A systematic review of AI as a digital twin for prostate cancer care
Bibliographic record
Abstract
Artificial Intelligence (AI) and Digital Twin (DT) technologies are rapidly transforming healthcare, offering the potential for personalized, accurate, and efficient medical care. This systematic review focuses on the intersection of AI-based digital twins and their applications in prostate cancer pathology. A digital twin, when applied to healthcare, creates a dynamic, data-driven virtual model that simulates a patient's biological systems in real-time. By incorporating AI techniques such as Machine Learning (ML) and Deep Learning (DL), these systems enhance predictive accuracy, enable early diagnosis, and facilitate individualized treatment strategies for prostate cancer. This review systematically examines recent advances (2020-2025) in AI-driven digital twins for prostate cancer, highlighting key methodologies, algorithms, and data integration strategies. The literature analysis also reveals substantial progress in image processing, predictive modeling, and clinical decision support systems, which are the basic tools used when implementing digital twins for prostate cancer care. Our survey also critically evaluates the strengths and limitations of current approaches, identifying gaps such as the need for real-time data integration, improved explainability in AI models, and more robust clinical validation. It concludes with a discussion of future research directions, emphasizing the importance of integrating multi-modal data with Large Language Models (LLMs) and Vision-Language Models (VLMs), scalability, and ethical considerations in advancing AI-driven digital twins for prostate cancer diagnosis and treatment. This paper provides a comprehensive resource for researchers and clinicians, offering insights into how AI-based digital twins can enhance precision medicine and improve patient outcomes in prostate cancer 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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".