Ortopedik Tedavide 3 Boyutlu Baskı ve Yapay Zeka

Papagelopoulos, P. J., Savvidou, O. D., Koutsouradis, P., Chloros, G. D., Kaseta, M. K., & Sourlas, I. (2021). Personalized medicine in orthopedic surgery. EFORT Open Reviews, 6(6), 472-480. https://doi.org/10.1302/2058-5241.6.200122

Ma, L., Yu, S., Xu, X., Moses Amadi, S., Zhang, J., & Wang, Z. (2023). Application of artificial intelligence in 3D printing physical organ models. Materials today. Bio, 23, 100792. https://doi.org/10.1016/j.mtbio.2023.100792

Tuomi, J., Paloheimo, K. S., Vehviläinen, J., Björkstrand, R., Salmi, M., Huotilainen, E., Kontio, R., Rouse, S., Gibson, I., & Mäkitie, A. A. (2014). A novel classification and online platform for planning and documentation of medical applications of additive manufacturing. Surgical Innovation, 21(6), 553-559. https://doi.org/10.1177/1553350614524838

Cabitza, F., Locoro, A., & Banfi, G. (2018). Machine learning in orthopedics: a literature review. Frontiers in Bioengineering and Biotechnology, 6, 75. https://doi.org/10.3389/fbioe.2018.00075

Wilcox, B., Mobbs, R. J., Wu, A. M., & Phan, K. (2017). Systematic review of 3D printing in spinal surgery: the current state of play. Journal of Spine Surgery, 3(3), 433-443. https://doi.org/10.21037/jss.2017.09.01

Javaid, M., & Haleem, A. (2018). Additive manufacturing applications in orthopaedics: A review. Journal of Clinical Orthopaedics and Trauma, 9(3), 202-206. https://doi.org/10.1016/j.jcot.2018.04.008

Wang, X., Xu, S., Zhou, S., Xu, W., Leary, M., Choong, P., Qian, M., Brandt, M., & Xie, Y. M. (2016). Topological design and additive manufacturing of porous metals for bone scaffolds and orthopaedic implants: A review. Biomaterials, 83, 127-141. https://doi.org/10.1016/j.biomaterials.2016.01.012

Xie, L., Chen, C., Zhang, Y., Zheng, W., Chen, H., & Cai, L. (2018). Three-dimensional printing assisted ORIF versus conventional ORIF for tibial plateau fractures: A systematic review and meta-analysis. International Journal of Surgery, 57, 35-44. https://doi.org/10.1016/j.ijsu.2018.07.012

Wong, K. C., Kumta, S. M., Geel, N. V., & Demol, J. (2015). One-step reconstruction with a 3D-printed, biomechanically evaluated custom implant after complex pelvic tumor resection. Computer Aided Surgery, 20(1), 14-23. https://doi.org/10.3109/10929088.2015.1076039

Angelini, A., Trovarelli, G., Berizzi, A., Pala, E., Breda, A., & Ruggieri, P. (2019). Three-dimension-printed custom-made prosthetic reconstructions: from revision surgery to oncologic reconstructions. International Orthopaedics, 43(1), 123-132. https://doi.org/10.1007/s00264-018-4232-0

Bizzotto, N., Tami, I., Tami, A., Spiegel, A., Romani, D., Corain, M., Adani, R., & Magnan, B. (2016). 3D Printed models of distal radius fractures. Injury, 47(4), 976-978. https://doi.org/10.1016/j.injury.2016.01.013

Thienpont, E., Schwab, P. E., & Fennema, P. (2014). A systematic review and meta-analysis of patient-specific instrumentation for improving alignment of the components in total knee replacement. The bone & joint journal, 96-B(8), 1052–1061. https://doi.org/10.1302/0301-620X.96B8.33747

Punyaratabandhu, T., Lohwongwatana, B., Puncreobutr, C., Kosiyatrakul, A., Veerapan, P., & Luenam, S. (2017). A Patient-Matched Entire First Metacarpal Prosthesis in Treatment of Giant Cell Tumor of Bone. Case reports in orthopedics, 2017, 4101346. https://doi.org/10.1155/2017/4101346

