Influence of next-generation artificial intelligence on headache research, diagnosis and treatment: the junior editorial board members' vision - part 1.
Augmented reality
Digital applications
Ethical AI regulations
Machine learning
Migraine
Virtual health assistants
Journal
The journal of headache and pain
ISSN: 1129-2377
Titre abrégé: J Headache Pain
Pays: England
ID NLM: 100940562
Informations de publication
Date de publication:
13 Sep 2024
13 Sep 2024
Historique:
received:
05
07
2024
accepted:
18
08
2024
medline:
14
9
2024
pubmed:
14
9
2024
entrez:
13
9
2024
Statut:
epublish
Résumé
Artificial intelligence (AI) is revolutionizing the field of biomedical research and treatment, leveraging machine learning (ML) and advanced algorithms to analyze extensive health and medical data more efficiently. In headache disorders, particularly migraine, AI has shown promising potential in various applications, such as understanding disease mechanisms and predicting patient responses to therapies. Implementing next-generation AI in headache research and treatment could transform the field by providing precision treatments and augmenting clinical practice, thereby improving patient and public health outcomes and reducing clinician workload. AI-powered tools, such as large language models, could facilitate automated clinical notes and faster identification of effective drug combinations in headache patients, reducing cognitive burdens and physician burnout. AI diagnostic models also could enhance diagnostic accuracy for non-headache specialists, making headache management more accessible in general medical practice. Furthermore, virtual health assistants, digital applications, and wearable devices are pivotal in migraine management, enabling symptom tracking, trigger identification, and preventive measures. AI tools also could offer stress management and pain relief solutions to headache patients through digital applications. However, considerations such as technology literacy, compatibility, privacy, and regulatory standards must be adequately addressed. Overall, AI-driven advancements in headache management hold significant potential for enhancing patient care, clinical practice and research, which should encourage the headache community to adopt AI innovations.
Identifiants
pubmed: 39272003
doi: 10.1186/s10194-024-01847-7
pii: 10.1186/s10194-024-01847-7
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
151Informations de copyright
© 2024. The Author(s).
Références
Russell S, Norvig P, Artificial Intelligence (2020) A Modern Approach (4th ed.). Pearson. pp. 1–34
Park C-W, Seo SW, Kang N, Ko BS, Choi BW, Park CM et al (2020) Artificial Intelligence in Health Care: current applications and issues. J Korean Med Sci 35:e379. https://doi.org/10.3346/jkms.2020.35.e379
doi: 10.3346/jkms.2020.35.e379
pubmed: 33140591
pmcid: 7606883
Thakur A, Mishra AP, Panda B, Rodríguez DCS, Gaurav I, Majhi B (2020) Application of Artificial Intelligence in Pharmaceutical and Biomedical studies. Curr Pharm Des 26:3569–3578. https://doi.org/10.2174/1381612826666200515131245
doi: 10.2174/1381612826666200515131245
pubmed: 32410553
Tso AR, Brudfors M, Danno D, Grangeon L, Cheema S, Matharu M, Nachev P (2021) Machine phenotyping of cluster headache and its response to verapamil. Brain 144:655–664. https://doi.org/10.1093/brain/awaa388
doi: 10.1093/brain/awaa388
pubmed: 33230532
Ganesh GS, Kolusu AS, Prasad K, Samudrala PK, Nemmani KVS (2022) Advancing health care via artificial intelligence: from concept to clinic. Eur J Pharmacol 934:175320. https://doi.org/10.1016/j.ejphar.2022.175320
doi: 10.1016/j.ejphar.2022.175320
Bajwa J, Munir U, Nori A, Williams B (2021) Artificial intelligence in healthcare: transforming the practice of medicine. Future Healthc J 8:e188–e194. https://doi.org/10.7861/fhj.2021-0095
doi: 10.7861/fhj.2021-0095
pubmed: 34286183
pmcid: 8285156
Davenport T, Kalakota R (2019) The potential for artificial intelligence in healthcare. Future Healthc J 6:94–98. https://doi.org/10.7861/futurehosp.6-2-94
doi: 10.7861/futurehosp.6-2-94
pubmed: 31363513
pmcid: 6616181
Cohen F (2023) The role of artificial intelligence in headache medicine: potential and peril. Headache 63:694–696. https://doi.org/10.1111/head.14495
