Making the case for digital twins: Italian healthcare needs AI-driven predictive modeling for personalized medicine
Bibliographic record
Abstract
Precision medicine seeks to tailor care by integrating genetic, clinical, and environmental data. Digital twins, dynamic, virtual replicas of patients that are updated with longitudinal information, represent a significant step in this direction. Enabled by artificial intelligence, they allow in silico experimentation to simulate therapies, disease trajectories, and adverse events, reducing risk and sharpening personalization. By bridging data and decisions, digital twins can promote earlier diagnosis, targeted treatments, and faster drug discovery, supporting a shift from reactive to predictive and participatory care. Nonetheless, challenges surrounding data integration, privacy, regulation, and equity persist and necessitate collaborative solutions. This viewpoint examines the opportunities and system-level requirements to integrate digital twins into Italian healthcare. Digital twins redefine medicine by turning episodic encounters into continuous, adaptive care. They can anticipate clinical events, simulate individualized treatments, and support shared decision-making, advancing the vision of predictive, preventive, personalized, and participatory medicine. Realizing this potential requires robust governance, interoperable infrastructures, and clinician training, alongside ethical frameworks that protect autonomy and fairness. Public-private partnerships and international collaboration will be crucial for the responsible, inclusive, and transparent adoption of these initiatives. Ultimately, digital twins inaugurate a paradigm in which simulation and clinical reality converge, fostering innovation that is both scientifically rigorous and deeply human.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".