Artificial intelligence and ChatGPT in Orthopaedics and sports medicine
Journal of Experimental Orthopaedics volume 10, Article number: 74 (2023)
Artificial intelligence (AI) is looked upon nowadays as the potential major catalyst for the fourth industrial revolution. In the last decade, AI use in Orthopaedics increased approximately tenfold. Artificial intelligence helps with tracking activities, evaluating diagnostic images, predicting injury risk, and several other uses. Chat Generated Pre-trained Transformer (ChatGPT), which is an AI-chatbot, represents an extremely controversial topic in the academic community. The aim of this review article is to simplify the concept of AI and study the extent of AI use in Orthopaedics and sports medicine literature. Additionally, the article will also evaluate the role of ChatGPT in scientific research and publications.
Level of evidence: Level V, letter to review.
The term artificial intelligence (AI) was first described by McCarthy et al. in 1955, when they called AI "the science and engineering of making intelligent machines". They thought that these machines would be able to do tasks that people used to think only humans could do, like abstract thinking and advanced problem solving . Artificial intelligence also refers to the scientific and technological endeavor of developing intelligent computers that can perform functions normally associated with human effort . There is a subset of AI known as machine learning (ML), which uses computational techniques to examine massive data sets in order to categorize, forecast, or obtain valuable information without explicit instructions . The terms AI and ML are frequently used interchangeably .
Artificial intelligence is looked upon nowadays as the potential major catalyst for the fourth industrial revolution after steam engines in the 1760s, electricity and the petroleum revolution in the 1870s, and computers in the 1970s [35, 49]. Artificial intelligence has the potential to play such a role and provide new avenues as well as explore new frontiers in health care research and practice. In a recent study, single electronic medical record (EMR) research identified over 30.000 unique data items per patient . “Inadequate time, insufficient context, and insufficient presence" make it difficult for physicians and researchers to synthesize data and make therapeutic decisions in an era of information overload. AI's predictive powers might help with economic sustainability and data surfeit . The aim of this review is to simplify the concept of AI as well as evaluate its application in Orthopaedics in general and sports medicine in particular. Additionally, the article will discuss the rising and controversial role of Chat Generated Pre-trained Transformer (ChatGPT) in academia.
How does it work?
Artificial intelligence uses sophisticated statistical techniques to analyze and interpret complicated relationships between variables. These algorithms can "learn" from data with minimal human programming. It uses a huge dataset that is divided into predictors such as graft diameter or associated injuries and outcomes, for example, graft failure, non-union, or revision procedures. The computer model analyzes each set of "predictor" characteristics to predict a certain result. The study may discover key elements, measure and rank them, and design an algorithm to predict the result. These orthopaedic algorithms may be utilized for future patients . In other words, given a set of patient specific-data, for example, radiologic imaging, lab results, or any other data from electronic medical records, a diagnosis could be made, a risk score could be evaluated, or some treatment options could be evaluated .
Several modalities are used to accomplish the process of getting a meaningful output from input data. In general, algorithms could be categorized as supervised (the algorithm is trained by comparing its outcome to correctly labeled outputs) or unsupervised (the algorithm autonomously searches for patterns without trial-and-error training). The following table summarizes these methods (Table 1).
Chat Generative Pre-trained Transformer (ChatGPT)
It is the newest member of the AI family and has found its way very rapidly into healthcare services and research. ChatGPT uses a hybrid type of language formatting that includes supervised learning as well as non-supervised or reinforcement learning with human feedback (RLHF). It simply generates an output report depending on the inputs provided. It has the potential advantage of providing an overview of the existing literature about a certain topic, detecting some existing knowledge gaps, and providing novel ideas or hypotheses for research . Searching PubMed on June 6th, 2023, for the term "ChatGPT" revealed 564 articles (560 published in 2023). This chatbot had even been tested to pass high-level exams such as the United States Medical License Exam (USMLE) and the American Board of Orthopaedic Surgery (ABOS) exam [31, 33]. No one doubts the high potential and capabilities of different AI tools such as ChatGPT; however, there are several concerns about their application in health care services and research (which will be discussed later in a separate section).
