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DeFoor MT, Sheean AJ. Editorial Commentary: Experts in Shoulder Surgery Do Not Consistently Detect Artificial Intelligence-Generated Scientific Abstracts. Arthroscopy 2024:S0749-8063(24)00641-8. [PMID: 39243996 DOI: 10.1016/j.arthro.2024.08.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2024] [Accepted: 08/30/2024] [Indexed: 09/09/2024]
Abstract
There has been an exponential growth in the number of artificial intelligence (AI) and machine learning (ML)-related publications in recent years. For example, in shoulder and elbow surgery, there was a 6-fold increase in the number of publications between 2018 and 2021. AI shows potential to improve diagnostic precision, generate precise surgical templates, direct personalized treatment plans, and reduce administrative costs. However, while AI and ML technologies have the ability to positively impact biomedical research, it should be closely monitored and used with extreme caution in the realm of research and scientific writing. Of concern, current large language models raise concerns regarding veracity of AI-generated content, copyright and ownership infringement, fabricated references, lack of in-text citation, plagiarism, and questions of authorship. Recent research shows that even the most experienced surgeons are unable to consistently detect AI-generated scientific writing. Of note, AI detection software is more adept in this role. AI should be used with caution in the development and production of scholarly work.
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Dubin JA, Bains SS, DeRogatis MJ, Moore MC, Hameed D, Mont MA, Nace J, Delanois RE. Appropriateness of Frequently Asked Patient Questions Following Total Hip Arthroplasty From ChatGPT Compared to Arthroplasty-Trained Nurses. J Arthroplasty 2024; 39:S306-S311. [PMID: 38626863 DOI: 10.1016/j.arth.2024.04.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 04/02/2024] [Accepted: 04/05/2024] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND The use of ChatGPT (Generative Pretrained Transformer), which is a natural language artificial intelligence model, has gained unparalleled attention with the accumulation of over 100 million users within months of launching. As such, we aimed to compare the following: 1) orthopaedic surgeons' evaluation of the appropriateness of the answers to the most frequently asked patient questions after total hip arthroplasty; and 2) patients' evaluation of ChatGPT and arthroplasty-trained nurses responses to answer their postoperative questions. METHODS We prospectively created 60 questions to address the most commonly asked patient questions following total hip arthroplasty. We obtained answers from arthroplasty-trained nurses and from the ChatGPT-3.5 version for each of the questions. Surgeons graded each set of responses based on clinical judgment as 1) "appropriate," 2) "inappropriate" if the response contained inappropriate information, or 3) "unreliable" if the responses provided inconsistent content. Each patient was given a randomly selected question from the 60 aforementioned questions, with responses provided by ChatGPT and arthroplasty-trained nurses, using a Research Electronic Data Capture survey hosted at our local hospital. RESULTS The 3 fellowship-trained surgeons graded 56 out of 60 (93.3%) responses for the arthroplasty-trained nurses and 57 out of 60 (95.0%) for ChatGPT to be "appropriate." There were 175 out of 252 (69.4%) patients who were more comfortable following the ChatGPT responses and 77 out of 252 (30.6%) who preferred arthroplasty-trained nurses' responses. However, 199 out of 252 patients (79.0%) responded that they were "uncertain" with regard to trusting AI to answer their postoperative questions. CONCLUSIONS ChatGPT provided appropriate answers from a physician perspective. Patients were also more comfortable with the ChatGPT responses than those from arthroplasty-trained nurses. Inevitably, its successful implementation is dependent on its ability to provide credible information that is consistent with the goals of the physician and patient alike.
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Affiliation(s)
- Jeremy A Dubin
- LifeBridge Health, Sinai Hospital of Baltimore, Rubin Institute for Advanced Orthopedics, Baltimore, Maryland
| | - Sandeep S Bains
- LifeBridge Health, Sinai Hospital of Baltimore, Rubin Institute for Advanced Orthopedics, Baltimore, Maryland
| | - Michael J DeRogatis
- Department of Orthopaedic Surgery, St. Luke's University Health Network, Bethlehem, Pennsylvania
| | - Mallory C Moore
- LifeBridge Health, Sinai Hospital of Baltimore, Rubin Institute for Advanced Orthopedics, Baltimore, Maryland
| | - Daniel Hameed
- LifeBridge Health, Sinai Hospital of Baltimore, Rubin Institute for Advanced Orthopedics, Baltimore, Maryland
| | - Michael A Mont
- LifeBridge Health, Sinai Hospital of Baltimore, Rubin Institute for Advanced Orthopedics, Baltimore, Maryland
| | - James Nace
- LifeBridge Health, Sinai Hospital of Baltimore, Rubin Institute for Advanced Orthopedics, Baltimore, Maryland
| | - Ronald E Delanois
- LifeBridge Health, Sinai Hospital of Baltimore, Rubin Institute for Advanced Orthopedics, Baltimore, Maryland
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Pires DG, Silva NM, de Sousa BM, Marques JL, Ramos A, Ferreira JAF, Morais R, Vieira SI, Soares Dos Santos MP. A millimetre-scale capacitive biosensing and biophysical stimulation system for emerging bioelectronic bone implants. J R Soc Interface 2024; 21:20240279. [PMID: 39257282 DOI: 10.1098/rsif.2024.0279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Revised: 06/26/2024] [Accepted: 07/29/2024] [Indexed: 09/12/2024] Open
Abstract
Bioelectronic bone implants are being widely recognized as a promising technology for highly personalized bone/implant interface sensing and biophysical therapeutic stimulation. Such bioelectronic devices are based on an innovative concept with the ability to be applied to a wide range of implants, including in fixation and prosthetic systems. Recently, biointerface sensing using capacitive patterns was proposed to overcome the limitations of standard imaging technologies and other non-imaging technologies; moreover, electric stimulation using capacitive patterns was proposed to overcome the limitations of non-instrumented implants. We here provide an innovative low-power miniaturized electronic system with ability to provide both therapeutic stimulation and bone/implant interface monitoring using network-architectured capacitive interdigitated patterns. It comprises five modules: sensing, electric stimulation, processing, communication and power management. This technology was validated using in vitro tests: concerning the sensing system, its ability to detect biointerface changes ranging from tiny to severe bone-implant interface changes in target regions was validated; concerning the stimulation system, its ability to significantly enhance bone cells' full differentiation, including matrix maturation and mineralization, was also confirmed. This work provides an impactful contribution and paves the way for the development of the new generation of orthopaedic biodevices.
