Artificial Intelligence and the Future of Diagnostic and Therapeutic Radiopharmaceutical Development
Open accessAbstract
Novel diagnostic and therapeutic radiopharmaceuticals are increasingly becoming a central part of personalized medicine. Continued innovation in the development of new radiopharmaceuticals is key to sustained growth and advancement of precision medicine. Artificial intelligence has been used in multiple fields of medicine to develop and validate better tools for patient diagnosis and therapy, including in radiopharmaceutical design. In this review, we first discuss common in silico approaches and focus on their usefulness and challenges in radiopharmaceutical development. Next, we discuss the practical applications of in silico modeling in design of radiopharmaceuticals in various diseases.
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AI algorithms efficiently navigate vast datasets to reveal subtle patterns and anomalies that might escape human detection in radiopharmaceutical imaging.
“This partnership boosts the accuracy of radiopharmaceutical imaging, with AI algorithms efficiently navigating through vast datasets to reveal subtle patterns and anomalies that might escape human detection [121].”
From Radiopharmaceuticals: navigating the frontier of precision medicine and therapeutic innovation (2024) · cites this paper as [121]
The Introduction discusses AI as a way to speed radiopharmaceutical design, while Applications > CNS discusses computational prediction of tracer binding and accuracy of those predictions. Neither section addresses AI searching imaging datasets for subtle patterns or anomalies that humans might miss; searches for those concepts found no supporting passage in our copy. (LLM, conf=0.74)
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- Authors
- Ataeinia, B., & Heidari, P.
- Affiliations
- Massachusetts General Hospital (US)
- Harvard University (US)
- Corresponding author
- Heidari, Pedram
- Venue
- PET Clinics
- Publisher
- Elsevier BV
- Published
- 2021-10
- Volume
- 16
- Issue
- 4
- Pages
- 513-523
- ISSN
- 1556-8598 (ISSN-L)
- PMC
- PMC8453048
- OpenAlex
- W3189317366
- Cited by
- 31
- References
- 91
- Citations by year
- 2026: 4 · 2025: 12 · 2024: 4 · 2023: 4 · 2022: 5 · 2021: 2
- FWCI
- 0.61
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- PET Clinics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- NCI (K08CA249047)
- National Institutes of Health
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiopharmaceutical Chemistry and Applications
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- artificial intelligence, radiopharmaceutical development, radiopharmaceutical design, in silico modeling, personalized medicine, precision medicine, diagnostic radiopharmaceuticals, therapeutic radiopharmaceuticals, positron emission tomography
- MeSH terms
- Artificial Intelligence, Computer Simulation, Forecasting, Humans, Radiopharmaceuticals, Precision Medicine
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