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In this review, we build upon the existing literature on AI applications in insurance and provide a comprehensive review to identify obstacles to future research.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>\n                      A search was conducted on the Web of Sciences (WOS) database until January 5\n                      <jats:sup>th<\/jats:sup>\n                      , 2025. Using the terms AI and insurance, 6913 articles were extracted from the database search and they were reviewed by two experts based on the inclusion\/exclusion criteria. In the end, 76 articles were included in the study and then scientometric and content analysis were carried out on them.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Based on recent studies, the volume of scientific publications on AI applications in the insurance industry has grown significantly since 2022. China (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200934), the United States of America (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200914), Belgium (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200913), the United Kingdom (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200912), Spain (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200910), and Egypt (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20099) are the leading contributors to this research domain. The findings highlight that AI has been integrated into the insurance sector across seven major categories. However, critical research gaps remain, classified into three overarching stages: pre\u2010AI implementation, focusing on challenges related to data readiness, regulatory compliance, and organizational preparedness; AI application areas, addressing the scope, effectiveness, and ethical concerns of AI\u2010driven solutions; and post\u2010AI implementation, examining long\u2010term impacts, performance evaluations, and continuous improvements. To bridge these gaps, future research should explore these three stages in depth, ensuring a more comprehensive and sustainable integration of AI in the insurance industry.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>In today\u2019s competitive market, insurance managers should be aware of how AI can help organizations provide innovative services and achieve valuable results. Therefore, future research should leverage the gaps identified in this study to introduce new and innovative algorithms for insurance data analysis in the modern world, thereby increasing profitability and reducing costs for insurance companies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1155\/int\/8864251","type":"journal-article","created":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T06:31:17Z","timestamp":1766989877000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence Applicability in the Insurance Industry: A Scientometric and Content Analysis Approach"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9118-5324","authenticated-orcid":false,"given":"Rasha","family":"Atlasi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7423-8853","authenticated-orcid":false,"given":"Sorayya","family":"Rezayi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4591-2209","authenticated-orcid":false,"given":"Abdollah","family":"Mahdavi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7324-6050","authenticated-orcid":false,"given":"Masoud","family":"Amanzadeh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3943-7053","authenticated-orcid":false,"given":"Roya","family":"Naemi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,12,28]]},"reference":[{"key":"e_1_2_12_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/tie.2018.2873526"},{"key":"e_1_2_12_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2017.2738069"},{"key":"e_1_2_12_3_2","volume-title":"Intelligent Multi Agent Systems for Decision Support in Insurance Industry. 2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics IEEE,","author":"Markic I.","year":"2014"},{"key":"e_1_2_12_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.01.026"},{"key":"e_1_2_12_5_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13634-016-0355-x"},{"key":"e_1_2_12_6_2","volume-title":"Big Data Management. 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