– Written by Dr. Ilaria Lavagi Sr. Clinical data scientist at Roche Diagnostics

Introduction

Neurological diseases such as Alzheimer’s, Parkinson’s and multiple sclerosis are among the most complex and varied conditions in medicine. Even within the same diagnosis, patients can experience vastly different symptoms, disease progression and treatment responses. Early signs often remain subtle – such as minor changes in memory or movement – and can easily be overlooked.

This is where artificial intelligence (AI) steps in. Traditional diagnostic approaches often rely on linear models and struggle to integrate the diverse type of data (i.e. multimodal data) associated with these conditions. AI, however, excels at processing complex datasets and identifying nonlinear patterns, enabling the detection of subtle biomarkers and early indicators that might elude conventional methods. However, the effectiveness of AI is highly contingent upon the quality and diversity of the underlying data, which presents considerable challenges – particularly in safeguarding data privacy and ensuring consistently high data quality.

From Raw Data to Ready Insights

Neurology presents a unique data challenge, involving multimodal sources such as imaging, lab results, and genetic data. Integrating these into a coherent, usable dataset requires robust data management and curation. Clinical data professionals, data engineers and research analysts play a central role in collecting, cleaning and organising this information to ensure it is reliable and analysis-ready.

To support this process, the OMOP (Observational Medical Outcomes Partnership) common data model is widely used by research institutions, academic consortia and pharmaceutical companies. OMOP provides a shared structure to standardise diverse healthcare data – such as electronic health records, claims, and registry data – into a single, analysable format.

This harmonisation allows teams across organisations to collaborate more efficiently, run reproducible analyses, and scale AI models across compatible datasets. In short, the data format goal is analytics-ready standardisation. Aligned with the FAIR (Findable, Accessible, Interoperable, and Reusable) principles, it ensures curated datasets can be reused for multiple research questions, ultimately accelerating discovery in complex fields such as neurology.

Federated Learning for Global Collaboration

One of the most promising techniques for applying AI to sensitive health data is federated learning. It enables collaboration across hospitals and research institutions worldwide by training models on local data – such as neuroimaging, genetic information and clinical outcomes – without moving the data itself. This enhances privacy and unlocks access to a more diverse dataset, which in turn leads to better diagnostic models and personalised treatment strategies.

Federated learning can also streamline clinical trials and generate real-world evidence more efficiently. However, its success hinges on the harmonisation of data formats, metadata standards and ontologies across institutions. To ensure secure and consistent collaboration, strong data curation and interoperability frameworks – such as FHIR (Fast Healthcare Interoperability Resources) – are crucial. FHIR is a modern standard for exchanging healthcare data that supports seamless integration across diverse datasets. It ensures data is consistently formatted, accurate and privacy-compliant, making it an essential tool in federated learning for sharing critical data securely across institutions and enabling AI-driven insight. Since FHIR’s primary goal is interoperability – not analytics-ready standardisation – additional curation into formats such as OMOP can further enhance results by enabling deeper, harmonised analysis across sites.

Detecting Patterns and Stratifying Patients

AI is already making a difference in neurology by detecting patterns and uncovering disease subtypes.

For instance, deep learning models trained on MRI data have identified early signs of Alzheimer’s (detecting pattern) – such as hippocampal atrophy – years before clinical diagnosis (Li et al., 2020). In Parkinson’s, AI has found speech and motor changes via wearables and voice analysis long before formal diagnosis (Shen et al., 2024).

Beyond early detection, AI supports patient stratification. Machine learning has revealed Parkinson’s subtypes – like tremor-dominant versus gait-difficulty profiles – that differ in progression and treatment response (Dadu et al., 2024). These insights inform more personalised treatments and smarter trial designs.

Digital Twins and Synthetic Data

As the field progresses, two emerging technologies are adding new dimensions: digital twins and synthetic data.

Digital twins are virtual patient models generated by integrating longitudinal data from multiple sources – such as brain MRI, lab values, genetic markers, electronic health records, and data from wearable devices. Using machine learning, this data is combined to simulate the biological and clinical behaviour of an individual over time. In neurology, this allows clinicians to test how a specific patient might respond to different treatments – for example, estimating whether starting a certain medication now might delay cognitive decline, or predicting mobility loss under care plans. By simulating “what-if” scenarios in silico, digital twins support earlier, more personalised interventions and reduce the need for trial and error in real life.

Synthetic data, on the other hand, enables AI training and validation without exposing real patient information. This is particularly useful in neurology, where data is both sensitive and heterogeneous. Synthetic datasets can expand training cohorts, improve model robustness, and enable data sharing while preserving privacy.

Both approaches rely on standardised, high-quality input, again emphasising the foundational importance of good data curation and interoperability.

Next Steps

Looking ahead, a promising strategy involves integrating federated learning with digital twin technologies to advance personalised neurological care. Federated learning enables decentralised model training across institutions, ensuring sensitive patient data remains local and private. Once the model has been trained, digital twins can be created. Digital twins will simulate disease progression and treatment responses, allowing clinicians to test therapeutic scenarios in a virtual environment before applying them in real life.

The combination of these approaches offers advantages such as enhanced model accuracy, privacy preservation and personalised simulations.

Implementing these systems requires robust data harmonisation frameworks, such as OMOP and FHIR, to ensure interoperability and data quality across diverse sources. Embracing this integrated approach can transform neurological care, making it more predictive, personalised and secure.

Conclusion

AI is revolutionising neurology by enabling early detection, personalised treatment and disease subtyping. However, its success depends on high-quality, standardised data. Frameworks such as OMOP and FHIR ensure data harmonisation, while digital twins and synthetic data support personalised care and privacy. Federated learning promotes inclusive, privacy-preserving AI collaboration. To unlock AI’s full potential in neurology, strong data governance, ethical considerations and patient engagement are essential.

NOTE: This blog post focuses on research data interoperability and AI-driven insights into neurology, where frameworks such as OMOP and FHIR are applicable. Therefore, submission-specific standards such as SDTM and ADaM fall outside the scope of this discussion.