doi: 10.1111/head.14495
pubmed: 37171282
Messina R, Filippi M (2020) What we Gain from Machine Learning studies in Headache patients. Front Neurol 11:221. https://doi.org/10.3389/fneur.2020.00221
doi: 10.3389/fneur.2020.00221
pubmed: 32328022
pmcid: 7161430
Torrente A, Maccora S, Prinzi F, Alonge P, Pilati L, Lupica A et al (2024) The clinical relevance of Artificial Intelligence in Migraine. Brain Sci 14:85. https://doi.org/10.3390/brainsci14010085
doi: 10.3390/brainsci14010085
pubmed: 38248300
pmcid: 10813497
Singareddy S, Prabhu SNV, Jaramillo AP, Yasir M, Iyer N, Hussein S, Nath TS (2023) Artificial Intelligence and its role in the management of Chronic Medical conditions. Syst Rev Cureus 15:e46066. https://doi.org/10.7759/cureus.46066
doi: 10.7759/cureus.46066
Vandenbussche N, Hee CV, Hoste V, Paemeleire K (2022) Using natural language processing to automatically classify written self-reported narratives by patients with migraine or cluster headache. J Headache Pain 23:129. https://doi.org/10.1186/s10194-022-01490-0
doi: 10.1186/s10194-022-01490-0
pubmed: 36180844
pmcid: 9524092
Katsuki M, Shimazu T, Kikui S, Danno D, Miyahara J, Takeshima R et al (2023) Developing an artificial intelligence-based headache diagnostic model and its utility for non-specialists’ diagnostic accuracy. Cephalalgia 43:3331024231156925. https://doi.org/10.1177/03331024231156925
doi: 10.1177/03331024231156925
pubmed: 37072919
Martelletti P, Leonardi M, Ashina M, Burstein R, Cho SJ, Charway-Felli A et al (2023) Rethinking headache as a global public health case model for reaching the SDG 3 HEALTH by 2030. J Headache Pain 24:140. https://doi.org/10.1186/s10194-023-01666-2
doi: 10.1186/s10194-023-01666-2
pubmed: 37884869
pmcid: 10604921
Aggarwal A, Tam CC, Wu D, Li X, Qiao S (2023) Artificial Intelligence-based chatbots for promoting health behavioral changes: systematic review. J Med Internet Res 25:e40789. https://doi.org/10.2196/40789
doi: 10.2196/40789
pubmed: 36826990
pmcid: 10007007
Minen MT, Jaran J, Boyers T, Corner S (2020) Understanding what people with migraine consider to be important features of migraine tracking: an analysis of the utilization of smartphone-based migraine tracking with a Free‐text feature. Headache: J Head Face Pain 60:1402–1414. https://doi.org/10.1111/head.13851
doi: 10.1111/head.13851
Göbel H, Frank B, Heinze A, Zimmermann W, Göbel C, Göbel A et al (2019) Healthcare behavior of migraine and headache patients when treatment is accompanied by the digital migraine app. Der Schmerz 33:147–155. https://doi.org/10.1007/s00482-018-0355-x
doi: 10.1007/s00482-018-0355-x
pubmed: 30649625
Roesch A, Dahlem MA, Neeb L, Kurth T (2020) Validation of an algorithm for automated classification of migraine and tension-type headache attacks in an electronic headache diary. J Headache Pain 21:1–10. https://doi.org/10.1186/s10194-020-01139-w
doi: 10.1186/s10194-020-01139-w
Stone AA, Shiffman S, Schwartz JE, Broderick JE, Hufford MR (2003) Patient compliance with paper and electronic diaries. Control Clin Trials 24:182–199. https://doi.org/10.1016/s0197-2456(02)00320-3
doi: 10.1016/s0197-2456(02)00320-3
pubmed: 12689739
van Casteren DS, Verhagen IE, de Boer I, de Vries Lentsch S, Fronczek R, van Zwet EW et al (2021) E-diary use in clinical headache practice: a prospective observational study. Cephalalgia 41:1161–1171. https://doi.org/10.1177/03331024211010306
doi: 10.1177/03331024211010306
pubmed: 33938248
pmcid: 8504420
Vo P, Paris N, Bilitou A, Valena T, Fang J, Naujoks C et al (2018) Burden of migraine in Europe using self-reported digital diary data from the migraine buddy© application. Neurol Therapy 7:321–332. https://doi.org/10.1007/s40120-018-0113-0
doi: 10.1007/s40120-018-0113-0
Goadsby PJ, Constantin L, Ebel-Bitoun C, Igracki Turudic I, Hitier S, Amand‐Bourdon C et al (2021) Multinational descriptive analysis of the real‐world burden of headache using the Migraine Buddy application. Eur J Neurol 28:4184–4193. https://doi.org/10.1111/ene.15037
doi: 10.1111/ene.15037
pubmed: 34309986
Hundert AS, Huguet A, McGrath PJ, Stinson JN, Wheaton M (2014) Commercially available mobile phone headache diary apps: a systematic review. JMIR mHealth uHealth 2:e3452. https://doi.org/10.2196/mhealth.3452