Wong, K. C., Sze, K. Y., Wong, I. O., Wong, C. M., & Kumta, S. M. (2016). Patient-specific instrument can achieve same accuracy with less resection time than navigation assistance in periacetabular pelvic tumor surgery: a cadaveric study. International journal of computer assisted radiology and surgery, 11(2), 307–316. https://doi.org/10.1007/s11548-015-1250-x

Arvinte, D., Kiran, M., & Sood, M. (2020). Cup-cage construct for massive acetabular defect in revision hip arthroplasty- A case series with medium to long-term follow-up. Journal of clinical orthopaedics and trauma, 11(1), 62–66. https://doi.org/10.1016/j.jcot.2019.04.021

Garg, B., Gupta, M., Singh, M., & Kalyanasundaram, D. (2019). Outcome and safety analysis of 3D-printed patient-specific pedicle screw jigs for complex spinal deformities: a comparative study. The Spine Journal, 19(1), 56-64. https://doi.org/10.1016/j.spinee.2018.05.001

Olczak, J., Fahlberg, N., Maki, A., Razavian, A. S., Jilert, A., Stark, A., Sköldenberg, O., & Gordon, M. (2017). Artificial intelligence for analyzing orthopedic trauma radiographs. Acta orthopaedica, 88(6), 581–586. https://doi.org/10.1080/17453674.2017.1344459

Liu, F., Guan, B., Zhou, Z., Samsonov, A., Rosas, H., Lian, K., Sharma, R., Kanarek, A., Kim, J., Guermazi, A., & Kijowski, R. (2018). Fully automated diagnosis of anterior cruciate ligament tears on knee MR images by using deep learning. Radiology: Artificial Intelligence, 1(1), e180091. https://doi.org/10.1148/ryai.2019180091

Salazar, D., Rossouw, P. E., Javed, F., & Michelogiannakis, D. (2024). Artificial intelligence for treatment planning and soft tissue outcome prediction of orthognathic treatment: A systematic review. Journal of orthodontics, 51(2), 107–119. https://doi.org/10.1177/14653125231203743

Corban, J., Lorange, J. P., Laverdiere, C., Khoury, J., Rachevsky, G., Burman, M., & Martineau, P. A. (2021). Artificial Intelligence in the Management of Anterior Cruciate Ligament Injuries. Orthopaedic journal of sports medicine, 9(7), 23259671211014206. https://doi.org/10.1177/23259671211014206

Lisacek-Kiosoglous, A. B., Powling, A. S., Fontalis, A., Gabr, A., Mazomenos, E., & Haddad, F. S. (2023). Artificial intelligence in orthopaedic surgery. Bone & joint research, 12(7), 447–454. https://doi.org/10.1302/2046-3758.127.BJR-2023-0111.R1

Nayak, S., & Das, R. (2020). Application of Artificial Intelligence (AI) in Prosthetic and Orthotic Rehabilitation. Service Robotics. https://doi.org/10.5772/intechopen.93903.

Navarrete-Welton, A., & Hashimoto, D. (2020). Current applications of artificial intelligence for intraoperative decision support in surgery. Frontiers of Medicine, 1-13. https://doi.org/10.1007/s11684-020-0784-7.

Loftus, T., Altieri, M., Balch, J., Abbott, K., Choi, J., Marwaha, J., Hashimoto, D., Brat, G., Raftopoulos, Y., Evans, H., Jackson, G., Walsh, D., & Tignanelli, C. (2023). Artificial Intelligence–enabled Decision Support in Surgery. Annals of Surgery, 278, 51 - 58. https://doi.org/10.1097/SLA.0000000000005853.

Dio, M., Barbuto, S., Bisegna, C., Bellin, A., Boccia, M., Amparore, D., Verri, P., Busacca, G., Sica, M., Cillis, S., Piramide, F., Zaccone, V., Piana, A., Alba, S., Volpi, G., Fiori, C., Porpiglia, F., & Checcucci, E. (2023). Artificial Intelligence-Based Hyper Accuracy Three-Dimensional (HA3D®) Models in Surgical Planning of Challenging Robotic Nephron-Sparing Surgery: A Case Report and Snapshot of the State-of-the-Art with Possible Future Implications. Diagnostics, 13. https://doi.org/10.3390/diagnostics13142320.

