Nanomedicine and clinical diagnostics part I: applications in conventional imaging (MRI, X-ray/CT, and ultrasound)
Bibliographic record
Abstract
Integrating nanotechnologies in diagnostic imaging presents a promising step forward compared to traditional methods, which carry certain limitations. Conventional imaging routes, such as X-ray/computed tomography and magnetic resonance imaging, derive significant advantages from nanoparticles (NPs), which allow researchers and clinicians to overcome some of the limitations of traditional imaging agents. In this literature review, we explore recent advancements in nanomaterials being applied in conventional diagnostic imaging techniques by exploring relevant reviews and original research papers (e.g. experimental models and theoretical model studies) in the literature. Collectively, there are numerous nanomaterials currently being examined for use in conventional imaging modalities, and each imaging technique has unique NPs with properties that can be manipulated to answer an array of clinical questions specific to that imaging modality. There are still challenges to consider, including getting regulatory approval for clinical research and routine use about long-term biocompatibility, which collectively emphasize the need for continued research to facilitate the integration of nanotechnology into routine clinical practice. Most importantly, there is a continued need for strong, collaborative efforts between researchers, biomedical engineers, clinicians, and industry stakeholders, which are necessary to bridge the persistent gap between translational ideas and implementation in clinical settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".