Recommendations on analysing and testing for biases in datasets
Service Description
Overview
This service delivers specialized technical guidance on detecting, evaluating, and mitigating systematic biases within medical datasets and across differing data sources. Hidden disparities in training data arising from variations in measurement hardware, operator techniques, or study protocols can severely compromise algorithm accuracy and lead to unexpected performance drops when deployed in real-world clinical environments. The primary objective is to give startups and digital health developers a structured approach to uncovering hidden data imbalances, ensuring machine learning models are trained on representative, reliable data.
How can the service help you?
Unidentified biases in training datasets often cause machine learning models to learn unintended shortcuts rather than genuine biomedical signals, leading to poor generalization. This service provides actionable diagnostics to identify and correct these vulnerabilities early.
- Before: Unchecked datasets where subtle device-level differences or operator variations risk distorting model training and output validity.
- After: A comprehensive bias assessment identifying data collection artifacts, demographic imbalances, and hardware-induced variations alongside concrete mitigation strategies.
It empowers development teams to refine data collection protocols, enhance algorithmic robustness across different hardware, and prepare their AI solutions for rigorous clinical validation.
How will the service be delivered?
The service is conducted through remote technical consultations and analytical evaluations. Clients submit their dataset documentation, sensor specifications, and study protocols via secure digital channels. Our experts analyse the data structures and protocols, then present findings and recommendations in a structured review meeting.
Additional information
Provider description
Operating from the Department of Artificial Intelligence in Biomedical Engineering at Friedrich-Alexander-Universität Erlangen-Nürnberg, we are a service provider node within the TEF-Health consortium. The research group specializes in machine learning, biomedical signal processing, and multimodal sensor synchronization. Our team provides testing infrastructure and scientific support for evaluating medical devices, wearables, and contactless sensing systems.
Technical details
The evaluation employs both exploratory data science and explainable AI (XAI) techniques to systematically identify hidden dataset discrepancies:
- Input Data Requirements: Sample datasets and/or documentation detailing data collection procedures, sensor output specifications, and clinical study protocols.
- Core Tasks & Processing: Unsupervised cluster analysis (e.g., k-means, t-SNE/UMAP) to identify systemic data clustering driven by operator or hardware variations; Explainable AI methods for supervised models to verify whether the algorithm relies on genuine physiological features rather than dataset artifacts.
- Outputs: A comprehensive analysis report highlighting identified dataset limitations, confounders, and recommendations for protocol adjustments or data re-balancing prior to model training.
Service customization
The engagement scope is adaptable based on project needs. Clients can opt for an initial review of collection protocols before gathering data or request a full analytical evaluation of existing multi-center datasets to isolate hardware-specific biases.
Provider & Contact
The final price will be determined in the contract between the service provider and the applicant.