Current status of artificial intelligence use in orthopaedics
In a systematic review published in 2018, Cabitza et al. showed a trend of increased use of AI in Orthopaedics with an almost tenfold increase since 2010 . They also found that AI was mainly used for diagnostic purposes, for example, osteoarthritis prediction or detection, joints, bones, and spine pathology imaging. The following table provides insight about the use of AI in Orthopaedics (Table 2).
Artificial intelligence and sports medicine
Nowadays, there is widespread use of several smart tracking devices and phones, which are not only used by professional players but also amateur athletes and regular individuals during their daily life activities. The amount of data gathered by these devices and the development of deep learning and machine learning modules may increase the usefulness of these tracking devices. We could expect individually tailored treatment plans of care from a special training protocol to mitigate the risk of certain injuries and expect to return to play after sustaining sports injuries . Artificial intelligence is becoming an integral pillar in modern sports medicine practice. Since professional sports across the world are a multibillion-dollar enterprise, optimizing players health status by decreasing injury risk has become a very crucial part of today’s sport. Karnuta et al. used an advanced ML algorithm to predict the next-season injury in hockey players with an accuracy of 94.6% (SD 0.5%) with good to excellent dependability . Likewise, AI has been widely studied for image interpretation in radiology as well as other orthopaedic disciplines, and it is now slowly making its way into sports medicine practice and research [3, 19, 56]. Štajduhar et al. used a semi-automated technique to evaluate magnetic resonance imaging (MRI) images to detect anterior cruciate ligament (ACL) injuries. The area under the curve for complete rupture detection was 0.94 (which indicates excellent diagnostic accuracy) . Kottie et al. were able to detect knee injuries from gait analysis using several parameters of ground reaction forces such as slope, direction, and push-off time . Artificial intelligence was also used in sports medicine to predict possible changes in patient reported outcomes (PROs) after a procedure. Nwachukwu et al. used a specific ML algorithm to identify salient predictive variables that led to a clinically significant difference across three different hip scores in patients with femoro-acetabular impingement (FAI) . The 3D distance mapping (which assesses the relative position between two opposing articular surfaces), coverage mapping (which utilizes the calculated distance maps to provide insights about areas of abnormal coverage), and volume measurements (which calculate the 3D volume amount of certain areas on WBCT images) of ankle syndesmosis have been recently studied in patients with progressive collapsing foot deformity (PCFD) . It is possible that these new automated and semi-automated measurements will help untangle the confusion about the diagnosis of syndesmotic instability.
Advanced distance mapping algorithm in orthopaedics
Weight-bearing computed tomography (WBCT) has recently been used to assess a variety of lower extremity deformities and pathologies, such as knee osteoarthritis , ankle arthritis, progressive collapsing foot deformity , and hallux valgus  WBCT more accurately measures bone positioning than traditional weight-bearing radiographs and non-weight-bearing CT . Previous studies have focused on using two-dimensional (2D) radiographs with manually calculated distances across a joint. However, recent studies have begun to shift to a more comprehensive approach, mapping the joint space width in three dimensions across the entire articulation of interest. These novel, three-dimensional methods provide superior characterization of the joint, made possible by automated joint mapping and ML-informed segmentation techniques. Examples in the literature include 3D mapping of the Chopart joint in patients with PCFD , the results of which are illustrated in Figs. 1 and 2. Another example is the use of distance mapping to characterize changes in the first metatarsophalangeal joint in patients with hallux valgus (bunion), as illustrated in Figs. 3 and 4.