Rajamani, K., Styner, M., Talib, H., Zheng, G., Nolte, L., & Ballester, M. (2007). Statistical deformable bone models for robust 3D surface extrapolation from sparse data. Medical image analysis, 11 2, 99-109 . https://doi.org/10.1016/j.media.2006.05.001.

Chanda, S., Gupta, S., & Pratihar, D. (2016). A combined neural network and genetic algorithm based approach for optimally designed femoral implant having improved primary stability. Appl. Soft Comput., 38, 296-307. https://doi.org/10.1016/j.asoc.2015.10.020.

Muzalewska, M. (2017). Methodology of Multicriterial Optimization of Geometric Features of an Orthopedic Implant. Applied Sciences. https://doi.org/10.1007/978-3-319-70063-2_31.

Ding, H., Hai, Y., Zhou, L., Liu, Y., Zhang, Y., Han, C., & Zhang, Y. (2023). Clinical Application of Personalized Digital Surgical Planning and Precise Execution for Severe and Complex Adult Spinal Deformity Correction Utilizing 3D Printing Techniques. Journal of Personalized Medicine, 13. https://doi.org/10.3390/jpm13040602.

Lia, W. (2014). Individualized treatment of orthopaedics and 3D printing technology. Journal of Medical Biomechanics.

Mobbs, R., Choy, W., Wilson, P., McEvoy, A., Phan, K., & Parr, W. (2018). L5 En-Bloc Vertebrectomy with Customized Reconstructive Implant: Comparison of Patient-Specific Versus Off-the-Shelf Implant.. World neurosurgery, 112, 94-100 . https://doi.org/10.1016/j.wneu.2018.01.078.

Willemsen, K., Nizak, R., Noordmans, H., Castelein, R., Weinans, H., & Kruyt, M. (2019). Challenges in the design and regulatory approval of 3D-printed surgical implants: a two-case series.. The Lancet. Digital health, 1 4, e163-e171 . https://doi.org/10.1016/S2589-7500(19)30067-6.

Beckmann, J., Steinert, A., Zilkens, C., Zeh, A., Schnurr, C., Schmitt‐Sody, M., & Gebauer, M. (2016). [Partial replacement of the knee joint with patient-specific instruments and implants (ConforMIS iUni, iDuo)].. Der Orthopade, 45 4, 322-30 . https://doi.org/10.1007/s00132-016-3237-x.

Ryd, L., Flodström, K., & Manley, M. (2020). Patient-Specific Implants for Focal Cartilage Lesions in The Knee: Implant Survivorship Analysis up to Seven Years Post-Implantation.. Surgical technology international, 37. https://doi.org/10.52198/21.STI.38.OS1384.

Lal, H., & Patralekh, M. (2018). 3D printing and its applications in orthopaedic trauma: A technological marvel.. Journal of clinical orthopaedics and trauma, 9 3, 260-268 . https://doi.org/10.1016/j.jcot.2018.07.022.

Zhang, H., Ma, X., Wang, J., Guan, J., Li, K., Zhao, J., & Zhou, J. (2023). [Application and research progress of artificial intelligence technology in trauma treatment].. Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery, 37 11, 1431-1437 . https://doi.org/10.7507/1002-1892.202308003.

Morrison, R., Kashlan, K., Flanangan, C., Wright, J., Green, G., Hollister, S., & Weatherwax, K. (2015). Regulatory Considerations in the Design and Manufacturing of Implantable 3D‐Printed Medical Devices. Clinical and Translational Science, 8. https://doi.org/10.1111/cts.12315.

Murdoch, B. (2021). Privacy and artificial intelligence: challenges for protecting health information in a new era. BMC Medical Ethics, 22. https://doi.org/10.1186/s12910-021-00687-3.

