Artificial Intelligence in Orthopedics. Where are We and Where are We Going?

La inteligencia artificial (IA) ha emergido como una de las herramientas más prometedoras en la transformación del sector salud, ofreciendo soluciones innovadoras que apoyan el diagnóstico, así como guiar el tratamiento y rehabilitación de los pacientes.[1] En términos generales, la IA se refiere a sistemas computacionales capaces de realizar tareas que normalmente requieren inteligencia humana, como el reconocimiento de patrones, el aprendizaje a partir de datos y la toma de decisiones.[2] Dentro del ámbito de la salud, su impacto ha sido significativo en áreas como la radiología, la cirugía robótica y la optimización de flujos de trabajo en hospitales.

Publication History

Article published online:
20 May 2025

• Referencias

• 1 Khoriati AA, Shahid Z, Fok M. et al. Artificial intelligence and the orthopaedic surgeon: A review of the literature and potential applications for future practice: Current concepts. J ISAKOS 2024; 9 (02) 227-233
• 2 Farhadi F, Barnes MR, Sugito HR, Sin JM, Henderson ER, Levy JJ. Applications of artificial intelligence in orthopaedic surgery. Front Med Technol 2022; 4: 995526
• 3 Lex JR, Di Michele J, Koucheki R, Pincus D, Whyne C, Ravi B. Artificial Intelligence for Hip Fracture Detection and Outcome Prediction: A Systematic Review and Meta-analysis. JAMA Netw Open 2023; 6 (03) e233391
• 4 Tieu A, Kroen E, Kadish Y. et al. The Role of Artificial Intelligence in the Identification and Evaluation of Bone Fractures. Bioengineering (Basel) 2024; 11 (04) 338
• 5 Lo Mastro A, Grassi E, Berritto D. et al. Artificial intelligence in fracture detection on radiographs: a literature review. Jpn J Radiol 2025; 43 (04) 551-585
• 6 Shen L, Gao C, Hu S. et al. Using Artificial Intelligence to Diagnose Osteoporotic Vertebral Fractures on Plain Radiographs. J Bone Miner Res 2023; 38 (09) 1278-1287
• 7 Zhang J, Liu F, Xu J. et al. Automated detection and classification of acute vertebral body fractures using a convolutional neural network on computed tomography. Front Endocrinol (Lausanne) 2023; 14: 1132725
• 8 Chung SW, Han SS, Lee JW. et al. Automated detection and classification of the proximal humerus fracture by using deep learning algorithm. Acta Orthop 2018; 89 (04) 468-473
• 9 Beyaz S, Açıcı K, Sümer E. Femoral neck fracture detection in X-ray images using deep learning and genetic algorithm approaches. Jt Dis Relat Surg 2020; 31 (02) 175-183
• 10 Oakden-Rayner L, Gale W, Bonham TA. et al. Validation and algorithmic audit of a deep learning system for the detection of proximal femoral fractures in patients in the emergency department: a diagnostic accuracy study. Lancet Digit Health 2022; 4 (05) e351-e358
• 11 Uysal F, Hardalaç F, Peker O, Tolunay T, Tokgöz N. Classification of Shoulder X-Ray Images with Deep Learning Ensemble Models. Appl Sci (Basel) 2021; 11: 2723
• 12 Magnéli M, Ling P, Gislén J. et al. Deep learning classification of shoulder fractures on plain radiographs of the humerus, scapula and clavicle. PLoS One 2023; 18 (08) e0289808
• 13 Rayan JC, Reddy N, Kan JH, Zhang W, Annapragada A. Binomial Classification of Pediatric Elbow Fractures Using a Deep Learning Multiview Approach Emulating Radiologist Decision Making. Radiol Artif Intell 2019; 1 (01) e180015
• 14 Luo J, Kitamura G, Arefan D, Doganay E, Panigrahy A, Wu S. Knowledge-Guided Multiview Deep Curriculum Learning for Elbow Fracture Classification. Mach Learn Med Imaging 2021; 12966: 555-564
