Recent progress and applications of gold nanotechnology in medical biophysics using artificial intelligence and mathematical modeling
Bibliographic record
Abstract
Abstract In this topical review, we will explore and challenge how artificial intelligence (AI) and mathematical modeling apply towards the future in medical applications, focusing on their interactions with gold nanotechnology. There have been rapid advancements towards the applications of AI and mathematical modeling in medical biophysics. These specific techniques help to improve studies related to nanoscale technology. Many works have been published in relation to this topic; it is now time to collectively analyze and review them to assess the contributions these applications made within nanotechnology. Through this review, both theoretical and clinical data is examined for a fresh and present-day understanding. Observations of set parameters and defined equations through AI and mathematical modeling are made to help give explanation towards variable interaction. This review focuses on gold nanoparticle synthesis and preparation via the Turkevich and Brust and Schiffrins one-pot method. From this, findings show that gold nanoparticle size, shape, and overall functionality affect its synthetic properties. Depending on the characteristics within the gold nanoparticle, its ability to maximize light absorbency, wavelengths, and optical densities within the particle is limited. Finding an ideal wavelength (dependent on nanoparticle sizing) allows for higher absorbency of light within the nanoparticle itself. Examining the cellular uptake and cytotoxicity within the nanoparticle is done so via transmission electron microscope (TEM) and Fourier transform infrared radiation (FT-IR) spectroscopy. By manipulating AI and stochastic and diagnostic models, nanoparticle efficiency within precision cancer therapy is set to ensure maximal treatment. Set conditions allow ideal tumor treatment planning, where manipulated nano-probes are used in gold nanoparticle-based therapy. Versatility in nanoparticle sensors allow for multimodal imaging and assistance towards further diagnostic and therapeutic imaging practices. Drawn conclusions will help expand further knowledge and growth for future gold nanoparticle technology research in medical biophysics application using AI and mathematical modeling.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".