Navigating concerns and potential solutions in AI integration in healthcare
After reviewing what AI is capable of across different fields, health care professionals can start tailoring this new technology to best serve their scope of practice. Although we are living in an era of exponential growth in AI use, this technology comes with several concerns that must be tackled very well to achieve the best possible outcomes. First, there are concerns about a decrease or break in the physician–patient relationship with the increased use of technology in modern medical practice. Actually, AI could be a very useful tool to strengthen the physician–patient relationship by decreasing the time physicians spend navigating electronic medical records. Artificial intelligence could present patient-specific data in a very organized and stratified way that even makes the physician very aware of all the fine details of his patients, which will help build a stronger rapport with their patients. Second, the AI "black box phenomenon" is a source of concern to several physicians as the development of outcomes from different algorithms can’t be tracked, which could render certain outcomes unquestionable (especially in deep learning modules) . There are also concerns about conflicts of decisions or potentially wrong AI outcomes (especially in the early use of this technology), which could decrease confidence levels at the physician or patient level or deskill physicians and turn them into machine-dependable. However, with judicious and supervised introduction and use of AI in practice, in addition to continuous appraisal and development of algorithms, we believe that the accuracy and precision of AI will get better over time.
The impact and imperative of regulating ChatGPT in scientific publishing
Artificial intelligence, especially in its very recent form, ChatGPT, plays a very controversial role in the scientific and academic community. It was even listed as the author of several peer-reviewed, indexed articles [28, 54]. ChatGPT was also capable of writing abstracts and manuscripts that were difficult to distinguish from human abstracts, even by experts in the field . However, the AI-generated articles carry a high risk of bias, inaccuracy, and misleading data . In a study by Bhattacharyya et al., they found that ChatGPT-generated articles had only 7% authentic references, while the rest were either fabricated or authentic but inaccurate . Since scholarly articles are the gatekeepers for the current body of scientific evidence and future directions, it becomes necessary to set rules and regulations for this double-edged sword. In a proactive move from the scientific community, authors now should sign a license not only to indicate that their work is original but also to explicitly prohibit the use of AI-generated materials such as texts, figures, and images . The academic community also needs to cooperate with AI developers to validate programs to detect AI-generated articles, as is the case in plagiarism checking . Training ChatGPT processing to be limited only to peer-reviewed articles or at least prioritized over other non-peer-reviewed articles could help increase the quality of its output. Moreover, AI could be used with caution as a research assistant to help summarize an article, generate potential research questions, extract relevant data such as authors or dates of publications, etc. Until more discrete regulations of AI rule in the academic world, the whole scientific community should judiciously use it with integrity, honesty, and transparency.
To keep up with the ever-increasing sophistication of artificial intelligence, orthopaedic surgeons must be familiar with and able to implement a variety of AI-based approaches and modalities. Without a doubt, the field of orthopaedic surgery has a wealth of human and material resources that may be used to advance artificial intelligence and harness it to serve patients optimally.