doi: 10.2196/mhealth.3452
Jonker L, Fitzgerald L, Vanderpol J, Fisher S (2022) Digital diary app use for migraine in primary care: prospective cohort study. Clin Neurol Neurosurg 216:107225. https://doi.org/10.1016/j.clineuro.2022.107225
doi: 10.1016/j.clineuro.2022.107225
pubmed: 35364371
Vives-Mestres M, Casanova A, Buse DC, Donoghue S, Houle TT, Lipton RB et al (2021) Patterns of perceived stress throughout the migraine cycle: a longitudinal cohort study using daily prospective diary data. Headache: J Head Face Pain 61:90–102. https://doi.org/10.1111/head.13943
doi: 10.1111/head.13943
Choi J-Y, Oh K, Kim B-J, Chung C-S, Koh S-B, Park K-W (2009) Usefulness of a Photophobia Questionnaire in patients with migraine. Cephalalgia 29:953–959. https://doi.org/10.1111/j.1468-2982.2008.01822.x
doi: 10.1111/j.1468-2982.2008.01822.x
pubmed: 19298545
Hoggan RN, Subhash A, Blair S, Digre KB, Baggaley SK, Gordon J et al (2016) Thin-film optical notch filter spectacle coatings for the treatment of migraine and photophobia. J Clin Neurosci 28:71–76. https://doi.org/10.1016/j.jocn.2015.09.024
doi: 10.1016/j.jocn.2015.09.024
pubmed: 26935748
pmcid: 5510464
Noseda R, Bernstein CA, Nir R-R, Lee AJ, Fulton AB, Bertisch SM et al (2016) Migraine photophobia originating in cone-driven retinal pathways. Brain 139:1971–1986. https://doi.org/10.1093/brain/aww119
doi: 10.1093/brain/aww119
pubmed: 27190022
pmcid: 4939697
Posternack C, Kupchak P, Capriolo AI, Katz BJ (2023) Targeting the intrinsically photosensitive retinal ganglion cell to reduce headache pain and light sensitivity in migraine: a randomized double-blind trial. J Clin Neurosci 113:22–31. https://doi.org/10.1016/j.jocn.2023.04.015
doi: 10.1016/j.jocn.2023.04.015
pubmed: 37150129
Minen MT, Adhikari S, Padikkala J, Tasneem S, Bagheri A, Goldberg E et al (2020) Smartphone-delivered progressive muscle relaxation for the treatment of migraine in primary care: a randomized controlled trial. Headache: J Head Face Pain 60:2232–2246. https://doi.org/10.1111/head.14010
doi: 10.1111/head.14010
Chen X, Luo Y (2023) Digital Therapeutics in Migraine Management: a Novel Treatment option in the COVID-19 era. J Pain Res 111–117. https://doi.org/10.2147/JPR.S387548
Dodick DW, Tepper SJ, Lipton RB, Buse DC, Stewart WF, Bayliss M et al (2018) Improving medical communication in migraine management: a modified Delphi study to develop a digital migraine tracker. Headache: J Head Face Pain 58:1358–1372. https://doi.org/10.1111/head.13426
doi: 10.1111/head.13426
Huguet A, Stinson J, MacKay B, Watters C, Tougas M, White M et al (2014) Bringing psychosocial support to headache sufferers using information and communication technology: lessons learned from asking potential users what they want. Pain Res Manage 19:e1–e8. https://doi.org/10.1155/2014/631638
doi: 10.1155/2014/631638
Gulec H, Smahel D (2022) Individual and parental factors of adolescents’ mHealth app use: nationally representative cross-sectional study. JMIR mHealth uHealth 10:e40340. https://doi.org/10.2196/40340
doi: 10.2196/40340
pubmed: 36525286
pmcid: 9804093
Misra S, Lewis TL, Aungst TD (2013) Medical application use and the need for further research and assessment for clinical practice: creation and integration of standards for best practice to alleviate poor application design. JAMA Dermatology 149:661–662. https://doi.org/10.1001/jamadermatol.2013.606
doi: 10.1001/jamadermatol.2013.606
pubmed: 23783150
Biswas M, Tania MH, Kaiser MS, Kabir R, Mahmud M, Kemal AA (2021) ACCU3RATE: a mobile health application rating scale based on user reviews. PLoS ONE 16:e0258050. https://doi.org/10.1371/journal.pone.0258050
doi: 10.1371/journal.pone.0258050
pubmed: 34914718
pmcid: 8675707
Stubberud A, Linde M (2018) Digital technology and mobile health in behavioral migraine therapy: a narrative review. Curr Pain Headache Rep 22:1–6. https://doi.org/10.1007/s11916-018-0718-0
doi: 10.1007/s11916-018-0718-0
Food, Administration D (2019) Policy for device software functions and mobile medical applications. Food and Drug Administration
Harris P, Loveman E, Clegg A, Easton S, Berry N (2015) Systematic review of cognitive behavioural therapy for the management of headaches and migraines in adults. Br J pain 9:213–224. https://doi.org/10.1177/2049463715578291