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Affiliation(s)
- Diogo G Pires
- Department of Mechanical Engineering, Centre for Mechanical Technology & Automation (TEMA), University of Aveiro , Aveiro 3810-193, Portugal
| | - Nuno M Silva
- Engineering Department, University of Trás-os-Montes e Alto Douro , Vila Real 5000-801, Portugal
| | - Bárbara M de Sousa
- Department of Medical Sciences, Institute of Biomedicine (iBiMED), University of Aveiro , Aveiro 3810-193, Portugal
| | - João L Marques
- Department of Physics, University of Aveiro , Aveiro 3810-193, Portugal
| | - António Ramos
- Department of Mechanical Engineering, Centre for Mechanical Technology & Automation (TEMA), University of Aveiro , Aveiro 3810-193, Portugal
- Intelligent Systems Associate Laboratory (LASI) , Guimarães 4800-058, Portugal
| | - Jorge A F Ferreira
- Department of Mechanical Engineering, Centre for Mechanical Technology & Automation (TEMA), University of Aveiro , Aveiro 3810-193, Portugal
- Intelligent Systems Associate Laboratory (LASI) , Guimarães 4800-058, Portugal
| | - Raul Morais
- Engineering Department, University of Trás-os-Montes e Alto Douro , Vila Real 5000-801, Portugal
- Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), University of Trás-os-Montes e Alto Douro , Vila Real, 5000-801, Portugal
| | - Sandra I Vieira
- Department of Medical Sciences, Institute of Biomedicine (iBiMED), University of Aveiro , Aveiro 3810-193, Portugal
| | - Marco P Soares Dos Santos
- Department of Mechanical Engineering, Centre for Mechanical Technology & Automation (TEMA), University of Aveiro , Aveiro 3810-193, Portugal
- Intelligent Systems Associate Laboratory (LASI) , Guimarães 4800-058, Portugal
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Liang W, Zhou C, Bai J, Zhang H, Long H, Jiang B, Wang J, Huang X, Zhang H, Zhao J. Prospective applications of bioactive materials in orthopedic therapies: A review. Heliyon 2024; 10:e36152. [PMID: 39247306 PMCID: PMC11379564 DOI: 10.1016/j.heliyon.2024.e36152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Revised: 07/26/2024] [Accepted: 08/10/2024] [Indexed: 09/10/2024] Open
Abstract
The biomedical application of biodegradable polymers for addressing bone-related diseases has garnered considerable attention in recent years. Advances in material technology have expanded the repertoire of materials suitable for orthopedic implants, with nanomaterials playing a pivotal role in replicating crucial surface properties akin to natural tissues. This comprehensive review explores the evaluation of bioactive glass ceramics, shedding light on their properties and applications. The synthesis of composites through composite manufacturing has emerged as a strategy to enhance biocompatibility and biomechanical characteristics. They are addressing challenges associated with conventional implants and nanomaterials, whether in the form of functional nano coatings or nanostructured surfaces, present opportunities to refine implant techniques. Novel developments in orthopedic biomaterials, such as smart biomaterials, porous structures, and 3D implants, offer stimuli-responsive behavior to achieve desired implant shapes and characteristics. Bioactive and biodegradable porous polymer/inorganic composite materials are explored for bone tissue engineering scaffolds, aiming to promote bone formation and regeneration. As a prospective direction, the integration of stem cells into scaffolds hints at the creation of next-generation synthetic/living hybrid biomaterials, displaying high adaptability in biological settings. This review establishes a foundation for nanotechnology-driven biomaterials by elucidating fundamental design factors crucial for orthopedic implant performance and their response to cell differentiation, proliferation, and adhesion.