Adams M, Chen W, Holcdorf D, McCusker MW, Howe PD, Gaillard F (2019) Computer vs human: deep learning versus perceptual training for the detection of neck of femur fractures. J Med Imaging Radiat Oncol 63:27–32
Adankon MM, Dansereau J, Labelle H, Cheriet F (2012) Non invasive classification system of scoliosis curve types using least-squares support vector machines. Artif Intell Med 56:99–107
Al-Helo S, Alomari RS, Ghosh S, Chaudhary V, Dhillon G, Al-Zoubi MB et al (2013) Compression fracture diagnosis in lumbar: a clinical CAD system. Int J Comput Assist Radiol Surg 8:461–469
Alaqtash M, Sarkodie-Gyan T, Yu H, Fuentes O, Brower R, Abdelgawad A (2011) Automatic classification of pathological gait patterns using ground reaction forces and machine learning algorithms. Annu Int Conf IEEE Eng Med Biol Soc 2011:453–457
Ashinsky BG, Bouhrara M, Coletta CE, Lehallier B, Urish KL, Lin PC et al (2017) Predicting early symptomatic osteoarthritis in the human knee using machine learning classification of magnetic resonance images from the osteoarthritis initiative. J Orthop Res 35:2243–2250
Atkinson EJ, Therneau TM, Melton LJ 3rd, Camp JJ, Achenbach SJ, Amin S et al (2012) Assessing fracture risk using gradient boosting machine (GBM) models. J Bone Miner Res 27:1397–1404
Behrens A, Dibbern K, Lalevée M, Mendes A, de Carvalho K, Lintz F, Barbachan Mansur NS et al (2022) Coverage maps demonstrate 3D Chopart joint subluxation in weightbearing CT of progressive collapsing foot deformity. Sci Rep 12:19367
Bernasconi A, De Cesar NC, Siegler S, Jepsen M, Lintz F (2022) Weightbearing CT assessment of foot and ankle joints in Pes Planovalgus using distance mapping. Foot Ankle Surg 28:775–784
Bhattacharyya M, Miller VM, Bhattacharyya D, Miller LE, Miller V (2023) High Rates of Fabricated and Inaccurate References in ChatGPT-Generated Medical Content. Cureus 15:e39238
Bloomfield RA, Williams HA, Broberg JS, Lanting BA, McIsaac KA, Teeter MG (2019) Machine learning groups patients by early functional improvement likelihood based on wearable sensor instrumented preoperative timed-up-and-go tests. J Arthroplasty 34:2267–2271
Cabitza F, Locoro A, Banfi G (2018) Machine learning in orthopedics: a literature review. Front Bioeng Biotechnol 6:75
Cunha P, Moura DC, Guevara López MA, Guerra C, Pinto D, Ramos I (2014) Impact of ensemble learning in the assessment of skeletal maturity. J Med Syst 38:87
Dahmen J, Kayaalp ME, Ollivier M, Pareek A, Hirschmann MT, Karlsson J et al (2023) Artificial intelligence bot ChatGPT in medical research: the potential game changer as a double-edged sword. Knee Surg Sports Traumatol Arthrosc 31:1187–1189
de Carvalho KAM, Behrens A, Mallavarapu V, Jasper R, Mansur NSB, Lalevee M et al (2022) Automated three-dimensional distance and coverage mapping of hallux valgus: a case-control study. J Foot Ankle 16:41–45
de Carvalho KAM, Mallavarapu V, Ehret A, Dibbern K, Lee HY, Barbachan Mansur NS et al (2022) The use of advanced Semiautomated bone segmentation in hallux Rigidus. Foot Ankle Orthop 7:24730114221137596
de Carvalho KAM, Walt JS, Ehret A, Tazegul TE, Dibbern K, Mansur NSB et al (2022) Comparison between Weightbearing-CT semiautomatic and manual measurements in Hallux Valgus. Foot Ankle Surg 28:518–525
de Cesar NC, Schon LC, Thawait GK, da Fonseca LF, Chinanuvathana A, Zbijewski WB et al (2017) Flexible adult acquired flatfoot deformity: comparison between weight-bearing and non-weight-bearing measurements using cone-beam computed tomography. J Bone Joint Surg Am 99:e98
Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K et al (2019) A guide to deep learning in healthcare. Nat Med 25:24–29
