Evidence map›Paper›PMID 38646652›Full record

ReviewTheranostics2024

Theranostics and artificial intelligence: new frontiers in personalized medicine.

Gokce Belge Bilgin, Cem Bilgin, Brian J Burkett, Jacob J Orme, Daniel S Childs, Matthew P Thorpe, Thorvardur R Halfdanarson, Geoffrey B Johnson, Ayse Tuba Kendi, Oliver Sartor

Abstract readReview
In one paragraph

Review in Theranostics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.

0numbers the graph read from it
0cells of the map it votes in
57citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

57 citing papers in PubMed.

  1. Review
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  4. Applications of artificial intelligence in nuclear medicine.Zeitschrift fur medizinische Physik · 2026
    Review
  5. Review
  6. Review
  7. Review
  8. When evidence meets artificial intelligence.Lancet regional health. Americas · 2026
    Review
  9. Review
  10. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Gokce Belge BilginDepartment of Radiology, Mayo Clinic Rochester, MN, USA.
Cem BilginDepartment of Radiology, Mayo Clinic Rochester, MN, USA.
Brian J BurkettDepartment of Radiology, Mayo Clinic Rochester, MN, USA.
Jacob J OrmeDepartment of Oncology, Mayo Clinic Rochester, MN, USA.
Daniel S ChildsDepartment of Oncology, Mayo Clinic Rochester, MN, USA.
Matthew P ThorpeDepartment of Radiology, Mayo Clinic Rochester, MN, USA.
Thorvardur R HalfdanarsonDepartment of Oncology, Mayo Clinic Rochester, MN, USA.
Geoffrey B JohnsonDepartment of Radiology, Mayo Clinic Rochester, MN, USA.
Ayse Tuba KendiDepartment of Radiology, Mayo Clinic Rochester, MN, USA.
Oliver SartorDepartment of Radiology, Mayo Clinic Rochester, MN, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The field of theranostics is rapidly advancing, driven by the goals of enhancing patient care. Recent breakthroughs in artificial intelligence (AI) and its innovative theranostic applications have marked a critical step forward in nuclear medicine, leading to a significant paradigm shift in precision oncology. For instance, AI-assisted tumor characterization, including automated image interpretation, tumor segmentation, feature identification, and prediction of high-risk lesions, improves diagnostic processes, offering a precise and detailed evaluation. With a comprehensive assessment tailored to an individual's unique clinical profile, AI algorithms promise to enhance patient risk classification, thereby benefiting the alignment of patient needs with the most appropriate treatment plans. By uncovering potential factors unseeable to the human eye, such as intrinsic variations in tumor radiosensitivity or molecular profile, AI software has the potential to revolutionize the prediction of response heterogeneity. For accurate and efficient dosimetry calculations, AI technology offers significant advantages by providing customized phantoms and streamlining complex mathematical algorithms, making personalized dosimetry feasible and accessible in busy clinical settings. AI tools have the potential to be leveraged to predict and mitigate treatment-related adverse events, allowing early interventions. Additionally, generative AI can be utilized to find new targets for developing novel radiopharmaceuticals and facilitate drug discovery. However, while there is immense potential and notable interest in the role of AI in theranostics, these technologies do not lack limitations and challenges. There remains still much to be explored and understood. In this study, we investigate the current applications of AI in theranostics and seek to broaden the horizons for future research and innovation.

Indexed as

Artificial IntelligenceNeoplasmsPrecision MedicineAlgorithmsHumansTheranostic Nanomedicineartificial intelligencedrug discoverymachine learningnuclear medicinetheranosticstumor dosimetry

Identifiers

PMID38646652
PMCPMC11024845

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.