A roadmap for personalized medicine: the findings of the IC2PerMed project
Bibliographic record
Abstract
Introduction: Personalized Medicine (PM) tailors prevention and treatment to individuals based on their unique characteristics. It has the potential to improve health outcomes and healthcare sustainability by optimizing resource allocation. Both the European Union (EU) and China have prioritized PM in their health strategies. The IC2PerMed project was established to foster collaboration between the EU and China by developing a joint PM roadmap. Methods: To assess the state of PM in the EU and China, the project conducted a comprehensive mapping of relevant policies, programs, funding mechanisms, and health ecosystems. Additionally, three Delphi surveys were carried out, identifying 65 priorities, which were synthesized into a set of strategic actions. Results: The main output of the project is a joint roadmap for implementing PM in the EU and China. The roadmap promotes best practice exchange and addresses potential barriers to PM adoption. It outlines structural actions including enhancing health literacy, fostering intersectoral and international collaboration, ongoing review of emerging technologies, and facilitating innovation market entry through needs assessment and Health Technology Assessment. Key enablers such as data interoperability and shared standards are highlighted, along with ethical, social, and regulatory considerations that are universally relevant to PM implementation. Discussion: A shared action plan can guide health policy and help policymakers understand the interconnection between healthcare, the economy, and society. By supporting international projects and investing in research and innovation, stakeholders can advance global healthcare.
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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 | 0.101 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".