Gan K, Xu D, Lin Y, Shen Y, Zhang T, Hu K et al (2019) Artificial intelligence detection of distal radius fractures: a comparison between the convolutional neural network and professional assessments. Acta Orthop 90:394–400
Gao CA, Howard FM, Markov NS, Dyer EC, Ramesh S, Luo Y et al (2023) Comparing scientific abstracts generated by ChatGPT to real abstracts with detectors and blinded human reviewers. NPJ Digit Med 6:75
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 13:69–76
Jones GG, Kotti M, Wiik AV, Collins R, Brevadt MJ, Strachan RK et al (2016) Gait comparison of unicompartmental and total knee arthroplasties with healthy controls. Bone Joint J 98-b:16–21
Jørgensen DR, Dam EB, Lillholm M (2013) Predicting knee cartilage loss using adaptive partitioning of cartilage thickness maps. Comput Biol Med 43:1045–1052
Kang YJ, Yoo JI, Cha YH, Park CH, Kim JT (2020) Machine learning-based identification of hip arthroplasty designs. J Orthop Translat 21:13–17
Karhade AV, Schwab JH, Bedair HS (2019) Development of machine learning algorithms for prediction of sustained postoperative opioid prescriptions after total hip arthroplasty. J Arthroplasty 34:2272-2277.e2271
Karnuta JM, Churchill JL, Haeberle HS, Nwachukwu BU, Taylor SA, Ricchetti ET et al (2020) The value of artificial neural networks for predicting length of stay, discharge disposition, and inpatient costs after anatomic and reverse shoulder arthroplasty. J Shoulder Elbow Surg 29:2385–2394
Karnuta JM, Luu BC, Haeberle HS, Saluan PM, Frangiamore SJ, Stearns KL et al (2020) Machine Learning Outperforms Regression Analysis to Predict Next-Season Major League Baseball Player Injuries: Epidemiology and Validation of 13,982 Player-Years From Performance and Injury Profile Trends, 2000–2017. Orthop J Sports Med 8:2325967120963046
King MR, ChatGPT (2023) A conversation on artificial intelligence, chatbots, and plagiarism in higher education. Cell Mol Bioeng 16:1–2
Kotti M, Duffell LD, Faisal AA, McGregor AH (2017) Detecting knee osteoarthritis and its discriminating parameters using random forests. Med Eng Phys 43:19–29
Kruse C, Eiken P, Vestergaard P (2017) Machine Learning Principles Can Improve Hip Fracture Prediction. Calcif Tissue Int 100:348–360
Lee SI, Huang A, Mortazavi B, Li C, Hoffman HA, Garst J et al (2016) Quantitative assessment of hand motor function in cervical spinal disorder patients using target tracking tests. J Rehabil Res Dev 53:1007–1022
Marques J, Genant HK, Lillholm M, Dam EB (2013) Diagnosis of osteoarthritis and prognosis of tibial cartilage loss by quantification of tibia trabecular bone from MRI. Magn Reson Med 70:568–575
Martin RK, Ley C, Pareek A, Groll A, Tischer T, Seil R (2022) Artificial intelligence and machine learning: an introduction for orthopaedic surgeons. Knee Surg Sports Traumatol Arthrosc 30:361–364
Martin RK, Pareek A, Krych AJ, Maradit Kremers H, Engebretsen L (2021) Machine learning in sports medicine: need for improvement. J isakos 6:1–2
Maxmen JS. Long-term trends in health care: The post-physician era reconsidered. Indicators and Trends in Health and Health Care: Springer; 1987:109–115.
McCarthy J, Minsky ML, Rochester N, Shannon CE (2006) A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955. AI Mag 27:12–12
Mordenti M, Ferrari E, Pedrini E, Fabbri N, Campanacci L, Muselli M et al (2013) Validation of a new multiple osteochondromas classification through Switching Neural Networks. Am J Med Genet A 161a:556–560
Navarro SM, Wang EY, Haeberle HS, Mont MA, Krebs VE, Patterson BM et al (2018) Machine learning and primary total knee arthroplasty: patient forecasting for a patient-specific payment model. J Arthroplasty 33:3617–3623
Netto CdC, Vivtcharenko V, Behrens A, Lalevee M, Mansur NS, Anderson DD et al (2022) Three-Dimensional Distance Maps of Ankle and Syndesmotic Joints from Weightbearing CT in Progressive Collapsing Foot Deformity: A Retrospective Case-Control Study. Foot Ankle Orthop 7:2473011421S2473000016
