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digital-twin-clinical-agent
Maintainer FreedomIntelligence · Last updated April 1, 2026
Create patient digital twins for treatment simulation and outcome prediction.
Original source
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/digital-twin-clinical-agent
- Maintainer
- FreedomIntelligence
- License
- MIT
- Last updated
- April 1, 2026
Skill Snapshot
Key Details From SKILL.md
Key Notes
- The Digital Twin Clinical Agent creates AI-powered virtual replicas of individual patients by integrating genomics, imaging, wearable data, and clinical records. These digital twins enable clinical trial simulation, treatment response prediction, and personalized therapeutic optimization, qualified by EMA and aligned with FDA guidance.
- When simulating clinical trial outcomes for drug development.
- For creating patient-specific treatment response predictions.
- To optimize clinical trial design and reduce sample sizes.
- When predicting individual patient trajectories.
Source Doc
Excerpt From SKILL.md
Core Capabilities
-
Patient Digital Twin Creation: Build comprehensive patient models.
-
Clinical Trial Simulation: Predict trial outcomes virtually.
-
Treatment Response Prediction: Individualized response modeling.
-
Counterfactual Generation: "What-if" treatment scenarios.
-
Longitudinal Prediction: Forecast disease trajectories.
-
Trial Design Optimization: Reduce sample sizes, improve power.
Digital Twin Components
| Component | Data Sources | Models |
|---|---|---|
| Genomic Twin | WES/WGS, RNA-seq | Mutation effects, expression |
| Phenotypic Twin | EHR, labs, vitals | Clinical trajectories |
| Imaging Twin | CT, MRI, pathology | Tumor dynamics |
| Behavioral Twin | Wearables, PROs | Activity, symptoms |
| Pharmacokinetic | Drug levels, metabolism | PK/PD models |
Clinical Applications
| Application | Use Case | Benefit |
|---|---|---|
| Trial Simulation | Virtual control arms | Reduce placebo patients |
| Dose Optimization | Individual PK/PD | Personalized dosing |
| Treatment Selection | Compare therapies | Optimal choice |
| Progression Prediction | Disease trajectory | Early intervention |
| Drop-off Prediction | Compliance forecasting | Retention improvement |
Use cases
- When simulating clinical trial outcomes for drug development.
- For creating patient-specific treatment response predictions.
Not for
- Do not rely on this catalog entry alone for installation or maintenance details.
Upstream Related Skills
- Virtual_Lab_Agent - AI research coordination
- Multimodal_Radpath_Fusion_Agent - Data integration
- Multi_Ancestry_PRS_Agent - Genetic risk
- ctDNA_Dynamics_MRD_Agent - Disease monitoring
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