doi: 10.1177/2049463715578291
pubmed: 26526604
pmcid: 4616982
Aliwi I, Schot V, Carrabba M, Duong P, Shievano S, Caputo M et al (2023) The role of immersive virtual reality and augmented reality in Medical Communication: a scoping review. J Patient Exp 10:23743735231171562. https://doi.org/10.1177/23743735231171562
doi: 10.1177/23743735231171562
pubmed: 37441275
pmcid: 10333997
https://www.fda.gov/medical-devices/digital-health-center-excellence/augmented-reality-and-virtual-reality-medical-devices
Tana C, Mantini C, Cipollone F, Giamberardino MA (2021) Chest imaging of patients with Sarcoidosis and SARS-CoV-2 infection. Current evidence and clinical perspectives. Diagnostics (Basel) 11:183. https://doi.org/10.3390/diagnostics11020183
doi: 10.3390/diagnostics11020183
pubmed: 33514012
https://www.tryhealium.com/healium-for-veterans-affairs/
Cuneo A, Yang R, Zhou H, Wang K, Goh S, Wang Y et al (2023) The utility of a Novel, Combined Biofeedback-virtual reality device as add-on treatment for chronic migraine: a Randomized Pilot Study. Clin J Pain 39:286–296. https://doi.org/10.1097/AJP.0000000000001114
doi: 10.1097/AJP.0000000000001114
pubmed: 37026763
Connelly M, Boorigie M, McCabe K (2023) Acceptability and tolerability of extended reality relaxation training with and without Wearable Neurofeedback in Pediatric Migraine. Child (Basel) 10:329. https://doi.org/10.3390/children10020329
doi: 10.3390/children10020329
Bottiroli S, Matamala-Gomez M, Allena M, Guaschino E, Ghiotto N, De Icco R et al (2022) The virtual enfacement illusion on Pain Perception in patients suffering from chronic migraine: a study protocol for a Randomized Controlled Trial. J Clin Med 11:6876. https://doi.org/10.3390/jcm11226876
doi: 10.3390/jcm11226876
pubmed: 36431353
pmcid: 9699363
Tana C, Raffaelli B, Souza MNP, de la Torre ER, Massi DG, Kisani N et al (2024) Health equity, care access and quality in headache - part 1. J Headache Pain 25:12. https://doi.org/10.1186/s10194-024-01712-7
doi: 10.1186/s10194-024-01712-7
pubmed: 38281917
pmcid: 10823691
https://econsultancy.com/gsk-migraine-simulator-demonstrates-ar-vr-potential-for-healthcare-marketing/
Misztal S, Carbonell G, Zander L, Schild J (2020) Simulating Illness: Experiencing Visual Migraine Impairments in Virtual Reality Conference Proceedings: 2020 IEEE 8th International Conference on Serious Games and Applications for Health (SeGAH), Vancouver, BC, Canada, pp. 1–8. https://doi.org/10.1109/SeGAH49190.2020.9201756
Tana C, Bentivegna E, Cho SJ, Harriott AM, García-Azorín D, Labastida-Ramirez A et al Long COVID headache. J Headache Pain 23:93. https://doi.org/10.1186/s10194-022-01450-8
Tana C, Giamberardino MA, Martelletti P (2023) Long COVID and especially headache syndromes. Curr Opin Neurol 36:168–174. https://doi.org/10.1097/WCO.0000000000001153
doi: 10.1097/WCO.0000000000001153
pubmed: 37078648
Shlobin NA, Baig AA, Waqas M, Patel TR, Dossani RH, Wilson M, Cappuzzo JM, Siddiqui AH, Tutino VM, Levy EI (2022) Artificial Intelligence for large-vessel occlusion stroke: a systematic review. World Neurosurg 159:207–220e201. https://doi.org/10.1016/j.wneu.2021.12.004
doi: 10.1016/j.wneu.2021.12.004
pubmed: 34896351
Titano JJ, Badgeley M, Schefflein J, Pain M, Su A, Cai M, Swinburne N, Zech J, Kim J, Bederson J, Mocco J, Drayer B, Lehar J, Cho S, Costa A, Oermann EK (2018) Automated deep-neural-network surveillance of cranial images for acute neurologic events. Nat Med 24:1337–1341. https://doi.org/10.1038/s41591-018-0147-y
doi: 10.1038/s41591-018-0147-y
pubmed: 30104767
Nasser L, McLeod SL, Hall JN (2024) Evaluating the reliability of a Remote Acuity Prediction Tool in a Canadian academic Emergency Department. Ann Emerg Med 83:373–379. https://doi.org/10.1016/j.annemergmed.2023.11.018
doi: 10.1016/j.annemergmed.2023.11.018
pubmed: 38180398
Daripa B, Lucchese S (2022) Artificial Intelligence-aided headache classification based on a set of questionnaires: a short review. Cureus 14:e29514. https://doi.org/10.7759/cureus.29514
doi: 10.7759/cureus.29514
pubmed: 36299975
pmcid: 9588408
