Synthetic data for clinical research and innovation: opportunities, challenges and future directions

Data form the backbone of modern health care, driving improvements in patient care, enabling clinical research and supporting public health initiatives. The demand for high-quality individual-level data is growing, with electronic health records and clinical trial data offering critical opportunities for secondary analyses, hypothesis testing and methodological innovation. However, accessing and sharing this high-quality real-world data to enable personalized care and to respond to rapidly changing conditions is often limited by strict privacy requirements and complex regulations. This perspective highlights the promise of synthetic data in enabling research access, accelerating artificial intelligence development and supporting clinical trials while also discussing key barriers, including legal and ethical uncertainty, re-identification risk and the need for standardized validation frameworks. In this context, synthetic data present a promising path forward by enabling research while addressing privacy concerns, streamlining administrative processes and decreasing costs.

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Publication Details

Journal
ESMO Real World Data and Digital Oncology
Published
2025-11-21
DOI
https://doi.org/10.1016/j.esmorw.2025.100651
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
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article

Synthetic data for clinical research and innovation: opportunities, challenges and future directions

Mattia Delleani
ESMO Real World Data and Digital Oncology
Machine Learning in Healthcare
article

Synthetic data for clinical research and innovation: opportunities, challenges and future directions

Mattia Delleani
article en

Abstract

Data form the backbone of modern health care, driving improvements in patient care, enabling clinical research and supporting public health initiatives. The demand for high-quality individual-level data is growing, with electronic health records and clinical trial data offering critical opportunities for secondary analyses, hypothesis testing and methodological innovation. However, accessing and sharing this high-quality real-world data to enable personalized care and to respond to rapidly changing conditions is often limited by strict privacy requirements and complex regulations. This perspective highlights the promise of synthetic data in enabling research access, accelerating artificial intelligence development and supporting clinical trials while also discussing key barriers, including legal and ethical uncertainty, re-identification risk and the need for standardized validation frameworks. In this context, synthetic data present a promising path forward by enabling research while addressing privacy concerns, streamlining administrative processes and decreasing costs.

ESMO Real World Data and Digital OncologyVol. 10
Istituto di Ricerche Chimiche e Biochimiche G. Ronzoni (IT)
Openalex Percentile: Top 81%
Machine Learning in Healthcare
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Synthetic data for clinical research and innovation: opportunities, challenges and future directions — Mattia Delleani · ESMO Real World Data and Digital Oncology (2025) | TGRS Research Map | TGRS