current Landscape of Artificial Intelligence in Positron Emission Tomography (PET) Imaging Across the Cancer Continuum.
Review in Journal of clinical medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
PET scans have long been used in oncology imaging to provide molecular and metabolic information about diseases. The use of artificial intelligence (AI) in PET scans in oncology theranostics has the potential to optimise PET modality and overcome the constraints that PET scans have, such as semi-quantitative metrics, reader subjectivity, and variability across scanners/institutions. Advances in AI and radiomics are overcoming those limitations by deep learning lesion detection, enhancing image reconstruction, and improving noise resolution, which allows ultra-low dose acquisitions, while physics-informed models integrate with PET systems to strengthen interpretability and quantitative accuracy. There are also predictive AI frameworks that link PET imaging biomarkers to therapy response and outcomes, create individualised care and are even able to simulate treatment response and help with treatment planning. However, challenges do exist. Most AI PET studies are retrospective, single-centre, and underpowered (small sample), with limited external validation and inconsistent standardisation (in acquisition, segmentation, and extraction), leading to poor reproducibility and higher performance estimates. Furthermore, ethical considerations, including data protection and transparency, need to be considered before implementation. Federated learning, physics-informed frameworks, and adherence to standardised protocols offer steps towards regulated AI systems. In summary, PET is evolving from an imaging modality to a platform with the integration of deep learning, radiomics and reconstruction capable of predicting treatment response and guiding treatment. With rigorous prospective validation, cross-institutional collaboration, and regulatory standardisation, AI in PET would create an advancement in nuclear medicine imaging in oncology.
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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.