Almalawi, A., Khan, A., Alsolami, F., Abushark, Y., & Alfakeeh, A. (2023). Managing Security of Healthcare Data for a Modern Healthcare System. Sensors (Basel, Switzerland), 23. https://doi.org/10.3390/s23073612.

Marchant, G., Campos-Outcalt, D., & Lindor, R. (2011). Physician liability: the next big thing for personalized medicine?. Personalized medicine, 8 4, 457-467 . https://doi.org/10.2217/pme.11.33.

Hresko, A., & Haga, S. (2012). Insurance Coverage Policies for Personalized Medicine. Journal of Personalized Medicine, 2, 201 - 216. https://doi.org/10.3390/jpm2040201.

Brown, P. (2010). Personalized medicine and comparative effectiveness research in an era of fixed budgets. The EPMA Journal, 1, 633 - 640. https://doi.org/10.1007/s13167-010-0058-6.

Mathur, S., & Sutton, J. (2017). Personalized medicine could transform healthcare.. Biomedical reports, 7 1, 3-5 . https://doi.org/10.3892/br.2017.922.

Jain, K. (2020). Economics of Personalized Medicine. Textbook of Personalized Medicine, 701 - 711. https://doi.org/10.1007/978-3-030-62080-6_27.

Jakka, S., & Rossbach, M. (2013). An economic perspective on personalized medicine. The HUGO Journal, 7. https://doi.org/10.1186/1877-6566-7-1.

Arslan-Yildiz, A., Assal, R., Chen, P., Guven, S., Inci, F., & Demirci, U. (2016). Towards artificial tissue models: past, present, and future of 3D bioprinting. Biofabrication, 8. https://doi.org/10.1088/1758-5090/8/1/014103.

Matai, I., Kaur, G., Seyedsalehi, A., McClinton, A., & Laurencin, C. (2020). Progress in 3D bioprinting technology for tissue/organ regenerative engineering.. Biomaterials, 226, 119536 . https://doi.org/10.1016/j.biomaterials.2019.119536.

Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25, 24 - 29. https://doi.org/10.1038/s41591-018-0316-z.

Hasan, S., & Farri, O. (2019). Clinical Natural Language Processing with Deep Learning. , 147-171. https://doi.org/10.1007/978-3-030-05249-2_5.

Zhou, X., Guo, Y., Shen, M., & Yang, G. (2020). Application of artificial intelligence in surgery. Frontiers of Medicine, 14, 417 - 430. https://doi.org/10.1007/s11684-020-0770-0.

Andras, I., Mazzone, E., Leeuwen, F., Naeyer, G., Oosterom, M., Beato, S., Buckle, T., O’Sullivan, S., Leeuwen, P., Beulens, A., Crisan, N., D’hondt, F., Schatteman, P., Poel, H., Dell’Oglio, P., & Mottrie, A. (2019). Artificial intelligence and robotics: a combination that is changing the operating room. World Journal of Urology, 38, 2359 - 2366. https://doi.org/10.1007/s00345-019-03037-6.

Papagelopoulos, P. J., Savvidou, O. D., Koutsouradis, P., Chloros, G. D., Kaseta, M. K., & Sourlas, I. (2021). Personalized medicine in orthopedic surgery. EFORT Open Reviews, 6(6), 472-480. https://doi.org/10.1302/2058-5241.6.200122

Ma, L., Yu, S., Xu, X., Moses Amadi, S., Zhang, J., & Wang, Z. (2023). Application of artificial intelligence in 3D printing physical organ models. Materials today. Bio, 23, 100792. https://doi.org/10.1016/j.mtbio.2023.100792

Tuomi, J., Paloheimo, K. S., Vehviläinen, J., Björkstrand, R., Salmi, M., Huotilainen, E., Kontio, R., Rouse, S., Gibson, I., & Mäkitie, A. A. (2014). A novel classification and online platform for planning and documentation of medical applications of additive manufacturing. Surgical Innovation, 21(6), 553-559. https://doi.org/10.1177/1553350614524838

