1. Introduction to Synthetic Biomedical Data and Quality Assessment
The advancement of biomedical research is intrinsically linked to large-scale, high-quality datasets. Synthetic data, generated by computer algorithms, is a pivotal innovation for training AI models, deriving insights, and enabling collaborative research with privacy guarantees. It addresses challenges like data scarcity, privacy concerns in sharing sensitive health information, and accessibility for researchers.
However, synthetic data's utility depends entirely on its quality. It must accurately capture the statistical properties and patterns of real data. Generative models can unintentionally memorize training samples, risking sensitive information leakage. Thus, robust quality assessment is critical, evaluating fidelity (resemblance to real data), utility (usefulness for tasks), and privacy. Current evaluation methodologies lack standardization, highlighting a need for further development.
A key tension exists: high fidelity can increase privacy risks if models memorize data. Many advanced generative models are "black boxes," making it hard to diagnose deficiencies or subtle privacy leaks without comprehensive input-output analysis. This calls for more interpretable models and evaluation techniques.
For the full reference list and supplementary material, see the Google Doc.
2. Biomedical Data Modalities Amenable to Synthetic Generation
Synthetic data generation spans various biomedical data types. This section explores the major categories and their complexities. Understanding these modalities is crucial for tailoring generation and assessment techniques appropriately.
Time-Series Biomedical Signals
These signals represent dynamic physiological processes over time. Key examples include:
- Electrocardiograms (ECG): Record heart's electrical activity. Synthetic ECGs help overcome data scarcity and privacy issues for AI diagnostic tools. Methods range from mathematical models to GANs, VAEs, and Diffusion Models.
- Electroencephalograms (EEG): Measure brain's electrical activity. Synthetic EEGs aid in data augmentation, BCI calibration, and modeling brain dynamics (e.g., sleep, emotions). EEG data is often noisy, requiring robust models.
- Other Physiological Signals: Including Photoplethysmography (PPG), Electromyography (EMG), Electrooculography (EOG), Blood Pressure (BP), Heart Rate Variability (HRV), and mechano-acoustic signals. Each has unique characteristics and clinical applications.
Data Formats and Characteristics: Typically sequences of numerical values, stored in formats like .mat. Key characteristics for synthesis include sampling rates, amplitude ranges, noise profiles (e.g., baseline wander, $1/f$ noise), and non-stationarity.
Complexity Insight:
Simpler signals like a clean ECG beat are easier to synthesize than complex, artifact-prone EEG signals representing intricate brain states. Evaluation must be tailored to this complexity.
3. State-of-the-Art Generative Models
Various model architectures are used for biomedical data synthesis. This section provides an overview of dominant architectures and noteworthy applications. The choice of model often depends on the data type and desired output characteristics.
Key Trend: Conditional Generation
There's a shift towards models that generate data based on specific attributes or conditions (e.g., SSSD-ECG generating ECGs for specific clinical statements). This enhances utility but adds complexity to quality assessment, requiring validation of both realism and conditional accuracy.
4. Fundamental Dimensions for Assessing Synthetic Data Quality
Assessing synthetic data is multi-faceted. Key dimensions include fidelity, utility, privacy, and diversity. These are interconnected and often involve trade-offs. For example, very high fidelity might increase privacy risks if the model memorizes real data.
Contextual Utility:
"Utility" is task-dependent. Data useful for one task (e.g., binary classification) might be inadequate for another (e.g., detecting rare anomalies). Define target tasks before evaluating utility.
5. SOTA Quality Assessment Methodologies and Metrics
Evaluation requires metrics tailored to specific data types. This section explores metrics for biomedical signals (ECG, EEG) and other formats like EHRs. The "Train on Synthetic, Test on Real" (TSTR) paradigm is a key theme for utility assessment.
Metrics Summary by Aspect
Distribution of unique metrics from Table 1 & 2 by primary assessment aspect.
Metrics for Synthetic Biomedical Signals (ECG, EEG, etc.)
| Metric Name | Description/Purpose | Data Type(s) | Aspect Assessed |
|---|
Metrics for Synthetic EHRs and Other Formats
| Metric Name | Description/Purpose | EHR Structure | Aspect Assessed |
|---|
Beyond Known Metrics:
Current metrics compare against known characteristics. Challenges remain in detecting unforeseen artifacts or uncaptured complex interactions ("unknown unknowns"). Qualitative expert review is vital alongside quantitative measures.
6. Current Challenges and Future Research Avenues
Despite progress, several challenges persist in synthetic biomedical data generation and assessment. Addressing these is crucial for advancing the field.
Dynamic Landscape:
Generative AI is rapidly evolving (e.g., GANs to diffusion models, rise of multimodal AI). Evaluation methodologies must also evolve to remain state-of-the-art and address new challenges posed by these advancements.
7. Conclusion and Strategic Recommendations
Synthetic biomedical data is transformative but requires rigorous, multi-dimensional quality assessment. This includes evaluating fidelity, utility, privacy, and diversity, tailored to the data modality and application. The ultimate goal is "fitness for purpose," balancing benefits against risks and costs.
Actionable Recommendations:
A comprehensive, context-aware evaluation strategy is fundamental for building trust and enabling responsible deployment of synthetic biomedical data in healthcare.