{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T20:02:04Z","timestamp":1774468924083,"version":"3.50.1"},"reference-count":15,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2024,7,31]],"date-time":"2024-07-31T00:00:00Z","timestamp":1722384000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2024,7,31]],"date-time":"2024-07-31T00:00:00Z","timestamp":1722384000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Knee surg. sports traumatol. arthrosc."],"published-print":{"date-parts":[[2024,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Explorative data analysis (EDA) is a critical step in scientific projects, aiming to uncover valuable insights and patterns within data. Traditionally, EDA involves manual inspection, visualization, and various statistical methods. The advent of artificial intelligence (AI) and machine learning (ML) has the potential to improve EDA, offering more sophisticated approaches that enhance its efficacy. This review explores how AI and ML algorithms can improve feature engineering and selection during EDA, leading to more robust predictive models and data\u2010driven decisions. Tree\u2010based models, regularized regression, and clustering algorithms were identified as key techniques. These methods automate feature importance ranking, handle complex interactions, perform feature selection, reveal hidden groupings, and detect anomalies. Real\u2010world applications include risk prediction in total hip arthroplasty and subgroup identification in scoliosis patients. Recent advances in explainable AI and EDA automation show potential for further improvement. The integration of AI and ML into EDA accelerates tasks and uncovers sophisticated insights. However, effective utilization requires a deep understanding of the algorithms, their assumptions, and limitations, along with domain knowledge for proper interpretation. As data continues to grow, AI will play an increasingly pivotal role in EDA when combined with human expertise, driving more informed, data\u2010driven decision\u2010making across various scientific domains.<\/jats:p>\n                  <jats:p>\n                    <jats:bold>Level of Evidence:<\/jats:bold>\n                    Level V \u2010 Expert opinion.\n                  <\/jats:p>","DOI":"10.1002\/ksa.12389","type":"journal-article","created":{"date-parts":[[2024,7,31]],"date-time":"2024-07-31T09:35:08Z","timestamp":1722418508000},"page":"3039-3042","source":"Crossref","is-referenced-by-count":20,"title":["The artificial intelligence advantage: Supercharging exploratory data analysis"],"prefix":"10.1002","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9721-685X","authenticated-orcid":false,"given":"Felix C.","family":"Oettl","sequence":"first","affiliation":[{"name":"Hospital for Special Surgery New York New York USA"},{"name":"Department of Orthopedic Surgery Balgrist University Hospital, University of Z\u00fcrich Zurich Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4562-4373","authenticated-orcid":false,"given":"Jacob F.","family":"Oeding","sequence":"additional","affiliation":[{"name":"Department of Orthopaedics Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg Gothenburg Sweden"},{"name":"Sahlgrenska Sports Medicine Center G\u00f6teborg Sweden"},{"name":"Mayo Clinic Alix School of Medicine Mayo Clinic Rochester Minnesota USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5179-4205","authenticated-orcid":false,"given":"Robert","family":"Feldt","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2751-8902","authenticated-orcid":false,"given":"Christophe","family":"Ley","sequence":"additional","affiliation":[{"name":"Department of Mathematics University of Luxembourg Esch\u2010sur\u2010Alzette Luxembourg"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4014-424X","authenticated-orcid":false,"given":"Michael T.","family":"Hirschmann","sequence":"additional","affiliation":[{"name":"Department of Orthopedic Surgery and Traumatology Kantonspital Baselland Liestal Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5383-3370","authenticated-orcid":false,"given":"Kristian","family":"Samuelsson","sequence":"additional","affiliation":[{"name":"Department of Orthopaedics Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg Gothenburg Sweden"},{"name":"Sahlgrenska Sports Medicine Center G\u00f6teborg Sweden"},{"name":"Department of Orthopaedics Sahlgrenska University Hospital M\u00f6lndal Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"ESSKA Artificial Intelligence Working Group","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2024,7,31]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2788613"},{"key":"e_1_2_9_3_1","unstructured":"Donnelly J. 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