Synthetic data : Towards a new era of artificial intelligence
ALIA SANTé
Synthetic data: towards a new era of artificial intelligence
Reading time: 4 minutes
Over the past decade, major technological advances have dramatically reshaped various sectors thanks to AI.
However, data quality and quantity play a crucial role in the development and performance of AI algorithms.
In healthcare, data is often limited and highly confidential. This poses a major challenge in terms of access to sufficient quantities of high-quality data.
Indeed, AI is currently hampered by data scarcity, high cost and confidentiality.
Imagine a world where it would be possible to obtain unlimited amounts of high-quality, inexpensive, anonymous and secure data. This is now possible thanks to synthetic data!
What is synthetic data ?
Synthetic data are generated by artificial intelligence algorithms trained on real data. They faithfully reproduce the characteristics and relationships present in the original dataset. This innovative data overcomes the challenges of AI, particularly in healthcare where data confidentiality is crucial.
On the other hand, less than 1% of the data used for AI is synthetic. But Gartner predicts that by 2030, they will surpass real data in many models.
"By 2030, synthetic data will eclipse real data in a wide range of artificial intelligence models". Gartner has also placed synthetic data on the "Impact Radar for Edge AI", putting it in the top 3 of the hottest technologies.
Gartner
In an increasingly data-driven world, let’s explore how synthetic data can push the current limits of AI.
The benefits of synthetic data
- Unlimited quantity : Build unrestricted quantitative data sets, ideal for areas where real data is limited
- Improved accessibility : Overcome the challenges of accessing real data, which is often costly and regulated.
- Cost-efficiency : Synthetic data are often more cost-effective, offering an economical alternative for testing simulations or performing statistical analysis.
- Guaranteed confidentiality : Being fictitious data, synthetic data is completely anonymous, respecting the privacy of individuals and facilitating its sharing.
How do you assess the quality of synthetic data?
Assessing the quality of synthetic data is based on three key dimensions: fidelity, usefulness and confidentiality.
- Fidelity : Synthetic data must faithfully reproduce the characteristics and statistical distribution of real data.
- Usefulness : The usefulness of synthetic data is assessed by comparing the performance of models trained solely with real data with those incorporating synthetic data.
- Confidentiality : Synthetic data must be fully anonymized. Metrics such as the absence of duplicates and the nearest-neighbor confidentiality score guarantee data security.
Create your own synthetic data with Alia Santé
Alia Santé, made up of experts in artificial intelligence, offers an innovative synthetic data generation platform.
Alia DataGen uses AI to create high-quality synthetic data, overcoming the challenges of data scarcity and confidentiality. The quality report assigns a score based on various metrics, contributing to the overall assessment.
Try the Alia DataGen platform now to generate synthetic data and transform your approach to artificial intelligence!
Conclusion
Synthetic data is revolutionizing AI, offering solutions to the challenges of real data. It opens up access to high-quality data, enabling the continuous improvement of AI models. Without doubt, they are the key to propelling AI towards a robust evolution, increasing performance while preserving privacy.
Thank you for following us on this exciting journey towards synthetic data!