ReviewCureus2026
Artificial Intelligence in Nuclear Medicine (2015-2025): Clinical Applications, Regulatory Governance, and Pharmacotherapeutic Implications.
Review in Cureus, 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is becoming increasingly relevant in nuclear medicine, particularly in applications that depend on quantitative image analysis, image reconstruction, acquisition optimization, and clinical decision support. This critical narrative review examines the evolution, clinical applications, ethical and regulatory challenges, and pharmacotherapeutic implications of AI in nuclear medicine from 2015 to 2025. A structured literature search was conducted in PubMed/MEDLINE, Scopus, Web of Science, Google Scholar, and selected professional and regulatory websites, including those of the Society of Nuclear Medicine and Molecular Imaging, the European Association of Nuclear Medicine, the International Atomic Energy Agency, the US Food and Drug Administration, and the European Medicines Agency. The reviewed literature reports advances in deep learning-based reconstruction, denoising, and preservation of quantitative parameters in positron emission tomography and single-photon emission computed tomography. Clinical applications have expanded across oncology, neurology, cardiology, theranostics, and radiopharmacy-related workflows. However, the evidence remains limited by methodological heterogeneity, predominantly retrospective and single-center designs, restricted external validation, uneven regulatory development, and limited representation of Latin American settings. Overall, AI may improve diagnostic precision, personalized dosimetry, workflow efficiency, and radiopharmaceutical safety in nuclear medicine. Responsible implementation will require robust multicenter validation, transparent evaluation standards, adaptive governance, and multidisciplinary oversight. Radiopharmacists should be actively involved in this process, particularly in relation to radiopharmaceutical quality, traceability, dosimetry, and pharmacotherapeutic decision-making.
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.