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Erschienen in:

17.02.2024 | Review

Artificial intelligence in neurology: opportunities, challenges, and policy implications

verfasst von: Sebastian Voigtlaender, Johannes Pawelczyk, Mario Geiger, Eugene J. Vaios, Philipp Karschnia, Merit Cudkowicz, Jorg Dietrich, Ira R. J. Hebold Haraldsen, Valery Feigin, Mayowa Owolabi, Tara L. White, Paweł Świeboda, Nita Farahany, Vivek Natarajan, Sebastian F. Winter

Erschienen in: Journal of Neurology | Ausgabe 5/2024

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Abstract

Neurological conditions are the leading cause of disability and mortality combined, demanding innovative, scalable, and sustainable solutions. Brain health has become a global priority with adoption of the World Health Organization’s Intersectoral Global Action Plan in 2022. Simultaneously, rapid advancements in artificial intelligence (AI) are revolutionizing neurological research and practice. This scoping review of 66 original articles explores the value of AI in neurology and brain health, systematizing the landscape for emergent clinical opportunities and future trends across the care trajectory: prevention, risk stratification, early detection, diagnosis, management, and rehabilitation. AI’s potential to advance personalized precision neurology and global brain health directives hinges on resolving core challenges across four pillars—models, data, feasibility/equity, and regulation/innovation—through concerted pursuit of targeted recommendations. Paramount actions include swift, ethical, equity-focused integration of novel technologies into clinical workflows, mitigating data-related issues, counteracting digital inequity gaps, and establishing robust governance frameworks balancing safety and innovation.

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AUC

A measure of a model’s performance, often utilized in binary classification problems. AUC quantifies the total area under the receiver operating characteristic (ROC) curve, with a value of 1 indicating perfect prediction and a value of 0.5 indicating random chance.

Brain Health

The state of brain functioning across cognitive, sensory, social-emotional, behavioral and motor domains, allowing a person to realize their full potential over the life course, irrespective of the presence or absence of disorders (WHO definition).

Cognitive liberty

A right to access and change one’s brain, making advances in brain health crucial to securing cognitive liberty to individuals, and a right from interference with mental privacy and freedom of thought. The concept provides an important framework to ensure ethical innovation of advances to improve brain health.

Conditional average treatment effect

The difference between the expected response under control and the response under treatment.

Contrastive learning

A learning framework that trains models to distinguish between similar (positive) and dissimilar (negative) pairs of data samples, enhancing the capability of models to learn robust and discriminative features.

Epigenome

The ensemble of modifications to DNA and DNA-associated proteins that signal and regulate gene expression and other DNA-related processes.

European health data space

A health specific ecosystem comprised of rules, common standards and practices, infrastructures and a governance framework.

Federated learning

A privacy-preserving machine learning approach where a model is trained across multiple decentralized devices or servers, using local data samples without sharing them.

Foundation model

Large-scale artificial neural networks trained on vast, diverse, multimodal datasets that have shown the capability to excel at a variety of tasks by additional fine-tuning, or to flexibly adapt to novel tasks via text instructions or examples (‘in-context learning’) without additional training, enabling fine-grained control over model outputs. ‘Small FMs’ are FMs with fewer parameters than state-of-the-art large FMs .

Genome-wide association studies

An observational study of a genome-wide set of genetic variants in different individuals to see if any variant is associated with a trait. GWASs typically focus on associations between single-nucleotide polymorphisms (SNPs) and traits like major human diseases.

Inductive bias

The set of assumptions or a priori information that a machine learning model or algorithm uses for prediction, e.g., physical, geometric, or causal.

MR fingerprinting

The recording of multiple MR sequences with pseudorandomized acquisition parameters, combined with computational matching of resulting “fingerprints” to a predefined dictionary of predicted signal evolutions

Random forest

A machine learning technique that constructs multiple decision trees during training and outputs the mode of their predictions for classification or the mean for regression, enhancing prediction accuracy and reducing overfitting.

Stimulated Raman histology

Non-invasive, label-free histological imaging, based on stimulated Raman spectroscopy.

Support vector machine

A supervised learning model that identifies the optimal hyperplane in a high-dimensional space to segregate different classes, maximizing the margin between data points of different categories.

XGBoost

An optimized distributed gradient boosting library that enhances the efficiency, accuracy, and scalability of machine learning models, particularly in tree boosting methods.

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Titel: Artificial intelligence in neurology: opportunities, challenges, and policy implications
verfasst von: Sebastian Voigtlaender
Johannes Pawelczyk
Mario Geiger
Eugene J. Vaios
Philipp Karschnia
Merit Cudkowicz
Jorg Dietrich
Ira R. J. Hebold Haraldsen
Valery Feigin
Mayowa Owolabi
Tara L. White
Paweł Świeboda
Nita Farahany
Vivek Natarajan
Sebastian F. Winter
Publikationsdatum: 17.02.2024
Verlag: Springer Berlin Heidelberg
Erschienen in: Journal of Neurology / Ausgabe 5/2024
Print ISSN: 0340-5354
Elektronische ISSN: 1432-1459
DOI: https://doi.org/10.1007/s00415-024-12220-8

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