Nwachukwu BU, Beck EC, Lee EK, Cancienne JM, Waterman BR, Paul K et al (2020) Application of Machine Learning for Predicting Clinically Meaningful Outcome After Arthroscopic Femoroacetabular Impingement Surgery. Am J Sports Med 48:415–423
Olczak J, Fahlberg N, Maki A, Razavian AS, Jilert A, Stark A et al (2017) Artificial intelligence for analyzing orthopedic trauma radiographs. Acta Orthop 88:581–586
Ollivier M, Pareek A, Dahmen J, Kayaalp ME, Winkler PW, Hirschmann MT et al (2023) A deeper dive into ChatGPT: history, use and future perspectives for orthopaedic research. Knee Surg Sports Traumatol Arthrosc 31:1190–1192
Pedoia V, Haefeli J, Morioka K, Teng HL, Nardo L, Souza RB et al (2018) MRI and biomechanics multidimensional data analysis reveals R(2) -R(1ρ) as an early predictor of cartilage lesion progression in knee osteoarthritis. J Magn Reson Imaging 47:78–90
Prasoon A, Petersen K, Igel C, Lauze F, Dam E, Nielsen M (2013) Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network. Med Image Comput Comput Assist Interv 16:246–253
Ramkumar PN, Luu BC, Haeberle HS, Karnuta JM, Nwachukwu BU, Williams RJ (2022) Sports Medicine and Artificial Intelligence: A Primer. Am J Sports Med 50:1166–1174
Roemer FW, Guermazi A, Demehri S, Wirth W, Kijowski R (2022) Imaging in Osteoarthritis. Osteoarthr Cartil 30:913–934
Shahreyar M, Bob-Manuel T, Khouzam RN, Bashir MW, Sulaiman S, Akinseye O et al (2018) Trends, predictors and outcomes of ischemic stroke and intracranial hemorrhage in patients with a left ventricular assist device. Ann Transl Med 6:5
Shakoor D, Osgood GM, Brehler M, Zbijewski WB, de Cesar NC, Shafiq B et al (2019) Cone-beam CT measurements of distal tibio-fibular syndesmosis in asymptomatic uninjured ankles: does weight-bearing matter? Skeletal Radiol 48:583–594
Sovacool BK (2009) Early modes of transport in the United States: Lessons for modern energy policymakers. Policy Soc 27:411–427
Štajduhar I, Mamula M, Miletić D, Ünal G (2017) Semi-automated detection of anterior cruciate ligament injury from MRI. Comput Methods Programs Biomed 140:151–164
Tazegul TE, Anderson DD, Barbachan Mansur NS, Kajimura Chinelati RM, Iehl C, VandeLune C et al (2022) An Objective Computational Method to Quantify Ankle Osteoarthritis From Low-Dose Weightbearing Computed Tomography. Foot Ankle Orthop 7:24730114221116804
Thorp HH (2023) ChatGPT is fun, but not an author. Am Assoc Adv Sci 379:313–313
Topol EJ (2019) High-performance medicine: the convergence of human and artificial intelligence. Nat Med 25:44–56
Transformer CGP-t, Zhavoronkov A (2022) Rapamycin in the context of Pascal’s Wager: generative pre-trained transformer perspective. Oncoscience 9:82
Van Dis EA, Bollen J, Zuidema W, van Rooij R, Bockting CL (2023) ChatGPT: five priorities for research. Nature 614:224–226
Wang J, Fang Z, Lang N, Yuan H, Su MY, Baldi P (2017) A multi-resolution approach for spinal metastasis detection using deep Siamese neural networks. Comput Biol Med 84:137–146
Wyles CC, Tibbo ME, Fu S, Wang Y, Sohn S, Kremers WK et al (2019) Use of natural language processing algorithms to identify common data elements in operative notes for total hip arthroplasty. J Bone Joint Surg Am 101:1931–1938
Zhang J, Dushaj K, Rasquinha VJ, Scuderi GR, Hepinstall MS (2019) Monitoring surgical incision sites in orthopedic patients using an online physician-patient messaging platform. J Arthroplasty 34:1897–1900
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Fayed, A.M., Mansur, N.S.B., de Carvalho, K.A. et al. Artificial intelligence and ChatGPT in Orthopaedics and sports medicine. J EXP ORTOP 10, 74 (2023). https://doi.org/10.1186/s40634-023-00642-8
- Artificial intelligence
- Machine learning
- Distance mapping
- Statistical algorithm