Yang F, Meng T, Torben-Nielsen B, Magnus C, Liu C, Dejean E (2023) A machine learning approach to support triaging of primary versus secondary headache patients using complete blood count. PLoS ONE 18:e0282237. https://doi.org/10.1371/journal.pone.0282237
doi: 10.1371/journal.pone.0282237
pubmed: 36877693
pmcid: 9987784
Chu K, Kelly AM, Kuan WS, Kinnear FB, Keijzers G, Horner D et al (2024) Predictive performance of the common red flags in emergency department headache patients: a HEAD and HEAD-Colombia study. Emerg Med J 41:368–375. https://doi.org/10.1136/emermed-2023-213461
doi: 10.1136/emermed-2023-213461
pubmed: 38658053
Tsze DS, Ochs JB, Gonzalez AE, Dayan PS (2019) Red flag findings in children with headaches: prevalence and association with emergency department neuroimaging. Cephalalgia 39:185–196. https://doi.org/10.1177/0333102418781814
doi: 10.1177/0333102418781814
pubmed: 29874930
Boonstra A, Laven M (2022) Influence of artificial intelligence on the work design of emergency department clinicians a systematic literature review. BMC Health Serv Res 22:669. https://doi.org/10.1186/s12913-022-08070-7
doi: 10.1186/s12913-022-08070-7
pubmed: 35585603
pmcid: 9118875
Cerda IH, Zhang E, Dominguez M, Ahmed M, Lang M, Ashina S et al (2024) Artificial Intelligence and virtual reality in Headache Disorder diagnosis, classification, and management. Curr Pain Headache Rep. https://doi.org/10.1007/s11916-024-01279-7
doi: 10.1007/s11916-024-01279-7
pubmed: 38907793
Khan L, Shahreen M, Qazi A, Jamil Ahmed Shah S, Hussain S, Chang HT (2024) Migraine headache (MH) classification using machine learning methods with data augmentation. Sci Rep 14:5180. https://doi.org/10.1038/s41598-024-55874-0
doi: 10.1038/s41598-024-55874-0
pubmed: 38431729
pmcid: 10908834
Kwon J, Lee H, Cho S, Chung CS, Lee MJ, Park H (2020) Machine learning-based automated classification of headache disorders using patient-reported questionnaires. Sci Rep 10(1):14062. https://doi.org/10.1038/s41598-020-70992-1
doi: 10.1038/s41598-020-70992-1
pubmed: 32820214
pmcid: 7441379
Katsuki M, Narita N, Matsumori Y, Ishida N, Watanabe O, Cai S, Tominaga T (2020) Preliminary development of a deep learning-based automated primary headache diagnosis model using Japanese natural language processing of medical questionnaire. Surg Neurol Int 11:475. https://doi.org/10.25259/sni_827_2020
doi: 10.25259/sni_827_2020
pubmed: 33500813
pmcid: 7827501
Keel S, Lee PY, Scheetz J, Li Z, Kotowicz MA, MacIsaac RJ, He M (2018) Feasibility and patient acceptability of a novel artificial intelligence-based screening model for diabetic retinopathy at endocrinology outpatient services: a pilot study. Sci Rep 8:4330. https://doi.org/10.1038/s41598-018-22612-2
doi: 10.1038/s41598-018-22612-2
pubmed: 29531299
pmcid: 5847544
König IR, Fuchs O, Hansen G, von Mutius E, Kopp MV (2017) What is precision medicine? Eur Respir J 50. https://doi.org/10.1183/13993003.00391-2017
Ashina M, Terwindt GM, Al-Karagholi MA, de Boer I, Lee MJ, Hay DL et al (2021) Migraine: disease characterisation, biomarkers, and precision medicine. Lancet 397:1496–1504. https://doi.org/10.1016/s0140-6736(20)32162-0
doi: 10.1016/s0140-6736(20)32162-0
pubmed: 33773610
Meier TA, Refahi MS, Hearne G, Restifo DS, Munoz-Acuna R, Rosen GL, Woloszynek S (2024) The role and applications of Artificial Intelligence in the treatment of Chronic Pain. Curr Pain Headache Rep. https://doi.org/10.1007/s11916-024-01264-0
doi: 10.1007/s11916-024-01264-0
pubmed: 38822995
Gonzalez-Martinez A, Pagán J, Sanz-García A, García-Azorín D, Rodríguez-Vico JS, Jaimes A et al (2022) Machine-learning-based approach for predicting response to anti-calcitonin gene-related peptide (CGRP) receptor or ligand antibody treatment in patients with migraine: a multicenter Spanish study. Eur J Neurol 29:3102–3111. https://doi.org/10.1111/ene.15458
doi: 10.1111/ene.15458
pubmed: 35726393
Kogelman LJA, Esserlind AL, Francke Christensen A, Awasthi S, Ripke S, Ingason A et al (2019) Migraine polygenic risk score associates with efficacy of migraine-specific drugs. Neurol Genet 5:e364. https://doi.org/10.1212/nxg.0000000000000364
doi: 10.1212/nxg.0000000000000364
pubmed: 31872049
pmcid: 6878840
Pomes LM, Guglielmetti M, Bertamino E, Simmaco M, Borro M, Martelletti P (2019) Optimising migraine treatment: from drug-drug interactions to personalized medicine. J Headache Pain 20:56. https://doi.org/10.1186/s10194-019-1010-3
