{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,6]],"date-time":"2026-11-06T15:50:16Z","timestamp":1793980216074,"version":"4.8.0"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:00:00Z","timestamp":1760400000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:00:00Z","timestamp":1760400000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knee Surg &amp; Relat Res"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Background<\/jats:title>\n            <jats:p>Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), is increasingly being integrated into total knee arthroplasty (TKA) to improve accuracy, efficiency, and personalized care. These technologies enable the analysis of large, complex datasets to support evidence-based clinical decision-making across all phases of the surgical process.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Main body<\/jats:title>\n            <jats:p>AI has demonstrated utility in multiple stages of TKA. In patient selection, ML algorithms can predict postoperative complications such as transfusion needs with high accuracy (AUC up to 0.842). For preoperative planning, DL techniques facilitate 3D anatomical reconstruction and implant size prediction, with some models achieving over 90% accuracy for exact component sizing, significantly outperforming traditional 2D templating. Intraoperatively, AI-assisted robotic systems and sensor technologies offer real-time feedback on alignment and soft tissue balancing. Postoperatively, AI-integrated wearable devices and mobile applications enable continuous monitoring and tailored rehabilitation; in some randomized trials, these tools have been associated with a statistically significant reduction in hospital readmission rates. Despite these advances, significant challenges remain, including algorithmic bias, a lack of model generalizability and explainability, and unresolved ethical and regulatory hurdles that present formidable barriers to widespread clinical implementation.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>AI has the potential to significantly reshape TKA by enabling more precise, data-driven, and patient-centered care. However, its promise is contingent on overcoming critical limitations. Broader implementation requires robust multicenter validation to ensure model reliability, the development of explainable algorithms to build clinical trust, and a commitment to responsible innovation. With continued progress, AI can serve as a powerful complementary tool to augment surgical expertise and enhance patient outcomes in orthopedic surgery.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/s43019-025-00295-0","type":"journal-article","created":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T11:48:57Z","timestamp":1760442537000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Artificial intelligence in total knee arthroplasty: clinical applications and implications"],"prefix":"10.1186","volume":"37","author":[{"given":"Kyeong Baek","family":"Kim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gi Beom","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun-Ho","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7331-9539","authenticated-orcid":false,"given":"Sang-Min","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,14]]},"reference":[{"issue":"1","key":"295_CR1","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1186\/s42836-022-00119-6","volume":"4","author":"C Batailler","year":"2022","unstructured":"Batailler C, Shatrov J, Sappey-Marinier E, Servien E, Parratte S, Lustig S (2022) Artificial intelligence in knee arthroplasty: current concept of the available clinical applications. Arthroplasty 4(1):17. https:\/\/doi.org\/10.1186\/s42836-022-00119-6","journal-title":"Arthroplasty"},{"issue":"1","key":"295_CR2","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/s12178-020-09600-8","volume":"13","author":"JM Helm","year":"2020","unstructured":"Helm JM, Swiergosz AM, Haeberle HS, Karnuta JM, Schaffer JL, Krebs VE et al (2020) Machine learning and artificial intelligence: definitions, applications, and future directions. Curr Rev Musculoskelet Med 13(1):69\u201376. https:\/\/doi.org\/10.1007\/s12178-020-09600-8","journal-title":"Curr Rev Musculoskelet Med"},{"key":"295_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.artd.2024.101396","volume":"27","author":"JP Mickley","year":"2024","unstructured":"Mickley JP, Kaji ES, Khosravi B, Mulford KL, Taunton MJ, Wyles CC (2024) Overview of artificial intelligence research within