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Affiliation(s)
- Wenqing Liang
- Department of Orthopaedics, Zhoushan Hospital of Traditional Chinese Medicine Affiliated to Zhejiang Chinese Medical University, Zhoushan, 316000, China
| | - Chao Zhou
- Department of Orthopedics, Zhoushan Guanghua Hospital, Zhoushan, 316000, China
| | - Juqin Bai
- Department of Orthopaedics, Zhoushan Hospital of Traditional Chinese Medicine Affiliated to Zhejiang Chinese Medical University, Zhoushan, 316000, China
| | - Hongwei Zhang
- Department of Orthopaedics, Zhoushan Hospital of Traditional Chinese Medicine Affiliated to Zhejiang Chinese Medical University, Zhoushan, 316000, China
| | - Hengguo Long
- Department of Orthopaedics, Zhoushan Hospital of Traditional Chinese Medicine Affiliated to Zhejiang Chinese Medical University, Zhoushan, 316000, China
| | - Bo Jiang
- Rehabilitation Department, Zhoushan Hospital of Traditional Chinese Medicine Affiliated to Zhejiang Chinese Medical University, Zhoushan, 316000, China
| | - Jiangwei Wang
- Medical Research Center, Zhoushan Hospital of Traditional Chinese Medicine Affiliated to Zhejiang Chinese Medical University, Zhoushan, 316000, China
| | - Xiaogang Huang
- Department of Orthopaedics, Zhoushan Hospital of Traditional Chinese Medicine Affiliated to Zhejiang Chinese Medical University, Zhoushan, 316000, China
| | - Hengjian Zhang
- Department of Orthopaedics, Zhoushan Hospital of Traditional Chinese Medicine Affiliated to Zhejiang Chinese Medical University, Zhoushan, 316000, China
| | - Jiayi Zhao
- Department of Orthopaedics, Zhoushan Hospital of Traditional Chinese Medicine Affiliated to Zhejiang Chinese Medical University, Zhoushan, 316000, China
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Goldman SN, Hui AT, Choi S, Mbamalu EK, Tirabady P, Eleswarapu AS, Gomez JA, Alvandi LM, Fornari ED. Applications of artificial intelligence for adolescent idiopathic scoliosis: mapping the evidence. Spine Deform 2024:10.1007/s43390-024-00940-w. [PMID: 39153073 DOI: 10.1007/s43390-024-00940-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Accepted: 07/13/2024] [Indexed: 08/19/2024]
Abstract
PURPOSE Adolescent idiopathic scoliosis (AIS) is a common spinal deformity with varying progression, complicating treatment decisions. Artificial intelligence (AI) and machine learning (ML) are increasingly prominent in orthopedic care, aiding in diagnosis, risk-stratification, and treatment guidance. This scoping review outlines AI applications in AIS. METHODS This study followed PRISMA-ScR guidelines and included articles that reported the development, use, or validation of AI models for treating, diagnosing, or predicting clinical outcomes in AIS. RESULTS 40 full-text articles were included, with most studies published in the last 5 years (77.5%). Common ML techniques were convolutional neural networks (55%), decision trees and random forests (15%), and artificial neural networks (15%). Most AI applications in AIS were for imaging analysis (25/40; 62.5%), focusing on automatic measurement of Cobb angle, and axial vertebral rotation (13/25; 52%) and curve classification/severity (13/25; 52%). Prediction was the second most common application (15/40; 37.5%), with studies predicting curve progression (9/15; 60%), and Cobb angles (9/15; 60%). Only 15 studies (37.5%) reported clinical implementation guidelines for AI in AIS management. 52.5% of studies reported model accuracy, with an average of 85.4%. CONCLUSION This review highlights the applications of AI in AIS care, notably including automatic radiographic analysis, curve type classification, prediction of curve progression, and AIS diagnosis. However, the current lack of clear clinical implementation guidelines, model transparency, and external validation of studied models limits clinician trust and the generalizability and applicability of AI in AIS management.
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Affiliation(s)
- Samuel N Goldman
- Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461, USA
| | - Aaron T Hui
- Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461, USA.
| | - Sharlene Choi
- Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461, USA
| | - Emmanuel K Mbamalu
- Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461, USA
| | - Parsa Tirabady
- Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461, USA
| | - Ananth S Eleswarapu
- Department of Orthopaedics, Montefiore Medical Center, Bronx, NY, 10461, USA
| | - Jaime A Gomez
- Department of Orthopaedics, Montefiore Medical Center, Bronx, NY, 10461, USA
| | - Leila M Alvandi
- Department of Orthopaedics, Montefiore Medical Center, Bronx, NY, 10461, USA
| | - Eric D Fornari
- Department of Orthopaedics, Montefiore Medical Center, Bronx, NY, 10461, USA
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Fontalis A, Zhao B, Putzeys P, Mancino F, Zhang S, Vanspauwen T, Glod F, Plastow R, Mazomenos E, Haddad FS. Is it feasible to develop a supervised learning algorithm incorporating spinopelvic mobility to predict impingement in patients undergoing total hip arthroplasty? Bone Jt Open 2024; 5:671-680. [PMID: 39139101 PMCID: PMC11322786 DOI: 10.1302/2633-1462.58.bjo-2024-0020.r1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/15/2024] Open
Abstract
Aims Precise implant positioning, tailored to individual spinopelvic biomechanics and phenotype, is paramount for stability in total hip arthroplasty (THA). Despite a few studies on instability prediction, there is a notable gap in research utilizing artificial intelligence (AI). The objective of our pilot study was to evaluate the feasibility of developing an AI algorithm tailored to individual spinopelvic mechanics and patient phenotype for predicting impingement. Methods This international, multicentre prospective cohort study across two centres encompassed 157 adults undergoing primary robotic arm-assisted THA. Impingement during specific flexion and extension stances was identified using the virtual range of motion (ROM) tool of the robotic software. The primary AI model, the Light Gradient-Boosting Machine (LGBM), used tabular data to predict impingement presence, direction (flexion or extension), and type. A secondary model integrating tabular data with plain anteroposterior pelvis radiographs was evaluated to assess for any potential enhancement in prediction accuracy. Results We identified nine predictors from an analysis of baseline spinopelvic characteristics and surgical planning parameters. Using fivefold cross-validation, the LGBM achieved 70.2% impingement prediction accuracy. With impingement data, the LGBM estimated direction with 85% accuracy, while the support vector machine (SVM) determined impingement type with 72.9% accuracy. After integrating imaging data with a multilayer perceptron (tabular) and a convolutional neural network (radiograph), the LGBM's prediction was 68.1%. Both combined and LGBM-only had similar impingement direction prediction rates (around 84.5%). Conclusion This study is a pioneering effort in leveraging AI for impingement prediction in THA, utilizing a comprehensive, real-world clinical dataset. Our machine-learning algorithm demonstrated promising accuracy in predicting impingement, its type, and direction. While the addition of imaging data to our deep-learning algorithm did not boost accuracy, the potential for refined annotations, such as landmark markings, offers avenues for future enhancement. Prior to clinical integration, external validation and larger-scale testing of this algorithm are essential.