Cabitza, F., Locoro, A., & Banfi, G. (2018). Machine learning in orthopedics: a literature review. Frontiers in Bioengineering and Biotechnology, 6, 75. https://doi.org/10.3389/fbioe.2018.00075

Wilcox, B., Mobbs, R. J., Wu, A. M., & Phan, K. (2017). Systematic review of 3D printing in spinal surgery: the current state of play. Journal of Spine Surgery, 3(3), 433-443. https://doi.org/10.21037/jss.2017.09.01

Javaid, M., & Haleem, A. (2018). Additive manufacturing applications in orthopaedics: A review. Journal of Clinical Orthopaedics and Trauma, 9(3), 202-206. https://doi.org/10.1016/j.jcot.2018.04.008

Wang, X., Xu, S., Zhou, S., Xu, W., Leary, M., Choong, P., Qian, M., Brandt, M., & Xie, Y. M. (2016). Topological design and additive manufacturing of porous metals for bone scaffolds and orthopaedic implants: A review. Biomaterials, 83, 127-141. https://doi.org/10.1016/j.biomaterials.2016.01.012

Xie, L., Chen, C., Zhang, Y., Zheng, W., Chen, H., & Cai, L. (2018). Three-dimensional printing assisted ORIF versus conventional ORIF for tibial plateau fractures: A systematic review and meta-analysis. International Journal of Surgery, 57, 35-44. https://doi.org/10.1016/j.ijsu.2018.07.012

Wong, K. C., Kumta, S. M., Geel, N. V., & Demol, J. (2015). One-step reconstruction with a 3D-printed, biomechanically evaluated custom implant after complex pelvic tumor resection. Computer Aided Surgery, 20(1), 14-23. https://doi.org/10.3109/10929088.2015.1076039

Angelini, A., Trovarelli, G., Berizzi, A., Pala, E., Breda, A., & Ruggieri, P. (2019). Three-dimension-printed custom-made prosthetic reconstructions: from revision surgery to oncologic reconstructions. International Orthopaedics, 43(1), 123-132. https://doi.org/10.1007/s00264-018-4232-0

Bizzotto, N., Tami, I., Tami, A., Spiegel, A., Romani, D., Corain, M., Adani, R., & Magnan, B. (2016). 3D Printed models of distal radius fractures. Injury, 47(4), 976-978. https://doi.org/10.1016/j.injury.2016.01.013

Thienpont, E., Schwab, P. E., & Fennema, P. (2014). A systematic review and meta-analysis of patient-specific instrumentation for improving alignment of the components in total knee replacement. The bone & joint journal, 96-B(8), 1052–1061. https://doi.org/10.1302/0301-620X.96B8.33747

Punyaratabandhu, T., Lohwongwatana, B., Puncreobutr, C., Kosiyatrakul, A., Veerapan, P., & Luenam, S. (2017). A Patient-Matched Entire First Metacarpal Prosthesis in Treatment of Giant Cell Tumor of Bone. Case reports in orthopedics, 2017, 4101346. https://doi.org/10.1155/2017/4101346

Wong, K. C., Sze, K. Y., Wong, I. O., Wong, C. M., & Kumta, S. M. (2016). Patient-specific instrument can achieve same accuracy with less resection time than navigation assistance in periacetabular pelvic tumor surgery: a cadaveric study. International journal of computer assisted radiology and surgery, 11(2), 307–316. https://doi.org/10.1007/s11548-015-1250-x

Arvinte, D., Kiran, M., & Sood, M. (2020). Cup-cage construct for massive acetabular defect in revision hip arthroplasty- A case series with medium to long-term follow-up. Journal of clinical orthopaedics and trauma, 11(1), 62–66. https://doi.org/10.1016/j.jcot.2019.04.021

Garg, B., Gupta, M., Singh, M., & Kalyanasundaram, D. (2019). Outcome and safety analysis of 3D-printed patient-specific pedicle screw jigs for complex spinal deformities: a comparative study. The Spine Journal, 19(1), 56-64. https://doi.org/10.1016/j.spinee.2018.05.001