hip and knee arthroplasty. Arthroplasty Today 27:101396. https:\/\/doi.org\/10.1016\/j.artd.2024.101396","journal-title":"Arthroplasty Today"},{"issue":"4","key":"295_CR4","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1007\/s13244-018-0639-9","volume":"9","author":"R Yamashita","year":"2018","unstructured":"Yamashita R, Nishio M, Do RKG, Togashi K (2018) Convolutional neural networks: an overview and application in radiology. Insights Imaging 9(4):611\u2013629. https:\/\/doi.org\/10.1007\/s13244-018-0639-9","journal-title":"Insights Imaging"},{"issue":"10","key":"295_CR5","doi-asserted-by":"publisher","first-page":"2085","DOI":"10.1016\/j.arth.2022.10.039","volume":"38","author":"EA Karlin","year":"2023","unstructured":"Karlin EA, Lin CC, Meftah M, Slover JD, Schwarzkopf R (2023) The impact of machine learning on total joint arthroplasty patient outcomes: a systemic review. J Arthroplasty 38(10):2085\u20132095. https:\/\/doi.org\/10.1016\/j.arth.2022.10.039","journal-title":"J Arthroplasty"},{"issue":"4","key":"295_CR6","doi-asserted-by":"publisher","DOI":"10.1002\/rcs.2664","volume":"20","author":"X Liu","year":"2024","unstructured":"Liu X, Li S, Zou X, Chen X, Xu H, Yu Y et al (2024) Development and clinical validation of a deep learning-based knee CT image segmentation method for robotic-assisted total knee arthroplasty. Int J Med Robot 20(4):e2664. https:\/\/doi.org\/10.1002\/rcs.2664","journal-title":"Int J Med Robot"},{"issue":"1","key":"295_CR7","doi-asserted-by":"publisher","DOI":"10.1186\/s12891-024-07516-9","volume":"25","author":"UG Longo","year":"2024","unstructured":"Longo UG, De Salvatore S, Valente F, Villa Corta M, Violante B, Samuelsson K (2024) Artificial intelligence in total and unicompartmental knee arthroplasty. BMC Musculoskelet Disord 25(1):571. https:\/\/doi.org\/10.1186\/s12891-024-07516-9","journal-title":"BMC Musculoskelet Disord"},{"issue":"9","key":"295_CR8","doi-asserted-by":"publisher","first-page":"830","DOI":"10.2106\/jbjs.19.01128","volume":"102","author":"TG Myers","year":"2020","unstructured":"Myers TG, Ramkumar PN, Ricciardi BF, Urish KL, Kipper J, Ketonis C (2020) Artificial intelligence and orthopaedics: an introduction for clinicians. J Bone Joint Surg Am 102(9):830\u2013840. https:\/\/doi.org\/10.2106\/jbjs.19.01128","journal-title":"J Bone Joint Surg Am"},{"issue":"1s","key":"295_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.otsr.2020.102780","volume":"107","author":"R Pailh\u00e9","year":"2021","unstructured":"Pailh\u00e9 R (2021) Total knee arthroplasty: latest robotics implantation techniques. Orthop Traumatol Surg Res 107(1s):102780. https:\/\/doi.org\/10.1016\/j.otsr.2020.102780","journal-title":"Orthop Traumatol Surg Res"},{"issue":"12","key":"295_CR10","doi-asserted-by":"publisher","first-page":"676","DOI":"10.12968\/hmed.2018.79.12.676","volume":"79","author":"JR Panchmatia","year":"2018","unstructured":"Panchmatia JR, Visenio MR, Panch T (2018) The role of artificial intelligence in orthopaedic surgery. Br J Hosp Med (Lond) 79(12):676\u2013681. https:\/\/doi.org\/10.12968\/hmed.2018.79.12.676","journal-title":"Br J Hosp Med (Lond)"},{"issue":"1","key":"295_CR11","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1007\/s11999-009-1119-9","volume":"468","author":"RB Bourne","year":"2010","unstructured":"Bourne RB, Chesworth BM, Davis AM, Mahomed NN, Charron KD (2010) Patient satisfaction after total knee arthroplasty: who is satisfied and who is not? Clin Orthop Relat Res 468(1):57\u201363. https:\/\/doi.org\/10.1007\/s11999-009-1119-9","journal-title":"Clin Orthop Relat Res"},{"issue":"2","key":"295_CR12","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1007\/s00590-023-03784-8","volume":"34","author":"LA Salman","year":"2024","unstructured":"Salman LA, Khatkar H, Al-Ani A, Alzobi OZ, Abudalou A, Hatnouly AT et al (2024) Reliability of artificial intelligence in predicting total knee arthroplasty component sizes: a systematic review. Eur J Orthop Surg Traumatol 34(2):747\u2013756. https:\/\/doi.org\/10.1007\/s00590-023-03784-8","journal-title":"Eur J Orthop Surg Traumatol"},{"issue":"1","key":"295_CR13","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1186\/s43019-024-00228-3","volume":"36","author":"DW Lee","year":"2024","unstructured":"Lee DW, Song DS, Han H-S, Ro DH (2024) Accurate, automated classification of radiographic knee osteoarthritis severity using a novel method of deep learning: plug-in modules. Knee Surg Rel Res 36(1):24. https:\/\/doi.org\/10.1186\/s43019-024-00228-3","journal-title":"Knee