Algorithm improvement
Service Description
Overview
This service delivers specialized technical consultation and machine learning expertise to optimize, refine, and enhance artificial intelligence algorithms for digital health applications. Building robust algorithms requires continuous iteration across model architectures, feature engineering, and training pipelines. Through tailored expert evaluation, the service supports startups and SMEs in identifying performance bottlenecks, improving model accuracy, and optimizing computational efficiency. The primary objective is to accelerate the technological readiness of health-focused algorithms by applying agile development practices and peer-reviewed data science methodologies to real-world medical data challenges.
How can the service help you?
Developing algorithms internally often leads to plateaued model performance, overfitting, or inefficiency when handling complex biomedical datasets. This service provides targeted external expertise to address these technical challenges directly.
- Before: An algorithm hampered by sub-optimal accuracy, high compute requirements, or poor generalization across real-world data distributions.
- After: An upgraded model architecture supported by refined training strategies, improved predictive performance, and robust validation protocols.
It enables technical teams to solve complex data science roadblocks, speed up product iteration cycles, and ensure their core algorithms meet necessary technical standards.
How will the service be delivered?
The service is conducted through remote technical consultations, code/model reviews, and joint collaborative sessions. Client teams provide their specific requirements, performance targets, or current algorithmic pipelines. Our experts analyse the existing implementation and deliver tailored technical guidance and actionable optimization strategies.
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 advisory framework draws upon established agile development principles and modern deep learning / machine learning methodologies for biomedical data processing.
- Input Data Requirements: Project requirements, performance metrics, model architecture documentation, or anonymized training data samples.
- Core Tasks & Processing: Code and architecture audits, feature engineering assessment, hyperparameter optimization strategies, bias/overfitting analysis, and algorithmic benchmarking against medical data standards.
- Outputs: Technical consultation reports, code optimization recommendations, and structured technical documentation detailing improved model pipelines.
- Scientific Reference: Aligned with peer-reviewed research on biomedical algorithm development (DOI: 10.1109/OJEMB.2024.3356791).
Service customization
The scope of engagement is flexible and customized to match the client's development stage. SMEs can choose focused guidance on specific tasks (e.g., feature selection or model hyperparameter tuning) or request a broad end-to-end algorithmic review across the full research and development lifecycle.
Provider & Contact
depending on seniority