Current Proteomics
Ge Zhang, Jiashen Xu, Zenglei Zhang, Chaoyang Yu, Kaisaierjiang Kadier, Di Lu, Mingxuan Duan, Ge Zhang. A Multimodal artificial intelligence framework for mechanistic discovery and digital twin construction in cardiovascular medicine. Current Proteomics. 2025;22(5):100054. https://doi.org/10.1016/j.curpro.2025.100054
A Multimodal artificial intelligence framework for mechanistic discovery and digital twin construction in cardiovascular medicine
This Correspondence discusses a research agenda connecting multimodal biological interpretation with patient-specific simulation in cardiovascular medicine. It sets out design and translation requirements without reporting a newly built, clinically validated digital twin system.
No new empirical evaluation is reported in this article. Cited examples and their cohorts are not a new combined study population, and no synthetic sample total is assigned.
How can multimodal AI support cardiovascular mechanism research and updateable patient-specific digital models?
The Correspondence outlines two linked directions for cardiovascular AI: investigating disease mechanisms and developing patient-specific digital twins. It discusses combining imaging and molecular information to generate network and disease-subtyping hypotheses, then considers updateable physiological models, image-to-mesh workflows and treatment-scenario simulation. Longitudinal data, computational capacity, prospective evaluation, privacy and governance are presented as requirements for translation. This page organizes those narrative arguments for research planning and methodological citation. The cited empirical examples remain evidence from other works; cross-modal integration does not itself establish causation, and simulated treatment scenarios are not observed therapeutic effects in patients.
The authors propose complementary research directions rather than introduce a clinically validated digital twin product.
| Measure | Value |
|---|---|
| Argument topic | Research agenda |
| Evidence basis | Narrative synthesis |
Source locator: Publisher correspondence: To the Editor; Sections 1–2
Data integration and cited empirical examples are discussed as a basis for interpretation, not as new causal experiments in this Correspondence.
| Measure | Value |
|---|---|
| Argument topic | Cross-modal biological interpretation |
| Evidence basis | Narrative synthesis |
Source locator: Publisher correspondence: Sections 1.1–1.2
The simulation discussion concerns model design and possible scenarios, not observed human treatment effects.
| Measure | Value |
|---|---|
| Argument topic | Patient-specific simulation |
| Evidence basis | Narrative synthesis |
Source locator: Publisher correspondence: Sections 2.1–2.2
These are stated development and evaluation requirements, not completed prospective clinical validation.
| Measure | Value |
|---|---|
| Argument topic | Translation requirements |
| Evidence basis | Narrative synthesis |
Source locator: Publisher correspondence: Sections 3.1–3.3; Section 4 Conclusion
Cite for framing a cardiovascular AI research agenda around mechanistic questions and individualized digital modeling, not for product validation.
Evidence: KF1
Cite for the methodological rationale of cross-modal hypothesis generation; causal claims require suitable primary experimental evidence.
Evidence: KF2
Cite as design background for physiological modeling and scenario simulation, not as evidence that a drug, surgery or commercial digital twin improves outcomes.
Evidence: KF3
Cite when discussing evaluation, data stewardship and implementation requirements; prospective benefit remains a question for future empirical assessment.
Evidence: KF4
| Row ID | Component | Context | Finding | Evidence Level | Scope | Source Locator | Evidence refs |
|---|---|---|---|---|---|---|---|
| evidence-cardiovascular-ai-1 | Research agenda | The authors propose complementary research directions rather than introduce a clinically validated digital twin product. | Develop multimodal AI along two directions: mechanism-oriented research and individualized digital twins. | Narrative synthesis | Cite for framing a cardiovascular AI research agenda around mechanistic questions and individualized digital modeling, not for product validation. | Publisher correspondence: To the Editor; Sections 1–2 | KF1 |
| evidence-cardiovascular-ai-2 | Cross-modal biological interpretation | Data integration and cited empirical examples are discussed as a basis for interpretation, not as new causal experiments in this Correspondence. | Integrate imaging and molecular information to formulate network and disease-subtyping hypotheses. | Narrative synthesis | Cite for the methodological rationale of cross-modal hypothesis generation; causal claims require suitable primary experimental evidence. | Publisher correspondence: Sections 1.1–1.2 | KF2 |
| evidence-cardiovascular-ai-3 | Patient-specific simulation | The simulation discussion concerns model design and possible scenarios, not observed human treatment effects. | Explore updateable physiological models, image-to-mesh workflows and treatment-scenario simulation. | Narrative synthesis | Cite as design background for physiological modeling and scenario simulation, not as evidence that a drug, surgery or commercial digital twin improves outcomes. | Publisher correspondence: Sections 2.1–2.2 | KF3 |
| evidence-cardiovascular-ai-4 | Translation requirements | These are stated development and evaluation requirements, not completed prospective clinical validation. | Address longitudinal data, computational resources, prospective evaluation, privacy and governance before clinical translation. | Narrative synthesis | Cite when discussing evaluation, data stewardship and implementation requirements; prospective benefit remains a question for future empirical assessment. | Publisher correspondence: Sections 3.1–3.3; Section 4 Conclusion | KF4 |
Develop multimodal AI along two directions: mechanism-oriented research and individualized digital twins. The authors propose complementary research directions rather than introduce a clinically validated digital twin product.
Evidence: KF1
Integrate imaging and molecular information to formulate network and disease-subtyping hypotheses. Data integration and cited empirical examples are discussed as a basis for interpretation, not as new causal experiments in this Correspondence.
Evidence: KF2
Explore updateable physiological models, image-to-mesh workflows and treatment-scenario simulation. The simulation discussion concerns model design and possible scenarios, not observed human treatment effects.
Evidence: KF3
Address longitudinal data, computational resources, prospective evaluation, privacy and governance before clinical translation. These are stated development and evaluation requirements, not completed prospective clinical validation.
Evidence: KF4
Study-level author-maintained evidence summary. Not participant-level data and not a ready-to-pool meta-analysis dataset. For formal citation, use the original article DOI.
Download study-level evidence CSV (UTF-8)Formula safety: cells starting with =, +, -, @ (after leading whitespace), or tab/CR/LF are prefixed with an apostrophe. Treat all cells as text; remove only that safety prefix when recovering the original string.
These records identify the same original work; database inclusion is not evidence of scientific validity or clinical benefit.