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Affiliation(s)
- Andreas Fontalis
- Department of Trauma and Orthopaedic Surgery, University College Hospital, London, UK
- Division of Surgery and Interventional Science, University College London, London, UK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Baixiang Zhao
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | | | - Fabio Mancino
- Department of Trauma and Orthopaedic Surgery, University College Hospital, London, UK
| | - Shuai Zhang
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | | | - Fabrice Glod
- Hôpitaux Robert Schuman, Luxembourg City, Luxembourg
| | - Ricci Plastow
- Department of Trauma and Orthopaedic Surgery, University College Hospital, London, UK
| | - Evangelos Mazomenos
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Fares S. Haddad
- Department of Trauma and Orthopaedic Surgery, University College Hospital, London, UK
- Division of Surgery and Interventional Science, University College London, London, UK
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Mancino F, Fontalis A, Haddad FS. Beyond the scalpel. Bone Joint J 2024; 106-B:760-763. [PMID: 39084644 DOI: 10.1302/0301-620x.106b8.bjj-2024-0291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/02/2024]
Affiliation(s)
- Fabio Mancino
- Department of Trauma and Orthopaedic Surgery, University College London Hospitals, London, UK
| | - Andreas Fontalis
- Department of Trauma and Orthopaedic Surgery, University College London Hospitals, London, UK
| | - Fares S Haddad
- Department of Trauma and Orthopaedic Surgery, University College London Hospitals, London, UK
- The Princess Grace Hospital, London, UK
- The Bone & Joint Journal , London, UK
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Berhouet J, Samargandi R. Emerging Innovations in Preoperative Planning and Motion Analysis in Orthopedic Surgery. Diagnostics (Basel) 2024; 14:1321. [PMID: 39001212 PMCID: PMC11240316 DOI: 10.3390/diagnostics14131321] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2024] [Revised: 06/15/2024] [Accepted: 06/20/2024] [Indexed: 07/16/2024] Open
Abstract
In recent years, preoperative planning has undergone significant advancements, with a dual focus: improving the accuracy of implant placement and enhancing the prediction of functional outcomes. These breakthroughs have been made possible through the development of advanced processing methods for 3D preoperative images. These methods not only offer novel visualization techniques but can also be seamlessly integrated into computer-aided design models. Additionally, the refinement of motion capture systems has played a pivotal role in this progress. These "markerless" systems are more straightforward to implement and facilitate easier data analysis. Simultaneously, the emergence of machine learning algorithms, utilizing artificial intelligence, has enabled the amalgamation of anatomical and functional data, leading to highly personalized preoperative plans for patients. The shift in preoperative planning from 2D towards 3D, from static to dynamic, is closely linked to technological advances, which will be described in this instructional review. Finally, the concept of 4D planning, encompassing periarticular soft tissues, will be introduced as a forward-looking development in the field of orthopedic surgery.
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Affiliation(s)
- Julien Berhouet
- Service de Chirurgie Orthopédique et Traumatologique, Centre Hospitalier Régional Universitaire (CHRU) de Tours, 1C Avenue de la République, 37170 Chambray-les-Tours, France
- Equipe Reconnaissance de Forme et Analyse de l'Image, Laboratoire d'Informatique Fondamentale et Appliquée de Tours EA6300, Ecole d'Ingénieurs Polytechnique Universitaire de Tours, Université de Tours, 64 Avenue Portalis, 37200 Tours, France
| | - Ramy Samargandi
- Service de Chirurgie Orthopédique et Traumatologique, Centre Hospitalier Régional Universitaire (CHRU) de Tours, 1C Avenue de la République, 37170 Chambray-les-Tours, France
- Department of Orthopedic Surgery, Faculty of Medicine, University of Jeddah, Jeddah 23218, Saudi Arabia
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Fangerau H. Artifical intelligence in surgery: ethical considerations in the light of social trends in the perception of health and medicine. EFORT Open Rev 2024; 9:323-328. [PMID: 38726973 PMCID: PMC11099585 DOI: 10.1530/eor-24-0029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/12/2024] Open
Abstract
The use of artificial intelligence (AI) in medicine and surgery is currently predicted to be very promising. However, AI has the potential to change the doctor's role and the doctor-patient relationship. It has the potential to support people's desires for health, along with the potential to nudge or push people to behave in a certain way. To understand these potentials, we must see AI in the light of social developments that have brought about changes in how medicine's role, in a given society, is understood. The trends of 'privatisation of medicine' and 'public-healthisation of the private' are proposed as a contextual backdrop to explain why AI raises ethical concerns different from those previously caused by new medical technologies, and which therefore need to be addressed specifically for AI.
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Affiliation(s)
- Heiner Fangerau
- Department for the History, Philosophy and Ethics of Medicine, Medical Faculty, Heinrich-Heine University Duesseldorf Centre Health & Society, Moorenstraße 5, Düsseldorf, Germany
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Morita A, Iida Y, Inaba Y, Tezuka T, Kobayashi N, Choe H, Ike H, Kawakami E. Preoperative prediction for periprosthetic bone loss and individual evaluation of bisphosphonate effect after total hip arthroplasty using artificial intelligence. Bone Joint Res 2024; 13:184-192. [PMID: 38631686 PMCID: PMC11023718 DOI: 10.1302/2046-3758.134.bjr-2023-0188.r1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/19/2024] Open
Abstract
Aims This study was designed to develop a model for predicting bone mineral density (BMD) loss of the femur after total hip arthroplasty (THA) using artificial intelligence (AI), and to identify factors that influence the prediction. Additionally, we virtually examined the efficacy of administration of bisphosphonate for cases with severe BMD loss based on the predictive model. Methods The study included 538 joints that underwent primary THA. The patients were divided into groups using unsupervised time series clustering for five-year BMD loss of Gruen zone 7 postoperatively, and a machine-learning model to predict the BMD loss was developed. Additionally, the predictor for BMD loss was extracted using SHapley Additive exPlanations (SHAP). The patient-specific efficacy of bisphosphonate, which is the most important categorical predictor for BMD loss, was examined by calculating the change in predictive probability when hypothetically switching between the inclusion and exclusion of bisphosphonate. Results Time series clustering allowed us to divide the patients into two groups, and the predictive factors were identified including patient- and operation-related factors. The area under the receiver operating characteristic (ROC) curve (AUC) for the BMD loss prediction averaged 0.734. Virtual administration of bisphosphonate showed on average 14% efficacy in preventing BMD loss of zone 7. Additionally, stem types and preoperative triglyceride (TG), creatinine (Cr), estimated glomerular filtration rate (eGFR), and creatine kinase (CK) showed significant association with the estimated patient-specific efficacy of bisphosphonate. Conclusion Periprosthetic BMD loss after THA is predictable based on patient- and operation-related factors, and optimal prescription of bisphosphonate based on the prediction may prevent BMD loss.