• 15 Ashkani-Esfahani S, Mojahed Yazdi R, Bhimani R. et al. Detection of ankle fractures using deep learning algorithms. Foot Ankle Surg 2022; 28 (08) 1259-1265
• 16 Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial Intelligence in Surgery: Promises and Perils. Ann Surg 2018; 268 (01) 70-76
• 17 Kim JS, Arvind V, Oermann EK. et al. Predicting Surgical Complications in Patients Undergoing Elective Adult Spinal Deformity Procedures Using Machine Learning. Spine Deform 2018; 6 (06) 762-770
• 18 Kunze KN, Polce EM, Clapp I, Nwachukwu BU, Chahla J, Nho SJ. Machine Learning Algorithms Predict Functional Improvement After Hip Arthroscopy for Femoroacetabular Impingement Syndrome in Athletes. J Bone Joint Surg Am 2021; 103 (12) 1055-1062
• 19 Ramkumar PN, Karnuta JM, Haeberle HS. et al. Association Between Preoperative Mental Health and Clinically Meaningful Outcomes After Osteochondral Allograft for Cartilage Defects of the Knee: A Machine Learning Analysis. Am J Sports Med 2021; 49 (04) 948-957
• 20 Pareek A, Parkes CW, Bernard CD, Abdel MP, Saris DBF, Krych AJ. The SIFK score: a validated predictive model for arthroplasty progression after subchondral insufficiency fractures of the knee. Knee Surg Sports Traumatol Arthrosc 2020; 28 (10) 3149-3155
• 21 Lu Y, Forlenza E, Cohn MR. et al. Machine learning can reliably identify patients at risk of overnight hospital admission following anterior cruciate ligament reconstruction. Knee Surg Sports Traumatol Arthrosc 2021; 29 (09) 2958-2966
• 22 Innocenti B, Bori E. Robotics in orthopaedic surgery: why, what and how?. Arch Orthop Trauma Surg 2021; 141 (12) 2035-2042
• 23 Ruangsomboon P, Ruangsomboon O, Pornrattanamaneewong C, Narkbunnam R, Chareancholvanich K. Clinical and radiological outcomes of robotic-assisted versus conventional total knee arthroplasty: a systematic review and meta-analysis of randomized controlled trials. Acta Orthop 2023; 94: 60-79
• 24 Chen X, Xiong J, Wang P. et al. Robotic-assisted compared with conventional total hip arthroplasty: systematic review and meta-analysis. Postgrad Med J 2018; 94 (1112) 335-341
• 25 Volk VL, Steele KA, Cinello-Smith M. et al. Pedicle Screw Placement Accuracy in Robot-Assisted Spinal Fusion in a Multicenter Study. Ann Biomed Eng 2023; 51 (11) 2518-2527
• 26 Groisser BN, Thakur A, Hillstrom HJ. et al. Fully automated determination of robotic pedicle screw accuracy and precision utilizing computer vision algorithms. J Robot Surg 2024; 18 (01) 278
• 27 Park JJ, Tiefenbach J, Demetriades AK. The role of artificial intelligence in surgical simulation. Front Med Technol 2022; 4: 1076755
• 28 Kuhn AW, Yu JK, Gerull KM, Silverman RM, Aleem AW. Virtual Reality and Surgical Simulation Training for Orthopaedic Surgery Residents: A Qualitative Assessment of Trainee Perspectives. JBJS Open Access 2024; 9 (01) e23
• 29 Schöbel T, Schuschke L, Youssef Y, Rotzoll D, Theopold J, Osterhoff G. Immersive virtual reality in orthopedic surgery as elective subject for medical students : First experiences in curricular teaching. Orthopadie (Heidelb) 2024; 53 (05) 369-378
• 30 Gomindes AR, Adeeko ES, Khatri C. et al. Use of Virtual Reality in the Education of Orthopaedic Procedures: A Randomised Control Study in Early Validation of a Novel Virtual Reality Simulator. Cureus 2023; 15 (09) e45943
• 31 Ghassemi M, Naumann T, Schulam P, Beam AL, Chen IY, Ranganath R. A Review of Challenges and Opportunities in Machine Learning for Health. AMIA Jt Summits Transl Sci Proc 2020; 2020: 191-200