POMICELL: Modeling Medicine with Transparent Intelligence
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Project Highlights
Project Highlights POMICELL merges scientific Big Data with individual health records to create personalized disease models. Unlike traditional AI systems, POMICELL is built around explainability. Every prediction can be traced back to data and logic, helping clinicians, researchers, and regulators trust the output. SLM Software joined the mission as a technical partner, delivering the integrations, pipelines, and visualization tools that turned this idea into a working platform.
The Challenge
The medical field generates more knowledge than any doctor can absorb: thousands of new studies come out daily. Clinicians face two problems:
- Information overload
- Opaque AI systems (existing models often act as “black boxes,” making them difficult for professionals to trust).
POMICELL set out to solve both by creating explainable AI disease models that integrate up-to-date research with patient data.
Our Approach
SLM Software needed to build the backbone of POMICELL’s data and AI systems. We focused on creating pipelines, fusing scientific and clinical data, and ensuring every output could be explained clearly.
Scientific Data Pipelines
Built NLP systems to continuously add and parse thousands of publications from open-access databases.
Data Fusion Layer
Developed processes to merge patient medical records with biosimulation and network biology insights.
Predictive AI Models
Implemented hybrid modeling approaches, from decision trees to deep neural networks, tuned for personalized disease prediction and treatment guidance.
Explainable AI Engine
Designed a white-box framework that translates model logic into visual decision trees, graphs, and traceable outputs clinicians can trust.
Visualization Tools
Delivered interactive dashboards for researchers and doctors, including protein structures, disease pathways, and treatment scenarios.
Agile Delivery
Rolled out features quarterly, starting with NLP pipelines and ending with full visualization and explainability layers.
The Impact
POMICELL is transforming how researchers and clinicians model diseases. By making predictions explainable and science-driven, it bridges the gap between raw data and clinical decision-making. Performance Results:
- 4,000+ research articles processed monthly
- 95% of AI predictions explainable via transparent logic
- 60% faster hypothesis generation for biomedical researchers
- 87% researcher satisfaction score
- Easier regulatory adoption thanks to explainability-first design
What We Learned
● In medicine, explainability is non-negotiable: predictions must be transparent to earn adoption. ● Integrating real-time research with patient data opens new horizons in precision medicine. ● When AI is understood, it’s embraced by clinicians and regulators alike. ● Agile delivery works when built on modular pipelines.
Partner With SLM Software
We help various companies turn data complexity into clarity. From NLP pipelines to explainable AI models, our team builds solutions that deliver both insight and trust. Ready to build the next generation of AI? Get in touch with us!
