Multiphysics modelling enhanced by imaging and artificial intelligence for personalised cancer nanomedicine: Foundations for clinical digital twins
Bibliographic record
Abstract
Nano-sized drug delivery systems have emerged as a more effective, versatile means for improving cancer treatment. However, the complexity of drug delivery to cancer involves intricate interactions between physiological and physicochemical processes across various temporal and spatial scales. Relying solely on experimental methods for developing and clinically translating nano-sized drug delivery systems is economically unfeasible. Multiphysics models, acting as open systems, offer a viable approach by allowing control over the individual and combined effects of various influencing factors on drug delivery outcomes. This provides an effective pathway for developing, optimising, and applying nano-sized drug delivery systems. These models are specifically designed to uncover the underlying mechanisms of drug delivery and to optimise effective delivery strategies. This review outlines the diverse applications of multiphysics simulations in advancing nanos-sized drug delivery systems for cancer treatment. The methods to develop these models and the integration of emerging technologies (i.e., medical imaging and artificial intelligence) are also addressed towards digital twins for personalised clinical translation of cancer nanomedicine. Multiphysics modelling tools are expected to become a powerful technology, expanding the scope of nano-sized drug delivery systems, thereby greatly enhancing cancer treatment outcomes and offering promising prospects for more effective patient care.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".