Olczak, J., Fahlberg, N., Maki, A., Razavian, A. S., Jilert, A., Stark, A., Sköldenberg, O., & Gordon, M. (2017). Artificial intelligence for analyzing orthopedic trauma radiographs. Acta orthopaedica, 88(6), 581–586. https://doi.org/10.1080/17453674.2017.1344459

Liu, F., Guan, B., Zhou, Z., Samsonov, A., Rosas, H., Lian, K., Sharma, R., Kanarek, A., Kim, J., Guermazi, A., & Kijowski, R. (2018). Fully automated diagnosis of anterior cruciate ligament tears on knee MR images by using deep learning. Radiology: Artificial Intelligence, 1(1), e180091. https://doi.org/10.1148/ryai.2019180091

Salazar, D., Rossouw, P. E., Javed, F., & Michelogiannakis, D. (2024). Artificial intelligence for treatment planning and soft tissue outcome prediction of orthognathic treatment: A systematic review. Journal of orthodontics, 51(2), 107–119. https://doi.org/10.1177/14653125231203743

Corban, J., Lorange, J. P., Laverdiere, C., Khoury, J., Rachevsky, G., Burman, M., & Martineau, P. A. (2021). Artificial Intelligence in the Management of Anterior Cruciate Ligament Injuries. Orthopaedic journal of sports medicine, 9(7), 23259671211014206. https://doi.org/10.1177/23259671211014206

Lisacek-Kiosoglous, A. B., Powling, A. S., Fontalis, A., Gabr, A., Mazomenos, E., & Haddad, F. S. (2023). Artificial intelligence in orthopaedic surgery. Bone & joint research, 12(7), 447–454. https://doi.org/10.1302/2046-3758.127.BJR-2023-0111.R1

Nayak, S., & Das, R. (2020). Application of Artificial Intelligence (AI) in Prosthetic and Orthotic Rehabilitation. Service Robotics. https://doi.org/10.5772/intechopen.93903.

Navarrete-Welton, A., & Hashimoto, D. (2020). Current applications of artificial intelligence for intraoperative decision support in surgery. Frontiers of Medicine, 1-13. https://doi.org/10.1007/s11684-020-0784-7.

Loftus, T., Altieri, M., Balch, J., Abbott, K., Choi, J., Marwaha, J., Hashimoto, D., Brat, G., Raftopoulos, Y., Evans, H., Jackson, G., Walsh, D., & Tignanelli, C. (2023). Artificial Intelligence–enabled Decision Support in Surgery. Annals of Surgery, 278, 51 - 58. https://doi.org/10.1097/SLA.0000000000005853.

Dio, M., Barbuto, S., Bisegna, C., Bellin, A., Boccia, M., Amparore, D., Verri, P., Busacca, G., Sica, M., Cillis, S., Piramide, F., Zaccone, V., Piana, A., Alba, S., Volpi, G., Fiori, C., Porpiglia, F., & Checcucci, E. (2023). Artificial Intelligence-Based Hyper Accuracy Three-Dimensional (HA3D®) Models in Surgical Planning of Challenging Robotic Nephron-Sparing Surgery: A Case Report and Snapshot of the State-of-the-Art with Possible Future Implications. Diagnostics, 13. https://doi.org/10.3390/diagnostics13142320.

Rajamani, K., Styner, M., Talib, H., Zheng, G., Nolte, L., & Ballester, M. (2007). Statistical deformable bone models for robust 3D surface extrapolation from sparse data. Medical image analysis, 11 2, 99-109 . https://doi.org/10.1016/j.media.2006.05.001.

Chanda, S., Gupta, S., & Pratihar, D. (2016). A combined neural network and genetic algorithm based approach for optimally designed femoral implant having improved primary stability. Appl. Soft Comput., 38, 296-307. https://doi.org/10.1016/j.asoc.2015.10.020.

Muzalewska, M. (2017). Methodology of Multicriterial Optimization of Geometric Features of an Orthopedic Implant. Applied Sciences. https://doi.org/10.1007/978-3-319-70063-2_31.