Surg Rel Res"},{"issue":"6","key":"295_CR14","doi-asserted-by":"publisher","first-page":"1757","DOI":"10.1007\/s00167-019-05602-3","volume":"28","author":"C Jo","year":"2020","unstructured":"Jo C, Ko S, Shin WC, Han HS, Lee MC, Ko T et al (2020) Transfusion after total knee arthroplasty can be predicted using the machine learning algorithm. Knee Surg Sports Traumatol Arthrosc 28(6):1757\u20131764. https:\/\/doi.org\/10.1007\/s00167-019-05602-3","journal-title":"Knee Surg Sports Traumatol Arthrosc"},{"issue":"06","key":"295_CR15","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1055\/s-0041-1741396","volume":"36","author":"I Yeo","year":"2023","unstructured":"Yeo I, Klemt C, Robinson MG, Esposito JG, Uzosike AC, Kwon Y-M (2023) The use of artificial neural networks for the prediction of surgical site infection following TKA. J Knee Surg 36(06):637\u2013643","journal-title":"J Knee Surg"},{"issue":"7","key":"295_CR16","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1530\/eor-21-0107","volume":"7","author":"EC Rodr\u00edguez-Merch\u00e1n","year":"2022","unstructured":"Rodr\u00edguez-Merch\u00e1n EC (2022) The current role of the virtual elements of artificial intelligence in total knee arthroplasty. EFORT Open Rev 7(7):491\u2013497. https:\/\/doi.org\/10.1530\/eor-21-0107","journal-title":"EFORT Open Rev"},{"issue":"1","key":"295_CR17","doi-asserted-by":"publisher","first-page":"15743","DOI":"10.1038\/s41598-020-72941-4","volume":"10","author":"SB Kwon","year":"2020","unstructured":"Kwon SB, Ku Y, Han HS, Lee MC, Kim HC, Ro DH (2020) A machine learning-based diagnostic model associated with knee osteoarthritis severity. Sci Rep 10(1):15743. https:\/\/doi.org\/10.1038\/s41598-020-72941-4","journal-title":"Sci Rep"},{"key":"295_CR18","doi-asserted-by":"publisher","DOI":"10.3389\/frobt.2022.840282","volume":"9","author":"A Lambrechts","year":"2022","unstructured":"Lambrechts A, Wirix-Speetjens R, Maes F, Van Huffel S (2022) Artificial intelligence based patient-specific preoperative planning algorithm for total knee arthroplasty. Front Robot AI 9:840282. https:\/\/doi.org\/10.3389\/frobt.2022.840282","journal-title":"Front Robot AI"},{"key":"295_CR19","doi-asserted-by":"publisher","DOI":"10.3390\/s24020585","author":"S Al-Nasser","year":"2024","unstructured":"Al-Nasser S, Noroozi S, Harvey A, Aslani N, Haratian R (2024) Exploring the performance of an artificial intelligence-based load sensor for total knee replacements. Sensors (Basel). https:\/\/doi.org\/10.3390\/s24020585","journal-title":"Sensors (Basel)"},{"issue":"1","key":"295_CR20","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-67276-3","volume":"14","author":"Q Lan","year":"2024","unstructured":"Lan Q, Li S, Zhang J, Guo H, Yan L, Tang F (2024) Reliable prediction of implant size and axial alignment in AI-based 3D preoperative planning for total knee arthroplasty. Sci Rep 14(1):16971. https:\/\/doi.org\/10.1038\/s41598-024-67276-3","journal-title":"Sci Rep"},{"key":"295_CR21","doi-asserted-by":"publisher","DOI":"10.3390\/jcm12175498","author":"S Pagano","year":"2023","unstructured":"Pagano S, M\u00fcller K, G\u00f6tz J, Reinhard J, Schindler M, Grifka J et al (2023) The role and efficiency of an AI-powered software in the evaluation of lower limb radiographs before and after total knee arthroplasty. J Clin Med. https:\/\/doi.org\/10.3390\/jcm12175498","journal-title":"J Clin Med"},{"issue":"6","key":"295_CR22","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1302\/2633-1462.16.Bjo-2020-0056.R1","volume":"1","author":"MA Verstraete","year":"2020","unstructured":"Verstraete MA, Moore RE, Roche M, Conditt MA (2020) The application of machine learning to balance a total knee arthroplasty. Bone Joint Open 1(6):236\u2013244. https:\/\/doi.org\/10.1302\/2633-1462.16.Bjo-2020-0056.R1","journal-title":"Bone Joint Open"},{"key":"295_CR23","doi-asserted-by":"publisher","DOI":"10.3390\/s21165427","author":"L Sabatini","year":"2021","unstructured":"Sabatini L, Bosco F, Barberis L, Camazzola D, Bistolfi A, Risitano S et al (2021) Kinetic sensors for ligament balance and kinematic evaluation in anatomic bi-cruciate stabilized total knee arthroplasty. Sensors. https:\/\/doi.org\/10.3390\/s21165427","journal-title":"Sensors"},{"key":"295_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.artd.2024.101565","volume":"30","author":"J Lambrechts","year":"2024","unstructured":"Lambrechts J, Vansintjan P, Lapierre C, Sinnaeve F, Van Lysebettens W, Van Overschelde P (2024) Accuracy of a new