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Artificial intelligence and headache.

Anker Stubberud^1,2, Helge Langseth^1,3, Parashkev Nachev⁴

¹NorHead Norwegian Centre for Headache Research, Trondheim, Norway.

Cephalalgia : an International Journal of Headache

|August 5, 2024

Summary

This summary is machine-generated.

Artificial intelligence (AI) and machine learning (ML) offer broad applications in headache research, including data analysis, diagnosis, and personalized treatment. However, current studies require improved reporting and clinical validation for AI and ML in headache management.

Keywords:

decision-support machine learning migraine prediction tension-type headache trigeminal autonomic cephalalgia

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Area of Science:

Neurology
Computer Science
Medical Informatics

Background:

Artificial intelligence (AI) and machine learning (ML) are increasingly relevant in medical research.
Headache clinicians and researchers require an understanding of these technologies.

Purpose of the Study:

To introduce key AI and ML concepts relevant to headache.
To review the current applications of AI and ML in headache research.
To discuss the limitations and future perspectives of AI and ML in headache.

Main Methods:

Narrative review methodology.
Comprehensive literature search across PubMed, Embase, and IEEExplore.
Synthesis of findings on AI and ML applications in headache.

Main Results:

Natural Language Processing (NLP) for extracting headache data.
ML for classifying headache disorders with high accuracy (60-90%).
ML for predicting disease trajectories, headache forecasting, and treatment optimization.

Conclusions:

AI and ML have extensive potential in headache management.
Current research often lacks robust reporting and clinical validation.
Further development and validation are needed for real-world clinical application.