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Affiliation(s)
- Akira Morita
- Department of Orthopaedic Surgery, Yokohama City University, Yokohama, Japan
| | - Yuta Iida
- Department of Orthopaedic Surgery, Yokohama City University, Yokohama, Japan
- Medical Sciences Innovation Hub Program, RIKEN, Yokohama, Japan
- Advanced Data Science Project, RIKEN Information R&D and Strategy Headquarters, RIKEN, Yokohama, Japan
| | - Yutaka Inaba
- Department of Orthopaedic Surgery, Yokohama City University, Yokohama, Japan
| | - Taro Tezuka
- Department of Orthopaedic Surgery, Yokohama City University, Yokohama, Japan
| | - Naomi Kobayashi
- Department of Orthopaedic Surgery, Yokohama City University Medical Center, Yokohama, Japan
| | - Hyonmin Choe
- Department of Orthopaedic Surgery, Yokohama City University, Yokohama, Japan
| | - Hiroyuki Ike
- Department of Orthopaedic Surgery, Yokohama City University, Yokohama, Japan
| | - Eiryo Kawakami
- Medical Sciences Innovation Hub Program, RIKEN, Yokohama, Japan
- Advanced Data Science Project, RIKEN Information R&D and Strategy Headquarters, RIKEN, Yokohama, Japan
- Department Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chiba, Japan
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Droppelmann G, Rodríguez C, Jorquera C, Feijoo F. Artificial intelligence in diagnosing upper limb musculoskeletal disorders: a systematic review and meta-analysis of diagnostic tests. EFORT Open Rev 2024; 9:241-251. [PMID: 38579757 PMCID: PMC11044087 DOI: 10.1530/eor-23-0174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/07/2024] Open
Abstract
Purpose The integration of artificial intelligence (AI) in radiology has revolutionized diagnostics, optimizing precision and decision-making. Specifically in musculoskeletal imaging, AI tools can improve accuracy for upper extremity pathologies. This study aimed to assess the diagnostic performance of AI models in detecting musculoskeletal pathologies of the upper extremity using different imaging modalities. Methods A meta-analysis was conducted, involving searches on MEDLINE/PubMed, SCOPUS, Cochrane Library, Lilacs, and SciELO. The quality of the studies was assessed using the QUADAS-2 tool. Diagnostic accuracy measures including sensitivity, specificity, diagnostic odds ratio (DOR), positive and negative likelihood ratios (PLR, NLR), area under the curve (AUC), and summary receiver operating characteristic were pooled using a random-effects model. Heterogeneity and subgroup analyses were also included. All statistical analyses and plots were performed using the R software package. Results Thirteen models from ten articles were analyzed. The sensitivity and specificity of the AI models to detect musculoskeletal conditions in the upper extremity were 0.926 (95% CI: 0.900; 0.945) and 0.908 (95% CI: 0.810; 0.958). The PLR, NLR, lnDOR, and the AUC estimates were found to be 19.18 (95% CI: 8.90; 29.34), 0.11 (95% CI: 0.18; 0.46), 4.62 (95% CI: 4.02; 5.22) with a (P < 0.001), and 95%, respectively. Conclusion The AI models exhibited strong univariate and bivariate performance in detecting both positive and negative cases within the analyzed dataset of musculoskeletal pathologies in the upper extremity.
