Role of Artificial Intelligence in Theranostics
Open accessAbstract
We highlight emerging uses of artificial intelligence (AI) in the field of theranostics, focusing on its significant potential to enable routine and reliable personalization of radiopharmaceutical therapies (RPTs). Personalized RPTs require patient-specific dosimetry calculations accompanying therapy. Additionally we discuss the potential to exploit biological information from diagnostic and therapeutic molecular images to derive biomarkers for absorbed dose and outcome prediction; toward personalization of therapies. We try to motivate the nuclear medicine community to expand and align efforts into making routine and reliable personalization of RPTs a reality.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
AI optimizes planning of radiopharmaceutical therapy by incorporating patient-specific factors, radiotracer biodistribution profiles, and dosimetry calculations.
“AI’s role extends to optimizing the planning of radiopharmaceutical therapy, incorporating patient-specific factors, radiotracer biodistribution profiles, and dosimetry calculations [122].”
From Radiopharmaceuticals: navigating the frontier of precision medicine and therapeutic innovation (2024) · cites this paper as [122]
The citation lists haven’t been fetched yet
References 113 · Cited by 42
- Authors
- Brosch-Lenz, J., Yousefirizi, F., Zukotynski, K., Beauregard, J.-M., Gaudet, V., Saboury, B., Rahmim, A., & Uribe, C.
- Affiliations
- BC Cancer Agency (CA)
- McMaster University (CA)
- Université Laval (CA)
- University of Waterloo (CA)
- National Institutes of Health (US)
- Corresponding author
- Uribe, Carlos
- ORCID
- Venue
- PET Clinics
- Publisher
- Elsevier BV
- Published
- 2021-10
- Volume
- 16
- Issue
- 4
- Pages
- 627-641
- ISSN
- 1556-8598 (ISSN-L)
- OpenAlex
- W3199922519
- Cited by
- 42
- References
- 113
- Citations by year
- 2026: 9 · 2025: 10 · 2024: 14 · 2023: 3 · 2022: 4 · 2021: 2
- FWCI
- 0.94
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- PET Clinics (publishedVersion)
- PubMed (publishedVersion)
- UWSpace (University of Waterloo) (submittedVersion, cc-by-nc-nd)
- Funders
- Natural Sciences and Engineering Research Council of Canada (RGPIN-2019-06467, RGPIN-2021-02965)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiopharmaceutical Chemistry and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- artificial intelligence, theranostics, radiopharmaceutical therapy, personalized treatment, patient-specific dosimetry, outcome prediction, biomarkers, molecular imaging, nuclear medicine
- MeSH terms
- Artificial Intelligence, Humans, Nuclear Medicine, Radiometry, Radiopharmaceuticals, Precision Medicine
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