Ding, H., Hai, Y., Zhou, L., Liu, Y., Zhang, Y., Han, C., & Zhang, Y. (2023). Clinical Application of Personalized Digital Surgical Planning and Precise Execution for Severe and Complex Adult Spinal Deformity Correction Utilizing 3D Printing Techniques. Journal of Personalized Medicine, 13. https://doi.org/10.3390/jpm13040602.

Lia, W. (2014). Individualized treatment of orthopaedics and 3D printing technology. Journal of Medical Biomechanics.

Mobbs, R., Choy, W., Wilson, P., McEvoy, A., Phan, K., & Parr, W. (2018). L5 En-Bloc Vertebrectomy with Customized Reconstructive Implant: Comparison of Patient-Specific Versus Off-the-Shelf Implant.. World neurosurgery, 112, 94-100 . https://doi.org/10.1016/j.wneu.2018.01.078.

Willemsen, K., Nizak, R., Noordmans, H., Castelein, R., Weinans, H., & Kruyt, M. (2019). Challenges in the design and regulatory approval of 3D-printed surgical implants: a two-case series.. The Lancet. Digital health, 1 4, e163-e171 . https://doi.org/10.1016/S2589-7500(19)30067-6.

Beckmann, J., Steinert, A., Zilkens, C., Zeh, A., Schnurr, C., Schmitt‐Sody, M., & Gebauer, M. (2016). [Partial replacement of the knee joint with patient-specific instruments and implants (ConforMIS iUni, iDuo)].. Der Orthopade, 45 4, 322-30 . https://doi.org/10.1007/s00132-016-3237-x.

Ryd, L., Flodström, K., & Manley, M. (2020). Patient-Specific Implants for Focal Cartilage Lesions in The Knee: Implant Survivorship Analysis up to Seven Years Post-Implantation.. Surgical technology international, 37. https://doi.org/10.52198/21.STI.38.OS1384.

Lal, H., & Patralekh, M. (2018). 3D printing and its applications in orthopaedic trauma: A technological marvel.. Journal of clinical orthopaedics and trauma, 9 3, 260-268 . https://doi.org/10.1016/j.jcot.2018.07.022.

Zhang, H., Ma, X., Wang, J., Guan, J., Li, K., Zhao, J., & Zhou, J. (2023). [Application and research progress of artificial intelligence technology in trauma treatment].. Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery, 37 11, 1431-1437 . https://doi.org/10.7507/1002-1892.202308003.

Morrison, R., Kashlan, K., Flanangan, C., Wright, J., Green, G., Hollister, S., & Weatherwax, K. (2015). Regulatory Considerations in the Design and Manufacturing of Implantable 3D‐Printed Medical Devices. Clinical and Translational Science, 8. https://doi.org/10.1111/cts.12315.

Murdoch, B. (2021). Privacy and artificial intelligence: challenges for protecting health information in a new era. BMC Medical Ethics, 22. https://doi.org/10.1186/s12910-021-00687-3.

Almalawi, A., Khan, A., Alsolami, F., Abushark, Y., & Alfakeeh, A. (2023). Managing Security of Healthcare Data for a Modern Healthcare System. Sensors (Basel, Switzerland), 23. https://doi.org/10.3390/s23073612.

Marchant, G., Campos-Outcalt, D., & Lindor, R. (2011). Physician liability: the next big thing for personalized medicine?. Personalized medicine, 8 4, 457-467 . https://doi.org/10.2217/pme.11.33.

Hresko, A., & Haga, S. (2012). Insurance Coverage Policies for Personalized Medicine. Journal of Personalized Medicine, 2, 201 - 216. https://doi.org/10.3390/jpm2040201.

Brown, P. (2010). Personalized medicine and comparative effectiveness research in an era of fixed budgets. The EPMA Journal, 1, 633 - 640. https://doi.org/10.1007/s13167-010-0058-6.

Mathur, S., & Sutton, J. (2017). Personalized medicine could transform healthcare.. Biomedical reports, 7 1, 3-5 . https://doi.org/10.3892/br.2017.922.

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