augmented reality assisted technique for total knee arthroplasty: an in vivo study. Arthroplasty Today 30:101565. https:\/\/doi.org\/10.1016\/j.artd.2024.101565","journal-title":"Arthroplasty Today"},{"key":"295_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcot.2021.101605","volume":"22","author":"KP Iyengar","year":"2021","unstructured":"Iyengar KP, Gowers BTV, Jain VK, Ahluwalia RS, Botchu R, Vaishya R (2021) Smart sensor implant technology in total knee arthroplasty. J Clin Orthop Trauma 22:101605. https:\/\/doi.org\/10.1016\/j.jcot.2021.101605","journal-title":"J Clin Orthop Trauma"},{"key":"295_CR26","doi-asserted-by":"publisher","DOI":"10.3390\/jpm14080794","author":"L Sabatini","year":"2024","unstructured":"Sabatini L, Ascani D, Vezza D, Masse A, Cacciola G (2024) Novel surgical technique for total knee arthroplasty integrating kinematic alignment and real-time elongation of the ligaments using the NextAR system. J Pers Med. https:\/\/doi.org\/10.3390\/jpm14080794","journal-title":"J Pers Med"},{"issue":"10","key":"295_CR27","doi-asserted-by":"publisher","first-page":"2253","DOI":"10.1016\/j.arth.2019.05.021","volume":"34","author":"PN Ramkumar","year":"2019","unstructured":"Ramkumar PN, Haeberle HS, Ramanathan D, Cantrell WA, Navarro SM, Mont MA et al (2019) Remote patient monitoring using mobile health for total knee arthroplasty: validation of a wearable and machine learning-based surveillance platform. J Arthroplasty 34(10):2253\u20132259. https:\/\/doi.org\/10.1016\/j.arth.2019.05.021","journal-title":"J Arthroplasty"},{"issue":"12","key":"295_CR28","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2020.28328","volume":"3","author":"SJ Mehta","year":"2020","unstructured":"Mehta SJ, Hume E, Troxel AB, Reitz C, Norton L, Lacko H et al (2020) Effect of remote monitoring on discharge to home, return to activity, and rehospitalization after hip and knee arthroplasty: a randomized clinical trial. JAMA Netw Open 3(12):e2028328. https:\/\/doi.org\/10.1001\/jamanetworkopen.2020.28328","journal-title":"JAMA Netw Open"},{"key":"295_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.artd.2023.101308","volume":"25","author":"J Park","year":"2024","unstructured":"Park J, Zhong X, Miley EN, Rutledge RS, Kakalecik J, Johnson MC et al (2024) Machine learning-based predictive models for 90-day readmission of total joint arthroplasty using comprehensive electronic health records and patient-reported outcome measures. Arthroplasty Today 25:101308. https:\/\/doi.org\/10.1016\/j.artd.2023.101308","journal-title":"Arthroplasty Today"},{"issue":"1","key":"295_CR30","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","volume":"25","author":"EJ Topol","year":"2019","unstructured":"Topol EJ (2019) High-performance medicine: the convergence of human and artificial intelligence. Nat Med 25(1):44\u201356. https:\/\/doi.org\/10.1038\/s41591-018-0300-7","journal-title":"Nat Med"},{"issue":"1","key":"295_CR31","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1038\/s41591-018-0316-z","volume":"25","author":"A Esteva","year":"2019","unstructured":"Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K et al (2019) A guide to deep learning in healthcare. Nat Med 25(1):24\u201329. https:\/\/doi.org\/10.1038\/s41591-018-0316-z","journal-title":"Nat Med"},{"key":"295_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.ostima.2023.100161","author":"O Cigdem","year":"2023","unstructured":"Cigdem O, Deniz CM (2023) Artificial intelligence in knee osteoarthritis: a comprehensive review for 2022. Osteoarthritis Imaging. https:\/\/doi.org\/10.1016\/j.ostima.2023.100161","journal-title":"Osteoarthritis Imaging"}],"container-title":["Knee Surgery &amp; Related Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s43019-025-00295-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s43019-025-00295-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s43019-025-00295-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T11:48:58Z","timestamp":1760442538000},"score":1,"resource":{"primary":{"URL":"https:\/\/kneesurgrelatres.biomedcentral.com\/articles\/10.1186\/s43019-025-00295-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,14]]},"references-count":32,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["295"],"URL":"https:\/\/doi.org\/10.1186\/s43019-025-00295-0","relation":{},"ISSN":["2234-2451"],"issn-type":[{"value":"2234-2451","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,14]]},"assertion":[{"value":"26 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"44"}}