ReviewTheranostics2024
Theranostics and artificial intelligence: new frontiers in personalized medicine.
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.
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
57 citing papers in PubMed.
- 4D nanoimaging-guided smart therapeutics: spatiotemporal control of disease microenvironments.RSC advances · 2026Review
- Artificial Intelligence Across the Cancer Theranostics Workflow: Critical Appraisal of Current Evidence and Future Clinical Translation.Molecular imaging and biology · 2026Review
- Applications of Artificial Intelligence in Cancer Diagnosis and Treatment.Cancer medicine · 2026Review
- Applications of artificial intelligence in nuclear medicine.Zeitschrift fur medizinische Physik · 2026Review
- Upper Tract Urothelial Carcinoma: Molecular Pathogenesis and Current Treatment Strategies-A Narrative Review.Cancers · 2026Review
- Multifunctional Nano-Contrast Agent Carriers: From Traditional Platforms to Next-Generation Theranostic Applications in Molecular Imaging.Biomedicines · 2026Review
- When intelligence meets radiology: the dual impact of ai on radiologists' workload, burnout, and economic value.Abdominal radiology (New York) · 2026Review
- When evidence meets artificial intelligence.Lancet regional health. Americas · 2026Review
- In silico models in oncology, neurology, and epidemiology: systems-level and multiscale perspectives.NPJ systems biology and applications · 2026Review
- Establishment of the China Elderly Comorbidity Medical Database (CECMed) and its application in machine learning-based prediction.BMC geriatrics · 2026Article
- Artificial intelligence driven protein design and sustainable nanomedicine for advanced theranostics.Bioactive materials · 2026Review
- Review
- Flexible wearable medical devices: from material innovations and data processing to intelligent healthcare applications.Journal of advanced research · 2026Review
- Artificial intelligence empowered biomaterials for cancer therapy: From rational design to clinical translation.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- Magnetically Controlled Nanocarriers for Site-Specific Drug Delivery and Theranostics in Cancer Precision Medicine.Molecular imaging and biology · 2026Review
- The Current Landscape of Artificial Intelligence in Positron Emission Tomography (PET) Imaging Across the Cancer Continuum.Journal of clinical medicine · 2026Review
- Questionnaires on Perceptions of Artificial Intelligence in Health Care Among Health Care Students: Cross-Cultural Translation Into French and Linguistic Validation.JMIR medical education · 2026Article
- AI-driven nanomedicine for cancer theranostics.Molecular cancer · 2026Review
- Diabetes and its complications: molecular mechanisms, prevention and treatment.Signal transduction and targeted therapy · 2026Review
- RadioPharm: the database of radiopharmaceuticals.Nucleic acids research · 2026Article
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
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
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.