Aima Diagnostics is a research-driven medical AI platform focused on advancing the science of laboratory data interpretation, longitudinal biomarker analysis, and clinical decision support.
Scientific Collaboration & Research
Scientific Methodology & Data Ethics
Aima Diagnostics operates under a strict scientific and ethical framework:

  • Use of de-identified, real-world medical data only;
  • Full compliance with GDPR and international data protection standards;
  • Emphasis on statistical validity, reproducibility, and clinical relevance;
  • Continuous model refinement in collaboration with clinical experts;
  • Clear distinction between analytical interpretation and medical diagnosis.


Transparency, peer review, and methodological rigor are central to all research collaborations.
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Our Philosophy
Who We Collaborate
 function.        Function
With
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  • Physicians and clinical experts
  • Medical and clinical researchers
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  • Universities and medical schools
  • Research institutes and scientific centers
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  • Diagnostic laboratories and laboratory networks
  • Digital health and medical AI research groups
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published
Publications & Research Output
Aima Diagnostics supports and contributes to:

  • peer-reviewed scientific publications,
  • white papers and technical reports,
  • conference presentations and posters,
  • applied research and real-world evidence studies.


Selected publications and ongoing research initiatives will be published on this page.
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our team
A collaboration of architects, light artists, and engineers who believe glasses can be more than just optics.
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welcome scientific collaboration in the following research areas:
Areas of Scientific Collaboration
Go to Blog
AI-Based Interpretation of Laboratory Data
Multivariate and longitudinal analysis of biochemical and hematological markers beyond static reference ranges.
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Clinical Decision Support Systems (CDSS)
Research and validation of AI systems that support physician decision-making while preserving clinical autonomy.
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Personalized & Context-Aware Interpretation
Population-specific and individualized modeling incorporating age, sex, ethnicity, geography, medication use, chronic conditions, and lifestyle factors.
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Areas of Scientific Collaboration
Go to Blog
Longitudinal Health Analytics
Trend analysis, early risk signal detection, and preclinical pattern identification over time.
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Explainability, Bias & Robustness in Medical AI
Improving transparency, interpretability, and fairness of AI models in clinical contexts.
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Translational & Applied Research
Bridging academic research with real-world clinical workflows and laboratory environments.
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