Cover Photo by National Cancer Institute on Unsplash
In our previous article, we discussed how healthcare data is often affected by important data quality issues, creating a challenging context for AI development. These issues comprise imbalance data, missing data, small data, noisy data, biased data, and sensitive data, where the Data-Centric AI paradigm can prove extremely transformative.
Within this data-centric mindset, synthetic data has been revolutionizing the way organizations interact with and improve their data assets, paving the way for a data revolution: one where artificially generated data will become a must-have across all industries.
In this article, we will explore the benefits that synthetic data offers for healthcare domains, namely in what concerns the mitigation of its data quality issues and concerns.
Tackling Healthcare Domains with Synthetic Data
By harnessing the power of synthetic data, data teams can revolutionize healthcare practices and create innovative solutions to improve diagnostics and patient care:
Perform Data Augmentation with Synthetic Data to mitigate Imbalanced Data
Imbalanced datasets, where one class, category, or subgroup is significantly more represented when compared to others, are a common issue in healthcare, leading to biased models that perform poorly on the minority classes.
Synthetic data generation techniques can be employed to balance the class distribution, thereby improving model performance. For instance, synthetic data can be created to supplement the limited positive cases and underrepresented subgroups in detecting rare diseases, ensuring more robust and accurate diagnostics.
Replace Missing Data with Synthetic Data via Data Imputation
Incomplete patient records and missing data points are frequent challenges in healthcare datasets. This can lead to incomplete analyses and inaccurate predictions.
Synthetic data can be used to fill in missing values, creating complete and comprehensive datasets for analysis. By generating synthetic data that resembles real patient information, researchers and practitioners can ensure a more holistic view of patient health, ultimately leading to improved decision-making.
Increase Dataset Size with Synthetic Data to overcome the Lack of Data
In medical research, access to large datasets is often limited due to privacy concerns and data sharing complexities. Additionally, for particular rare conditions, there may not be enough samples to allow proper training of AI solutions.
Synthetic data generation can help augment small datasets, enabling the development of more robust models. For instance, synthetic data can create larger patient cohorts, enabling researchers to accurately develop their diagnosis, prognosis, and treatment models with sufficient data.
Generate tailored examples with Synthetic Data to improve Rare Event Detection
Outliers and noisy data can distort analysis results and hinder accurate predictions.
Synthetic data can aid in handling such instances by generating data points that are representative of this behavior, improving the recognition of rare and extreme events, such as adverse reactions to drugs or treatments, or sparse values in medical records.
Create Diverse Synthetic Datasets to overcome Data Bias
Bias in healthcare data can stem from various sources, including population underrepresentation. These biases can lead to disparities in healthcare outcomes and treatment recommendations.
Synthetic data can be used to mitigate bias by creating diverse and representative datasets that account for various population segments. By training models on more inclusive data, healthcare professionals can make more equitable and effective decisions.
Guarantee Data Privacy and Access through Synthetic Data
Sharing or analyzing raw patient data can lead to privacy breaches and legal implications.
Synthetic data offers a privacy-preserving alternative, allowing researchers and organizations to generate data that retains the statistical properties of the original dataset without revealing sensitive patient information. This enables collaboration and research without compromising patient confidentiality.
Conclusion
The use of synthetic data in healthcare holds immense potential to address the complex challenges associated with real-world medical and biomedical data.
By mitigating issues related to imbalanced, missing, small, noisy, biased, and sensitive data, synthetic data empowers healthcare professionals, researchers, and innovators to develop more accurate diagnostic tools, personalized treatments, and predictive models, bridging the gap between innovation and improved patient care and ultimately shaping the future of healthcare for the better.
Start leveraging the benefits of synthetic for your healthcare use cases with Fabric to build accurate, equitable, and responsible diagnosis and treatment models. Explore our community version or contact us for trial access to the full platform and embrace the future of healthcare through data-driven precision and patient-centered care.
