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Synthetic Biology

Synthetic Biology

Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
Golden rice
Golden rice is a genetically modified...

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Related Experiment Video

Updated: May 6, 2026

Fabrication of 3D Cardiac Microtissue Arrays using Human iPSC-Derived Cardiomyocytes, Cardiac Fibroblasts, and Endothelial Cells

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The Cardiologist Driving Synthetic AI: The TIMA Method for Clinically Supervised Synthetic Data Generation.

Gianmarco Parise¹, Roberto Ceravolo^2,3, Fabiana Lucà^1,4

¹CARIM, School for Cardiovascular Diseases, Maastricht University, 6211 LK Maastricht, The Netherlands.

Journal of Clinical Medicine

|February 27, 2026

Summary

The TIMA method enhances synthetic data reliability in cardiovascular medicine by integrating clinicians into AI development. This ensures synthetic datasets are clinically meaningful and trustworthy for AI-assisted research.

Keywords:

TIMA cardiology clinical validation predictive models synthetic artificial intelligence

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Last Updated: May 6, 2026

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Published on: March 14, 2021

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In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

Area of Science:

Cardiovascular Medicine
Artificial Intelligence
Data Science

Background:

Synthetic data generation using AI is growing in cardiovascular medicine.
Concerns about clinical reliability hinder the adoption of synthetic datasets.
The TIMA method addresses these concerns by involving clinicians.

Purpose of the Study:

Introduce the TIMA (Team-Implementation Multidisciplinary Approach) framework.
Demonstrate how clinician-data scientist collaboration improves synthetic AI data.
Enhance the clinical robustness and plausibility of synthetic AI outputs.

Main Methods:

The TIMA approach structures synthetic data generation via continuous clinician-data scientist interaction.
Cardiologists define constraints, verify relationships, and assess plausibility.
Iterative validation ensures alignment with clinical knowledge.

Main Results:

Synthetic datasets generated using TIMA adhered more closely to clinical logic.
Collaboration facilitated early detection of implausible variable interactions.
Improved interpretability and internal consistency of synthetic data were observed.

Conclusions:

TIMA offers a scalable model for integrating synthetic AI in cardiology.
Embedding clinical expertise ensures synthetic data is clinically meaningful.
This approach strengthens trust and reliability in AI-driven cardiovascular research.