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Established in 2001 as a core facility at Karolinska Institutet and Karolinska University Hospital, our center was originally known as the Stockholm Medical Image Laboratory and Education (SMILE).
As of September 2024, we have transitioned to the Stockholm Medical Artificial Intelligence and Learning Environments (SMAILE), accompanied by a new management team.
This change reflects our strategic shift to focus on integrating artificial intelligence across various medical specialities, extending our research and applications beyond traditional medical imaging.
Here you can read more about the services that Stockholm Medical Artificial Intelligence and Learning Environments (SMAILE) offers.
SMAILE provides collaborative, educational support for AI healthcare innovation throughout the entire development lifecycle, from initial concept through regulatory approval to clinical deployment. We recognize that bringing AI solutions to clinical reality requires diverse expertise and resources that individual researchers, startups, and even established companies may lack internally.
Our approach to service delivery is fundamentally collaborative and educational. We don't simply execute tasks as directed but work as true partners, helping clients understand technical and regulatory considerations, make informed decisions, and build internal capabilities. Many engagements include knowledge transfer components where we document processes, provide training, and help establish internal procedures that enable clients to handle similar work independently in the future. This aligns with our academic mission of capacity building while ensuring sustainable, mutually beneficial relationships.
Services are designed to support diverse clients, including academic researchers (from within KI and external institutions), healthcare organizations implementing AI solutions, startups and SMEs developing AI medical devices, established medical technology companies, and pharmaceutical companies incorporating AI into drug development.
Many SMAILE services are accessible through TEF-Health, Europe's network of testing and experimentation facilities for digital health innovation. For startups and SMEs, accessing SMAILE services through TEF-Health may provide significant advantages, including subsidized rates, an integrated support ecosystem, and connections to Europe's broader digital health community.
Contact TEF-Health Network for information about TEF-Health programs and funding opportunities.
SMAILE utilizes the iLab platform for managing service requests and projects. The iLab system streamlines project initiation, tracks progress, manages billing, and maintains transparent communication throughout your engagement.
iLab Organizer :: Stockholm Medical Artificial Intelligence and Learning Environments (SMAILE)
The following table provides a high-level overview of SMAILE's core service offerings.
| Service | Description and scope | Key Deliverables | Timeline |
|---|---|---|---|
| AI Imaging Lab | End-to-end support for medical imaging AI projects covering all modalities (CT, MRI, ultrasound, histology, nuclear imaging). From concept development and feasibility assessment through algorithm development, training, validation, and deployment support. Includes data curation, annotation support, and regulatory-ready validation. |
• Trained/optimized models • Validation reports • Performance benchmarks • Integration guidance • Deployment documentation |
4-6 weeks |
| AI Bias & Fairness Audits | Independent, rigorous assessment of AI system fairness across patient populations. Includes demographic performance analysis, intersectionality analysis, root cause investigation, and actionable mitigation strategies. Uses XAI tools (SHAP, LIME) and fairness frameworks (IEEE P7003, EU AI Act). |
• Comprehensive bias assessment report • Subgroup performance metrics • Root cause analysis • Mitigation strategies • Ongoing monitoring framework |
|
| Clinical AI Validation | Full-spectrum clinical validation support from study design through execution and regulatory documentation. Includes retrospective validation studies, prospective observational studies, randomized controlled trials (RCTs), and reader studies. |
• Study protocol & SAP • Statistical analysis reports • Clinical evaluation report (CER) • Regulatory documentation • Publication-ready results |
Varies by study type |
| AI Readiness Review | Structured evaluation of AI development pipelines and technical documentation for MDR/AI Act compliance. Comprehensive assessment of traceability, software versioning, model lifecycle management, and documentation practices. Provides clear roadmap for regulatory preparedness. |
• Detailed evaluation report • Gap analysis • Prioritized improvement roadmap • MDR/AI Act compliance checklist • Documentation templates |
1-3 weeks |
| Wearables & Digital Health Evaluation | Technical and functional assessment of wearable systems and digital health technologies. End-to-end evaluation from sensor design to data acquisition, transfer, and analysis pipelines. Assesses feasibility, usability, data integrity, and deployment readiness. |
• Technical validation report • Performance benchmarks • Usability assessment • Data pipeline analysis • Deployment recommendations |
3-5 weeks |
| Regulatory Strategy & MDR Guidance | Expert consultation on regulatory pathways for AI medical devices. Comprehensive support including device classification, intended use definition, technical documentation audits, clinical evaluation design, and CE marking preparation. Aligned with EU MDR 2017/745, ISO 13485, ISO 14971, and IEC 62304. |
• Regulatory roadmap • Device classification guidance • MDR requirements checklist • Documentation templates • Gap analysis |
1-6 weeks |
| Data Analytics & Process Mining | Healthcare data analysis, clinical pathway optimization, and process mining with AI-powered insights. | • Analytics reports • Process maps • Bottleneck analysis • AI-powered insights • Optimization recommendations |
Varies by scope |
| Infrastructure & Technical Consulting | Cloud architecture design, compute infrastructure setup, MLOps pipelines, and DevOps support for healthcare AI systems. Includes model deployment, monitoring systems, CI/CD pipelines, and scalability optimization. Focus on secure, compliant, and maintainable infrastructure. |
• Architecture design • Infrastructure setup • Deployment pipelines • Monitoring systems • Maintenance playbooks |
Varies by scope |
| Client Type | Typical Services and Focus | Access Route and Contact |
|---|---|---|
| Academic Researchers | Method development and validation, collaboration on research projects, access to specialized infrastructure, co-authorship opportunities, student training. | Request through iLab |
| Startups & SMEs | Full AI development pipeline from concept to validation, regulatory guidance and MDR compliance, bias audits, clinical trial design, investment-ready evidence. | TEF-Health preferred: tef-health@ki.se (Subsidized rates available) |
| Established Companies | Product validation and benchmarking, independent bias audits, post-market surveillance, algorithm optimization, regulatory strategy for EU/US markets. | Contact Us |
| Hours | Location |
|
Public hours on GPU Services support: Public hours for new team signups: |
Neo floor 7 (room 7840) |
Note: until further notice, in-person support is available with a previous appointment.
| Name | Role | Phone | Location | |
|---|---|---|---|---|
| Farhad Abtahi |
Manager
|
(0) 85 2483 801
|
farhad.abtahi@ki.se
|
Huddinge
|
| Service list |
| ► User Onboarding (1) | |||
| Name | Description | Price | |
|---|---|---|---|
| User registeration |
For each user using the server/year |
Inquire | |