doi: 10.1186/s10194-019-1010-3
pubmed: 31101004
pmcid: 6734220
Lewinski AA, Walsh C, Rushton S, Soliman D, Carlson SM, Luedke MW et al (2022) Telehealth for the Longitudinal Management of Chronic conditions: systematic review. J Med Internet Res 24:e37100. https://doi.org/10.2196/37100
doi: 10.2196/37100
pubmed: 36018711
pmcid: 9463619
Clausen TC, Greve NK, Müller KI, Kristoffersen ES, Schytz HW (2022) Telemedicine in headache care: a systematic review. Cephalalgia 42:1397–1408. https://doi.org/10.1177/03331024221111554
doi: 10.1177/03331024221111554
pubmed: 35787157
Friedman DI, Rajan B, Seidmann A (2019) A randomized trial of telemedicine for migraine management. Cephalalgia 39:1577–1585. https://doi.org/10.1177/0333102419868250
doi: 10.1177/0333102419868250
pubmed: 31450969
Müller KI, Alstadhaug KB, Bekkelund SI (2017) Headache patients’ satisfaction with telemedicine: a 12-month follow-up randomized non-inferiority trial. Eur J Neurol 24:807–815. https://doi.org/10.1111/ene.13294
doi: 10.1111/ene.13294
pubmed: 28432757
Chiang CC, Halker Singh R, Lalvani N, Shubin Stein K, Henscheid Lorenz D, Lay C et al (2021) Patient experience of telemedicine for headache care during the COVID-19 pandemic: an American Migraine Foundation survey study. Headache 61:734–739. https://doi.org/10.1111/head.14110
doi: 10.1111/head.14110
pubmed: 34021595
pmcid: 8206943
Minen MT, Szperka CL, Kaplan K, Ehrlich A, Riggins N, Rizzoli P, Strauss LD (2021) Telehealth as a new care delivery model: the headache provider experience. Headache 61:1123–1131. https://doi.org/10.1111/head.14150
doi: 10.1111/head.14150
pubmed: 34309828
pmcid: 8721517
Sharma S, Rawal R, Shah D (2023) Addressing the challenges of AI-based telemedicine: best practices and lessons learned. J Educ Health Promot 12:338. https://doi.org/10.4103/jehp.jehp_402_23
doi: 10.4103/jehp.jehp_402_23
pubmed: 38023098
pmcid: 10671014
Stubberud A, Ingvaldsen SH, Brenner E, Winnberg I, Olsen A, Gravdahl GB et al (2023) Forecasting migraine with machine learning based on mobile phone diary and wearable data. Cephalalgia 43:3331024231169244. https://doi.org/10.1177/03331024231169244
doi: 10.1177/03331024231169244
pubmed: 37096352
Rogers DG, Santamaria K, Seng EK, Grinberg AS (2022) Behavioral health, Telemedicine, and opportunities for improving Access. Curr Pain Headache Rep 26:919–926. https://doi.org/10.1007/s11916-022-01096-w
doi: 10.1007/s11916-022-01096-w
pubmed: 36418847
pmcid: 9684808
Evans RW, Ghosh K (2015) A survey of Headache Medicine specialists on Career satisfaction and burnout. Headache 55:1448–1457. https://doi.org/10.1111/head.12708
doi: 10.1111/head.12708
pubmed: 26466948
Steiner TJ, Jensen R, Katsarava Z, Stovner LJ, Uluduz D, Adarmouch L et al (2021) Structured headache services as the solution to the ill-health burden of headache: 1. Rationale and description. J Headache Pain 22:78. https://doi.org/10.1186/s10194-021-01265-z
doi: 10.1186/s10194-021-01265-z
pubmed: 34289806
pmcid: 8293530
Tinelli M, Leonardi M, Paemeleire K, Raggi A, Mitsikostas D, de la Torre ER, Steiner TJ (2021) Structured headache services as the solution to the ill-health burden of headache. 3. Modelling effectiveness and cost-effectiveness of implementation in Europe: findings and conclusions. J Headache Pain 22:90. https://doi.org/10.1186/s10194-021-01305-8
doi: 10.1186/s10194-021-01305-8
pubmed: 34380429
pmcid: 8359596
Pereira F, Mitchell T, Botvinick M (2009) Machine learning classifiers and fMRI: a tutorial overview. NeuroImage 45:S199–209. https://doi.org/10.1016/j.neuroimage.2008.11.007
doi: 10.1016/j.neuroimage.2008.11.007
pubmed: 19070668
Orru G, Pettersson-Yeo W, Marquand AF, Sartori G, Mechelli A (2012) Using support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosci Biobehav Rev 36:1140–1152. https://doi.org/10.1016/j.neubiorev.2012.01.004
doi: 10.1016/j.neubiorev.2012.01.004
pubmed: 22305994
Chong CD, Gaw N, Fu Y, Li J, Wu T, Schwedt TJ (2017) Migraine classification using magnetic resonance imaging resting-state functional connectivity data. Cephalalgia 37:828–844. https://doi.org/10.1177/0333102416652091
doi: 10.1177/0333102416652091
pubmed: 27306407