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Affiliation(s)
- Guillermo Droppelmann
- Research Center on Medicine, Exercise, Sport and Health, MEDS Clinic, Santiago, RM, Chile
- Health Sciences PhD Program, Universidad Católica de Murcia UCAM, Murcia, Spain
- Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | | | - Carlos Jorquera
- Facultad de Ciencias, Escuela de Nutrición y Dietética, Universidad Mayor, Santiago, RM, Chile
| | - Felipe Feijoo
- School of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile
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Wu KA, Anastasio AT, Krez AN, Kutzer KM, DeOrio JK, Easley ME, Nunley JA, Adams SB. Association of Radiographic Soft Tissue Thickness With Revision Total Ankle Arthroplasty Following Primary Total Ankle Arthroplasty: A Minimum of 5-year Follow-up. FOOT & ANKLE ORTHOPAEDICS 2024; 9:24730114241255351. [PMID: 38803651 PMCID: PMC11129576 DOI: 10.1177/24730114241255351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/29/2024] Open
Abstract
Background The incidence of primary total ankle arthroplasty (TAA) is rising, with a corresponding increase in revision surgeries. Despite this, research on risk factors for revision TAA following primary TAA remains limited. Radiographic soft tissue thickness has been explored as a potential predictor for outcomes in hip, knee, and shoulder arthroplasty, but its role in TAA has not been assessed. This study aimed to assess the predictive value of radiographic soft tissue thickness for identifying patients at risk of requiring revision surgery following primary TAA. Methods A retrospective study was conducted on 323 patients who underwent primary TAA between 2003 and 2019. Radiographic measurements of soft tissue thickness were obtained from preoperative radiographs. Two novel radiographic measures of soft tissue thickness were developed and assessed (tibial tissue thickness and talus tissue thickness). Clinical variables including age, gender, body mass index (BMI), American Society of Anesthesiologists (ASA) classification, diabetes, smoking status, primary diagnosis, and implant type were recorded. Logistic regression analysis was used to assess the predictive value of soft tissue thickness and BMI for revision TAA. Results The rate of revision surgery was 4.3% (14 of 323 patients). Patients requiring revision had significantly greater tibial tissue (3.54 vs 2.48 cm; P = .02) and talus tissue (2.79 vs 2.42 cm; P = .02) thickness compared with those not requiring revision. Both the tibial tissue thickness (odds ratio 1.16 [1.12-1.20]; P < .01) and the talus tissue thickness (odds ratio: 1.10 [1.05-1.15]; P < .01) measurements were significant predictors of revision TAA in multivariable logistic regression models. However, BMI was not a significant predictor of revision TAA. The two metrics demonstrated excellent interrater reliability. Conclusion Greater soft tissue thickness was a better predictor of revision TAA compared with BMI. These findings suggest that radiographic soft tissue thickness may be a valuable tool for assessing the risk of the need for revision TAA following primary TAA. Further research is needed to validate and explore the potential impact on clinical practice. Level of Evidence Level III, comparative study.
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Affiliation(s)
- Kevin A. Wu
- Division of Foot and Ankle, Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, NC, USA
| | - Albert T. Anastasio
- Division of Foot and Ankle, Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, NC, USA
| | - Alexandra N. Krez
- Division of Foot and Ankle, Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, NC, USA
| | - Katherine M. Kutzer
- Division of Foot and Ankle, Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, NC, USA
| | - James K. DeOrio
- Division of Foot and Ankle, Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, NC, USA
| | - Mark E. Easley
- Division of Foot and Ankle, Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, NC, USA
| | - James A. Nunley
- Division of Foot and Ankle, Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, NC, USA
| | - Samuel B. Adams
- Division of Foot and Ankle, Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, NC, USA
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13
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Staats K, Kayani B, Haddad FS. The impact of the European Union's Medical Device Regulation on orthopaedic implants, technology, and future innovation. Bone Joint J 2024; 106-B:303-306. [PMID: 38555944 DOI: 10.1302/0301-620x.106b4.bjj-2023-1228.r1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/02/2024]
Affiliation(s)
- Kevin Staats
- Department of Trauma and Orthopaedics, University College Hospital, London, UK
| | - Babar Kayani
- Department of Trauma and Orthopaedics, University College Hospital, London, UK
| | - Fares S Haddad
- Department of Trauma and Orthopaedics, University College London NHS Hospitals, London, UK
- Princess Grace Hospital, London, UK
- The NIHR Biomedical Research Centre, UCLH, London, UK
- The Bone & Joint Journal , London, UK
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14
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Cote MP, Lubowitz JH. Recommended Requirements and Essential Elements for Proper Reporting of the Use of Artificial Intelligence Machine Learning Tools in Biomedical Research and Scientific Publications. Arthroscopy 2024; 40:1033-1038. [PMID: 38300189 DOI: 10.1016/j.arthro.2023.12.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Accepted: 12/30/2023] [Indexed: 02/02/2024]
Abstract
Essential elements required for proper use of artificial intelligence machine learning tools in biomedical research and scientific publications include (1) explanation justifying why a machine learning approach contributes to the purpose of the study; (2) description of the adequacy of the data (input) to produce the desired results (output); (3) details of the algorithmic (i.e., computational) approach including methods for organizing the data (preprocessing); the machine learning computational algorithm(s) assessed; on what data the models were trained; the presence of bias and efforts to mitigate these effects; and the methods for quantifying the variables (features) most influential in determining the results (e.g., Shapley values); (4) description of methods, and reporting of results, quantitating performance in terms of both model accuracy and model calibration (level of confidence in the model's predictions); (5) availability of the programming code (including a link to the code when available-ideally, the code should be available); (6) discussion of model internal validation (results applicable and sensitive to the population investigated and data on which the model was trained) and external validation (were the results investigated as to whether they are generalizable to different populations? If not, consideration of this limitation and discussion of plans for external validation, i.e., next steps). As biomedical research submissions using artificial intelligence technology increase, these requirements could facilitate purposeful use and comprehensive methodological reporting.
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Khoriati AA, Shahid Z, Fok M, Frank RM, Voss A, D'Hooghe P, Imam MA. Artificial intelligence and the orthopaedic surgeon: A review of the literature and potential applications for future practice: Current concepts. J ISAKOS 2024; 9:227-233. [PMID: 37949113 DOI: 10.1016/j.jisako.2023.10.015] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 10/28/2023] [Accepted: 10/30/2023] [Indexed: 11/12/2023]
Affiliation(s)
- Al-Achraf Khoriati
- Rowley Bristow Orthopaedic Centre, Ashford and St Peter's NHS Foundation Trust, Chertsey, KT106PZ, UK.
| | - Zuhaib Shahid
- Rowley Bristow Orthopaedic Centre, Ashford and St Peter's NHS Foundation Trust, Chertsey, KT106PZ, UK.
| | - Margaret Fok
- Department of Orthopaedics and Traumatology, Queen Mary Hospital, The University of Hong Kong, Pok Fu Lam Rd, High West, Hong Kong, China; Asia Pacific Orthopaedic Association, 57000, Malaysia.
| | - Rachel M Frank
- Department of Orthopaedic Surgery, Joint Preservation Program, University of Colorado School of Medicine, 12631 E 17th Ave, Mail Stop B202, Aurora, CO 80045, USA.
| | - Andreas Voss
- Sporthopaedicum Regensburg, Street, Hildegard-von-Bingen-Straße 1, 93053, Regensburg, Germany.
| | - Pieter D'Hooghe
- Aspetar Orthopedic and Sports Medicine Hospital, Aspire Zone, Sportscity Street 1, P.O. Box 29222, Doha, Qatar
| | - Mohamed A Imam
- Rowley Bristow Orthopaedic Centre, Ashford and St Peter's NHS Foundation Trust, Chertsey, KT106PZ, UK; Smart Health Centre, University of East London, University Way, London, E16 2RD, United Kingdom.
