The Digitalization of Neurology - Volume II

This Research Topic is Volume II of a series. The previous volume can be found here:

The Digitalization of Neurology

In recent decades, the field of medicine, particularly neurology, has experienced a significant digital transformation. Traditional qualitative practices in neurology are increasingly being supplemented or replaced by quantitative approaches enabled by digital advancements. The conversion of neurological data from free text to a formatted, computable language has facilitated the deployment of digital tools that enhance diagnosis and prognosis in neurology. These innovations offer the potential for heightened precision and insight. However, while digitalization has advanced the field significantly, there are still multiple questions and challenges that remain, such as ensuring the seamless integration and interpretation of data, maintaining patient privacy, and addressing biases within computational models.

This Research Topic aims to explore how digitization has revolutionized the field of neurology, particularly through the application of digital tools and technologies. The primary goal is to highlight the transformative impact that these advancements have had on understanding, diagnosing, and treating neurological diseases. We aim to delve into specific areas such as phenotyping, ontology development, and the expansion of electronic health records, as well as elucidate the role of machine learning, natural language processing, and telemedicine. These inquiries intend to understand better the mechanisms underlying neurological conditions and the opportunities digital tools present in addressing them.

To gather further insights in the digital evolution of neurological practices, we welcome articles addressing, but not limited to, the following themes:

- High throughput phenotyping and phenotype repositories for neurological diagnosis
- Ontology-based frameworks for neurological conditions
- Machine learning methodologies to enhance neurological insights
- Development of neurological-specific biobanks for enhanced research capabilities
- Application of natural language processing in electronic health records and textual databases
- Network analysis and graph theory for novel neurological insights
- Big data and computational models illuminating neurological disease dynamics
- Teleneurology's impact on patient-centered care models
- Telemetry and remote monitoring innovations for neurological conditions
- Leveraging electronic health records for disease diagnosis advancements
- Exploration of Large Language Models in neurological research contexts.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Clinical Trial
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Clinical Trial
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: big data, Neurology, electronic health records, computational neurology, teleneurology, telemetry, network analysis, phenotyping, ontologies, biobanks, natural language processing

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.