Medical Sciences

Multi-Sigma empowers researchers, clinicians, and medical device developers to harness the power of AI for faster discovery, personalized treatment strategies, and more efficient experimentation. From predictive modeling in clinical research to optimizing medical device performance, our platform helps teams generate insights with limited datasets.

Built for numerical data from patient records, clinical trials, or computational simulations, Multi-Sigma enables high accuracy prediction, contribution analysis, and multi-objective optimization without coding or expertise in AI.

Recent applications include:

  • Personalized Chronic Pain Treatment
    Multi-Sigma was used to model chronic low back pain outcomes from real world patient data. It was able to predict pain relief levels based on individual profiles, and it proposed optimized, personalized treatment plans. Contribution analysis identified both positive and negative factors influencing outcomes such as sleep quality, age, and initial pain levels.

  • Sleep Behavior and Cognitive Function Modeling
    Using Multi-Sigma’s AI Chain Analysis, researchers linked multiple models to study how sleep-related behaviors affect fatigue and concentration. By chaining predictions across sleep duration, caffeine intake, exercise, and environmental variables, it was able to identify optimal conditions for cognitive performance.

  • Medical Device Optimization: Artificial Heart Design
    In the development of a centrifugal blood pump, Multi-Sigma reduced simulation workload by over 99% by training a surrogate model from just 50 CFD simulations. The platform predicted thrust force and hemolysis index with high accuracy, revealed that groove depth was more critical than groove count, and produced a Pareto curve to identify the optimal design tradeoffs between mechanical performance and blood cell safety.

  • Early Risk Detection for Alzheimer’s Disease
    Leveraging clinical and brain imaging data, Multi-Sigma’s neural network achieved 96% classification accuracy in identifying dementia risk. Contribution analysis revealed key predictive features which guided early intervention strategies.

Multi-Sigma is designed for the challenges of medical R&D:

  • Surrogate Modeling to replace expensive or time intensive simulations

  • Contribution Analysis to uncover meaningful clinical or physiological variables

  • Multi-Objective Optimization for balancing efficacy, safety, and feasibility

  • Chain Modeling for understanding sequential effects

  • Cloud Based and Code-Free for fast integration into healthcare workflows

From clinical data modeling to biomedical engineering, Multi-Sigma helps medical R&D teams accelerate discovery while supporting transparency.

Let us show you how AI can support your research in medicine and health science. Contact us to set up a demonstration.