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16
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Salman LA, Khatkar H, Al-Ani A, Alzobi OZ, Abudalou A, Hatnouly AT, Ahmed G, Hameed S, AlAteeq Aldosari M. Reliability of artificial intelligence in predicting total knee arthroplasty component sizes: a systematic review. EUROPEAN JOURNAL OF ORTHOPAEDIC SURGERY & TRAUMATOLOGY : ORTHOPEDIE TRAUMATOLOGIE 2024; 34:747-756. [PMID: 38010443 PMCID: PMC10858112 DOI: 10.1007/s00590-023-03784-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 11/01/2023] [Indexed: 11/29/2023]
Abstract
PURPOSE This systematic review aimed to investigate the reliability of AI predictive models of intraoperative implant sizing in total knee arthroplasty (TKA). METHODS Four databases were searched from inception till July 2023 for original studies that studied the reliability of AI prediction in TKA. The primary outcome was the accuracy ± 1 size. This review was conducted per PRISMA guidelines, and the risk of bias was assessed using the MINORS criteria. RESULTS A total of four observational studies comprised of at least 34,547 patients were included in this review. A mean MINORS score of 11 out of 16 was assigned to the review. All included studies were published between 2021 and 2022, with a total of nine different AI algorithms reported. Among these AI models, the accuracy of TKA femoral component sizing prediction ranged from 88.3 to 99.7% within a deviation of one size, while tibial component sizing exhibited an accuracy ranging from 90 to 99.9% ± 1 size. CONCLUSION This study demonstrated the potential of AI as a valuable complement for planning TKA, exhibiting a satisfactory level of reliability in predicting TKA implant sizes. This predictive accuracy is comparable to that of the manual and digital templating techniques currently documented in the literature. However, future research is imperative to assess the impact of AI on patient care and cost-effectiveness. LEVEL OF EVIDENCE III PROSPERO registration number: CRD42023446868.
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Affiliation(s)
- Loay A Salman
- Department of Orthopaedic Surgery, Surgical Specialty Center, Hamad General Hospital, Hamad Medical Corporation, PO Box 3050, Doha, Qatar.
| | | | - Abdallah Al-Ani
- Office of Scientific Affairs and Research, King Hussein Cancer Center, Amman, Jordan
| | - Osama Z Alzobi
- Department of Orthopaedic Surgery, Surgical Specialty Center, Hamad General Hospital, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Abedallah Abudalou
- Department of Orthopaedic Surgery, Surgical Specialty Center, Hamad General Hospital, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Ashraf T Hatnouly
- Department of Orthopaedic Surgery, Surgical Specialty Center, Hamad General Hospital, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Ghalib Ahmed
- Department of Orthopaedic Surgery, Surgical Specialty Center, Hamad General Hospital, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Shamsi Hameed
- Department of Orthopaedic Surgery, Surgical Specialty Center, Hamad General Hospital, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Mohamed AlAteeq Aldosari
- Department of Orthopaedic Surgery, Surgical Specialty Center, Hamad General Hospital, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
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Andriollo L, Picchi A, Sangaletti R, Perticarini L, Rossi SMP, Logroscino G, Benazzo F. The Role of Artificial Intelligence in Anterior Cruciate Ligament Injuries: Current Concepts and Future Perspectives. Healthcare (Basel) 2024; 12:300. [PMID: 38338185 PMCID: PMC10855330 DOI: 10.3390/healthcare12030300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 01/19/2024] [Accepted: 01/22/2024] [Indexed: 02/12/2024] Open
Abstract
The remarkable progress in data aggregation and deep learning algorithms has positioned artificial intelligence (AI) and machine learning (ML) to revolutionize the field of medicine. AI is becoming more and more prevalent in the healthcare sector, and its impact on orthopedic surgery is already evident in several fields. This review aims to examine the literature that explores the comprehensive clinical relevance of AI-based tools utilized before, during, and after anterior cruciate ligament (ACL) reconstruction. The review focuses on current clinical applications and future prospects in preoperative management, encompassing risk prediction and diagnostics; intraoperative tools, specifically navigation, identifying complex anatomic landmarks during surgery; and postoperative applications in terms of postoperative care and rehabilitation. Additionally, AI tools in educational and training settings are presented. Orthopedic surgeons are showing a growing interest in AI, as evidenced by the applications discussed in this review, particularly those related to ACL injury. The exponential increase in studies on AI tools applicable to the management of ACL tears promises a significant future impact in its clinical application, with growing attention from orthopedic surgeons.
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Affiliation(s)
- Luca Andriollo
- Robotic Prosthetic Surgery Unit—Sports Traumatology Unit, Fondazione Poliambulanza Istituto Ospedaliero, 25124 Brescia, Italy; (R.S.); (L.P.); (S.M.P.R.); (F.B.)