Tu Y, Zeng F, Lan L, Li Z, Maleki N, Liuet B et al (2020) An fMRI-based neural marker for migraine without aura. Neurology 94:e741–e751. https://doi.org/10.1212/WNL.0000000000008962
doi: 10.1212/WNL.0000000000008962
pubmed: 31964691
pmcid: 7176301
Marino S, Jassar H, Kim DJ, Lim M, Nascimento TD, Dinovet ID et al (2023) Classifying migraine using PET compressive big data analytics of brain’s mu-opioid and D2/D3 dopamine neurotransmission. Front Pharmacol 14:1173596. https://doi.org/10.3389/fphar.2023.1173596
doi: 10.3389/fphar.2023.1173596
pubmed: 37383727
pmcid: 10294712
Schwedt TJ, Si B, Li J, Wu T, Chong CD (2017) Migraine Subclassification via a Data-Driven Automated Approach using multimodality factor mixture modeling of Brain structure measurements. Headache 57:1051–1064. https://doi.org/10.1111/head.13121
doi: 10.1111/head.13121
pubmed: 28627714
pmcid: 5507708
Rahman Siddiquee MM, Shah J, Chong C, Nikolova S, Dumkrieger G, Li B, Wu T, Schwedt TJ (2023) Headache classification and automatic biomarker extraction from structural MRIs using deep learning. Brain Commun 5:fcac311. https://doi.org/10.1093/braincomms/fcac311
doi: 10.1093/braincomms/fcac311
pubmed: 36751567
Yang H, Zhang J, Liu Q, Wang Y (2018) Multimodal MRI-based classification of migraine: using deep learning convolutional neural network. Biomed Eng Online 17:138. https://doi.org/10.1186/s12938-018-0587-0
doi: 10.1186/s12938-018-0587-0
pubmed: 30314437
pmcid: 6186044
Marucco E, Lisicki M, Magis D (2019) Electrophysiological characteristics of the migraine brain: current knowledge and perspectives. Curr Med Chem 26:6222–6235. https://doi.org/10.2174/0929867325666180627130811
doi: 10.2174/0929867325666180627130811
pubmed: 29956611
Hsiao F-J, Chen W-T, Pan L-LH, Liu H-Y, Wang Y-F, Chen S-P et al (2022) Resting-state magnetoencephalographic oscillatory connectivity to identify patients with chronic migraine using machine learning. J Headache Pain 23:130. https://doi.org/10.1186/s10194-022-01500-1
doi: 10.1186/s10194-022-01500-1
pubmed: 36192689
pmcid: 9531441
Kogelman LJA, Falkenberg K, Ottosson F, Ernst M, Russo F, Stentoft-Hansen V et al (2023) Multi-omic analyses of triptan-treated migraine attacks gives insight into molecular mechanisms. Sci Rep 2023 13:12395. https://doi.org/10.1038/s41598-023-38904-1
doi: 10.1038/s41598-023-38904-1
Mu J, Chen T, Quan S, Wang C, Zhao L, Liu J (2020) Neuroimaging features of whole-brain functional connectivity predict attack frequency of migraine. Hum Brain Mapp 41:984–993. https://doi.org/10.1002/hbm.24854
doi: 10.1002/hbm.24854
pubmed: 31680376
Garcia-Chimeno Y, Garcia-Zapirain B, Gomez-Beldarrain M, Fernandez-Ruanova B, Garcia-Monco JC (2017) Automatic migraine classification via feature selection committee and machine learning techniques over imaging and questionnaire data. BMC Med Inf Decis Mak 17:38. https://doi.org/10.1186/s12911-017-0434-4
doi: 10.1186/s12911-017-0434-4
Schwedt TJ, Chong CD, Wu T, Gaw N, Fu Y, Li J (2015) Accurate classification of chronic migraine via Brain magnetic resonance imaging. Headache 55:762–777. https://doi.org/10.1111/head.12584
doi: 10.1111/head.12584
pubmed: 26084235
pmcid: 4473808
Mitrovic K, Petrusic I, Radojicic A, Dakovic M, Savic A (2023) Migraine with aura detection and subtype classification using machine learning algorithms and morphometric magnetic resonance imaging data. Front Neurol 14:1106612. https://doi.org/10.3389/fneur.2023.1106612
doi: 10.3389/fneur.2023.1106612
pubmed: 37441607
pmcid: 10333052
Mitrovic K, Savic AM, Radojicic A, Dakovic M, Petrusic I (2023) Machine learning approach for migraine aura complexity score prediction based on magnetic resonance imaging data. J Headache Pain 24:169. https://doi.org/10.1186/s10194-023-01704-z
doi: 10.1186/s10194-023-01704-z
pubmed: 38105182
pmcid: 10726649
Messina R, Sudre CH, Wei DY, Filippi M, Ourselin S, Goadsby PJ (2023) Biomarkers of Migraine and Cluster Headache: differences and similarities. Ann Neurol 93:729–742. https://doi.org/10.1002/ana.26583
doi: 10.1002/ana.26583
pubmed: 36565271
Holmes S, Mar’i J, Simons LE, Zurakowski D, LeBel AA, O’Brien M, Borsook D (2022) Integrated Features for Optimizing Machine Learning classifiers of Pediatric and Young adults with a post-traumatic headache from healthy controls. Front Pain Res (Lausanne) 3:859881. https://doi.org/10.3389/fpain.2022.859881
