Efficient Patient Engagement
Self-Supervised Multi-Modal Framework for Stroke Risk Prediction
Early identification of stroke in individuals is essential to prevent neurological damage, reducing the likelihood of long-term disability, and improving patient outcomes. The multifaceted nature of stroke, driven by numerous interrelated risk factors, underscores the need for integrated approaches that combine diverse data sources to enhance predictive accuracy and inform proactive treatment planning.
Magnetic Resonance Imaging (MRI) offers a distinctive advantage in detecting brain biomarkers that may signal future stroke events. Its non-invasive, high-resolution imaging capabilities enable accurate evaluation of structural abnormalities and detailed mapping of the brain’s vascular system. These insights provide clinicians with a clearer understanding of the underlying conditions that contribute to elevated stroke risk.
Intractābilis has developed a scalable, self-supervised, multi-modal framework that unites research-grade MRI data with clinical records to predict stroke risk prior to onset. This methodology leverages cross-modal interactions and image-to-tabular alignment, integrating data into a unified analytical space that strengthens representation quality and improves the performance of downstream predictive tasks. Importantly, the model operates effectively on small datasets without requiring expert annotations, making it suitable for real-world clinical deployment. By enabling earlier identification of high-risk patients, it supports more proactive and personalised management strategies.
Multimodal Clinical Decision Support
Diagnostic errors, often resulting from cognitive biases and errors in clinical judgement lead to permanent disability and death of patients. These mistakes impose a considerable burden on healthcare systems, both in terms of patient outcomes and economic impact.
Intractābilis has developed an advanced medical agent to support diagnostic decision-making. By addressing cognitive biases and improving clinical efficiency, the system not only assists healthcare professionals but also empowers patients, ultimately enhancing the quality and accuracy of medical decisions. The framework integrates Retrieval-Augmented Generation (RAG) with an extensive diagnostic knowledge graph, enabling more precise reasoning and tailored treatment recommendations grounded in structured, inferable medical data. It securely processes privacy-sensitive Electronic Health Records (EHRs) to help clinicians reduce the risk of misdiagnosis. This approach strengthens RAG’s reasoning capabilities, allowing it to detect subtle diagnostic distinctions and generate targeted follow-up questions to clarify ambiguous or incomplete patient information.
Through this integration, the system delivers highly specific diagnostic insights alongside personalised treatment and medication recommendations. It has demonstrated strong adaptability across multiple large language models and has proven particularly effective in producing reasoning-driven follow-up questions when faced with insufficient data or uncertain diagnoses.
Intractābilis delivers an unmatched breadth of patient engagement capabilities across clinical, public health, and consumer healthcare domains.
By combining Cognitive AI with domain specificity and privacy-aware deployment, we help healthcare institutions, technology platforms, and public agencies engage patients meaningfully—at every stage of patient care.
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