Overview

This service provides a comprehensive evaluation and benchmarking report for SMEs' AI models, leveraging genomic research datasets. Following agreed-upon protocols and recommendations, the AI model is rigorously tested against curated datasets. This occurs in a secure environment managed by Karolinska Institutet. SMEs submit their models to the Swedish TEF Health node under pre-signed agreements, ensuring full confidentiality. The evaluation and benchmarking process produces detailed insights, resulting in a report that supports SMEs in validating and refining their AI solutions.

We provide expertise and technical support in the following areas:

• Project design & management
• AI model evaluation and benchmarking

How can the service help you?

This service helps address uncertainties about your AI model’s performance on external datasets. By providing thorough validation, it demonstrates the maturity and reliability of your model. The resulting report can serve as a valuable tool to build trust and confidence among stakeholders, supporting your efforts to showcase the model’s capabilities.

How the service will be delivered?

The service will be delivered according to established ethical agreements and guidelines, in collaboration with the SME and researchers from the Swedish TEF-Health node. Data usage is facilitated through a secure virtual environment managed by Karolinska Institutet, ensuring the highest standards of data protection.

Additional information

Provider description

The Swedish TEF-Health node is a collaboration between Karolinska Institutet, SciLifeLab and RISE, and is led by Karolinska Institutet. Together, we offer world-leading services with our unique collection of core facilities. We can grant services in expert consulting, virtual- and physical testing in the range of in vivo imaging, ex vivo OMICS, pharmaceutical development, simulated healthcare environments, AI-system validation and development, advanced data analysis and other data-driven life science.

Technical description

The service evaluates AI models in a secure environment using high-performance computing infrastructure optimized for large genomic datasets. Models are tested against curated and annotated genomic datasets using state-of-the-art frameworks such as TensorFlow and scikit-learn. Evaluation metrics, including accuracy, precision, and recall, are calculated to benchmark performance against industry standards and reference models. Encrypted storage and strict access controls ensure data security.

Service customization

The service can be customized according to your specific needs. It may be required to combine this service with other services on offer.

Use case example

Context

A biotech SME specializing in rare diseases has developed an AI model to predict genetic predispositions for a rare neurological disorder. The model was trained on internal datasets but has not been validated on external genomic data. Investors and clinical partners are reluctant to adopt the solution without independent evaluation and benchmarking to ensure its generalizability and maturity.

Objective

To validate the AI model’s accuracy and robustness on external genomic datasets, benchmark its performance against existing solutions, and deliver a detailed evaluation report to gain stakeholder trust and regulatory approval.

Solution

The SME submits its AI model to the Swedish TEF-Health node for evaluation. Using secure infrastructure and curated genomic datasets relevant to rare diseases, the model undergoes extensive testing and benchmarking against industry standards.

Implementation

Ethical Agreement

The SME enters into an ethical agreement with researchers from the Swedish TEF-Health node, ensuring all data collection and usage complies with GDPR and national Swedish regulations.

Secure Access

Usage of the collected data is facilitated through a secure virtual environment managed by Karolinska Institutet, ensuring the highest standards of data protection.

Evaluation and Benchmarking

The model is assessed using metrics like sensitivity, specificity, and ROC-AUC, focusing on its performance in predicting rare disease risks.

Outcome

A benchmarking report is generated, highlighting the model's strengths, weaknesses, and recommendations for improvement.

Benefits

• Validation & Credibility: Independent validation enhances trust among clinical and regulatory stakeholders.
• Competitive Benchmarking: Aligning with industry standards provides an advantage in the rare disease AI market.
• Model Refinement: Insights from the report drive improvements for clinical readiness.

Impact

The SME secures stakeholder confidence, accelerates discussions with clinical partners, and positions its AI solution as a trusted tool for rare disease risk prediction, paving the way for market adoption and broader collaborations.