doi: 10.3389/fpain.2022.859881
pubmed: 35655747
Chong CD, Berisha V, Ross K, Kahn M, Dumkrieger G, Schwedt TJ (2021) Distinguishing persistent post-traumatic headache from migraine: classification based on clinical symptoms and brain structural MRI data. Cephalalgia 41:943–955. https://doi.org/10.1177/0333102421991819
doi: 10.1177/0333102421991819
pubmed: 33926241
Wei HL, Yang WJ, Zhou GP, Chen Y-C, Yu Y-S, Yin X et al (2022) Altered static functional network connectivity predicts the efficacy of non-steroidal anti-inflammatory drugs in migraineurs without aura. Front Mol Neurosci 15:956797. https://doi.org/10.3389/fnmol.2022.956797
doi: 10.3389/fnmol.2022.956797
pubmed: 36176962
pmcid: 9513180
Yang XJ, Liu L, Xu ZL, Zhang Y-J, Liu D-P, Fishers M et al (2020) Baseline brain Gray Matter volume as a predictor of acupuncture outcome in treating migraine. Front Neurol 11:111. https://doi.org/10.3389/fneur.2020.00111
doi: 10.3389/fneur.2020.00111
pubmed: 32194493
pmcid: 7066302
Wang Y, Wang Y, Bu L, Wang S, Xie X, Lin F, Xiao Z (2022) Functional connectivity features of resting-state functional magnetic resonance imaging may distinguish migraine from tension-type headache. Front Neurosci 16:851111. https://doi.org/10.3389/fnins.2022.851111
doi: 10.3389/fnins.2022.851111
pubmed: 35557602
pmcid: 9087040
Lee SH, Lee J, Kim DW, Kim DH, Ahn SJ, Choi MG et al (2024) Factors to predict recurrence after epidural blood patch in patients with spontaneous intracranial hypotension. Headache 64:380–389. https://doi.org/10.1111/head.14703
doi: 10.1111/head.14703
pubmed: 38634709
Hirani R, Noruzi K, Khuram H, Hussaini AS, Aifuwa EI, Ely KE et al (2024) Artificial Intelligence and Healthcare: a journey through history, Present innovations, and future possibilities. Life (Basel) 14:557. https://doi.org/10.3390/life14050557
doi: 10.3390/life14050557
pubmed: 38792579
Kuner C, Cate F, Lynskey O, Millard C, Loideain N, Svantesson D (2018) Expanding the artificial intelligence-data protection debate. Int Data Priv Law 8:289–292
doi: 10.1093/idpl/ipy024
Kalkman S, Mostert M, Gerlinger C, van Delden JJ, van Thiel GJ (2019) Responsible data sharing in international health research: a systematic review of principles and norms. BMC Med Ethics 20:21. https://doi.org/10.1186/s12910-019-0359-9
doi: 10.1186/s12910-019-0359-9
pubmed: 30922290
pmcid: 6437875
He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K (2019) The practical implementation of artificial intelligence technologies in medicine. Nat Med 25:30–36. https://doi.org/10.1038/s41591-018-0307-0
doi: 10.1038/s41591-018-0307-0
pubmed: 30617336
pmcid: 6995276
Rashid D, Hirani R, Khessib S, Ali N, Etienne M (2024) Unveiling biases of artificial intelligence in healthcare: navigating the promise and pitfalls. Injury 55:111358. https://doi.org/10.1016/j.injury.2024.111358
doi: 10.1016/j.injury.2024.111358
pubmed: 38246015
Chopra H, Annu, Shin DK, Munjal K, Priyanka, Dhama K, Emran TB (2023) Revolutionizing clinical trials: the role of AI in accelerating medical breakthroughs. Int J Surg 109:4211–4220. https://doi.org/10.1097/JS9.0000000000000705
doi: 10.1097/JS9.0000000000000705
pubmed: 38259001
pmcid: 10720846
Morley J, Murphy L, Mishra A, Joshi I, Karpathakis K (2022) Governing data and Artificial Intelligence for Health Care: developing an international understanding. JMIR Form Res 6:e31623. https://doi.org/10.2196/31623
doi: 10.2196/31623
pubmed: 35099403
pmcid: 8844981
Khan B, Fatima H, Qureshi A, Kumar S, Hanan A, Hussain J, Abdullah S (2023) Drawbacks of Artificial Intelligence and their potential solutions in the Healthcare Sector. Biomed Mater Devices 1:731–738. https://doi.org/10.1007/s44174-023-00063-2
doi: 10.1007/s44174-023-00063-2
Gerke S, Minssen T, Cohen G (2020) Ethical and legal challenges of artificial intelligence-driven healthcare. Artif Intell Healthc 295–336. https://doi.org/10.1016/B978-0-12-818438-7.00012-5
Price WN, Gerke S, Cohen IG (2019) Potential liability for physicians using artificial intelligence. JAMA 322:1765. https://doi.org/10.1001/jama.2019.15064
doi: 10.1001/jama.2019.15064
pubmed: 31584609