Evidence map›Paper›PMID 38976174›Full record

ReviewCurrent pain and headache reports2024

Application of Artificial Intelligence in the Headache Field.

Keiko Ihara, Gina Dumkrieger, Pengfei Zhang, Tsubasa Takizawa, Todd J Schwedt, Chia-Chun Chiang

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current pain and headache reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Review
  6. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Keiko IharaDepartment of Neurology, Keio University School of Medicine, Shinjuku, Tokyo, Japan.ORCID http://orcid.org/0000-0001-5644-0468
Gina DumkriegerDepartment of Neurology, Mayo Clinic, Scottsdale, AZ, USA.ORCID http://orcid.org/0000-0001-9519-5370
Pengfei ZhangDepartment of Neurology, Rutgers University, New Brunswick, NJ, USA.ORCID http://orcid.org/0000-0003-1132-1937
Tsubasa TakizawaDepartment of Neurology, Keio University School of Medicine, Shinjuku, Tokyo, Japan.ORCID http://orcid.org/0000-0003-2605-7200
Todd J SchwedtDepartment of Neurology, Mayo Clinic, Scottsdale, AZ, USA.ORCID http://orcid.org/0000-0002-7780-7086
Chia-Chun ChiangDepartment of Neurology, Mayo Clinic, Rochester, MN, USA. Chiang.Chia-Chun@mayo.edu.ORCID http://orcid.org/0000-0001-7802-7172

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purpose of reviewHeadache 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

findingsWe 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. 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.

Indexed as

Artificial IntelligenceHeadacheHumansMachine LearningArtificial intelligenceChatGPTHeadacheLarge language modelMachine learningMigraine

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.