- Department of Orthopedics, Catholic University of the Sacred Heart, 00168 Rome, Italy
| | - Aurelio Picchi
- Unit of Orthopedics, Department of Life, Health and Environmental Sciences, University of L’Aquila, 67100 L’Aquila, Italy; (A.P.); (G.L.)
| | - Rudy Sangaletti
- Robotic Prosthetic Surgery Unit—Sports Traumatology Unit, Fondazione Poliambulanza Istituto Ospedaliero, 25124 Brescia, Italy; (R.S.); (L.P.); (S.M.P.R.); (F.B.)
| | - Loris Perticarini
- Robotic Prosthetic Surgery Unit—Sports Traumatology Unit, Fondazione Poliambulanza Istituto Ospedaliero, 25124 Brescia, Italy; (R.S.); (L.P.); (S.M.P.R.); (F.B.)
| | - Stefano Marco Paolo Rossi
- Robotic Prosthetic Surgery Unit—Sports Traumatology Unit, Fondazione Poliambulanza Istituto Ospedaliero, 25124 Brescia, Italy; (R.S.); (L.P.); (S.M.P.R.); (F.B.)
| | - Giandomenico Logroscino
- Unit of Orthopedics, Department of Life, Health and Environmental Sciences, University of L’Aquila, 67100 L’Aquila, Italy; (A.P.); (G.L.)
| | - Francesco Benazzo
- Robotic Prosthetic Surgery Unit—Sports Traumatology Unit, Fondazione Poliambulanza Istituto Ospedaliero, 25124 Brescia, Italy; (R.S.); (L.P.); (S.M.P.R.); (F.B.)
- Biomedical Sciences Area, IUSS University School for Advanced Studies, 27100 Pavia, Italy
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18
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Fontalis A, Haddad FS. A leap towards personalized orthopaedic surgery and the prediction of spinopelvic mechanics in total hip arthroplasty. Bone Joint J 2024; 106-B:3-5. [PMID: 38160698 DOI: 10.1302/0301-620x.106b1.bjj-2023-1319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Affiliation(s)
- Andreas Fontalis
- Department of Trauma and Orthopaedics, University College London NHS Hospitals, London, UK
- Princess Grace Hospital, London, UK
| | - Fares S Haddad
- Department of Trauma and Orthopaedics, University College London NHS Hospitals, London, UK
- Princess Grace Hospital, London, UK
- The NIHR Biomedical Research Centre, UCLH, London, UK, London, UK
- The Bone & Joint Journal , London, UK
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19
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Chatterjee S, Bhattacharya M, Pal S, Lee SS, Chakraborty C. ChatGPT and large language models in orthopedics: from education and surgery to research. J Exp Orthop 2023; 10:128. [PMID: 38038796 PMCID: PMC10692045 DOI: 10.1186/s40634-023-00700-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 11/16/2023] [Indexed: 12/02/2023] Open
Abstract
ChatGPT has quickly popularized since its release in November 2022. Currently, large language models (LLMs) and ChatGPT have been applied in various domains of medical science, including in cardiology, nephrology, orthopedics, ophthalmology, gastroenterology, and radiology. Researchers are exploring the potential of LLMs and ChatGPT for clinicians and surgeons in every domain. This study discusses how ChatGPT can help orthopedic clinicians and surgeons perform various medical tasks. LLMs and ChatGPT can help the patient community by providing suggestions and diagnostic guidelines. In this study, the use of LLMs and ChatGPT to enhance and expand the field of orthopedics, including orthopedic education, surgery, and research, is explored. Present LLMs have several shortcomings, which are discussed herein. However, next-generation and future domain-specific LLMs are expected to be more potent and transform patients' quality of life.
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Affiliation(s)
- Srijan Chatterjee
- Institute for Skeletal Aging & Orthopaedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon-Si, 24252, Gangwon-Do, Republic of Korea
| | - Manojit Bhattacharya
- Department of Zoology, Fakir Mohan University, Vyasa Vihar, Balasore, 756020, Odisha, India
| | - Soumen Pal
- School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Sang-Soo Lee
- Institute for Skeletal Aging & Orthopaedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon-Si, 24252, Gangwon-Do, Republic of Korea.
| | - Chiranjib Chakraborty
- Department of Biotechnology, School of Life Science and Biotechnology, Adamas University, Kolkata, West Bengal, 700126, India.
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Abstract
Cite this article: Bone Joint Res 2023;12(8):494–496.
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Affiliation(s)
| | - A. H. R. W. Simpson
- Department of Orthopaedics and Trauma, University of Edinburgh Queen's Medical Research Institute, Edinburgh, UK
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Hasan S, Ahmed A, Waheed MA, Saleh ES, Omari A. Transforming Orthopedic Joint Surgeries: The Role of Artificial Intelligence (AI) and Robotics. Cureus 2023; 15:e43289. [PMID: 37692654 PMCID: PMC10492632 DOI: 10.7759/cureus.43289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/10/2023] [Indexed: 09/12/2023] Open
Abstract
The landscape of orthopedic joint surgeries, specifically total hip arthroplasty (THA) and total knee arthroplasty (TKA), is rapidly changing, and artificial intelligence (AI) along with robotics is at the helm of this transformation. These technologies, working synergistically, have introduced unprecedented levels of precision and personalization to surgical procedures, thereby significantly enhancing patient outcomes. In this editorial, we explore the changing perspectives of orthopedic surgeons toward AI and robotics and dissect the incorporation of these technologies in surgeries, their associated advantages, their inherent limitations, and potential future prospects. We draw from a host of recent studies to provide a comprehensive understanding of how these transformative technologies can augment surgical performance and patient care.
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Affiliation(s)
- Sazid Hasan
- Orthopedic Surgery, Oakland University William Beaumont School of Medicine, Rochester, USA
| | - Ashar Ahmed
- Biology, Wayne State University, Detroit, USA
| | | | - Ehab S Saleh
- Orthopedic Surgery, Oakland University William Beaumont School of Medicine, Rochester, USA
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