TY - JOUR
T1 - Application of Artificial Intelligence in the Headache Field
AU - Ihara, Keiko
AU - Dumkrieger, Gina
AU - Zhang, Pengfei
AU - Takizawa, Tsubasa
AU - Schwedt, Todd J.
AU - Chiang, Chia Chun
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
PY - 2024/10
Y1 - 2024/10
N2 - Purpose of Review: Headache disorders are highly prevalent worldwide. Rapidly advancing capabilities in artificial intelligence (AI) have expanded headache-related research with the potential to solve unmet needs in the headache field. We provide an overview of AI in headache research in this article. Recent Findings: We briefly introduce machine learning models and commonly used evaluation metrics. We then review studies that have utilized AI in the field to advance diagnostic accuracy and classification, predict treatment responses, gather insights from various data sources, and forecast migraine attacks. Furthermore, given the emergence of ChatGPT, a type of large language model (LLM), and the popularity it has gained, we also discuss how LLMs could be used to advance the field. Finally, we discuss the potential pitfalls, bias, and future directions of employing AI in headache medicine. Summary: Many recent studies on headache medicine incorporated machine learning, generative AI and LLMs. A comprehensive understanding of potential pitfalls and biases is crucial to using these novel techniques with minimum harm. When used appropriately, AI has the potential to revolutionize headache medicine.
AB - Purpose of Review: Headache disorders are highly prevalent worldwide. Rapidly advancing capabilities in artificial intelligence (AI) have expanded headache-related research with the potential to solve unmet needs in the headache field. We provide an overview of AI in headache research in this article. Recent Findings: We briefly introduce machine learning models and commonly used evaluation metrics. We then review studies that have utilized AI in the field to advance diagnostic accuracy and classification, predict treatment responses, gather insights from various data sources, and forecast migraine attacks. Furthermore, given the emergence of ChatGPT, a type of large language model (LLM), and the popularity it has gained, we also discuss how LLMs could be used to advance the field. Finally, we discuss the potential pitfalls, bias, and future directions of employing AI in headache medicine. Summary: Many recent studies on headache medicine incorporated machine learning, generative AI and LLMs. A comprehensive understanding of potential pitfalls and biases is crucial to using these novel techniques with minimum harm. When used appropriately, AI has the potential to revolutionize headache medicine.
KW - Artificial intelligence
KW - ChatGPT
KW - Headache
KW - Large language model
KW - Machine learning
KW - Migraine
UR - https://www.scopus.com/pages/publications/85197736922
UR - https://www.scopus.com/pages/publications/85197736922#tab=citedBy
U2 - 10.1007/s11916-024-01297-5
DO - 10.1007/s11916-024-01297-5
M3 - Article
C2 - 38976174
AN - SCOPUS:85197736922
SN - 1531-3433
VL - 28
SP - 1049
EP - 1057
JO - Current Pain and Headache Reports
JF - Current Pain and Headache Reports
IS - 10
ER -