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Journal of Clinical Orthopaedics and Trauma logoLink to Journal of Clinical Orthopaedics and Trauma
. 2020 Mar 18;11(Suppl 4):S491–S499. doi: 10.1016/j.jcot.2020.03.006

Impact of industry 4.0 to create advancements in orthopaedics

Mohd Javaid 1,∗∗, Abid Haleem 1
PMCID: PMC7394797  PMID: 32774017

Abstract

Scientists and health professional are focusing on improving the medical sciences for the betterment of patients. The fourth industrial revolution, which is commonly known as Industry 4.0, is a significant advancement in the field of engineering. Industry 4.0 is opening a new opportunity for digital manufacturing with greater flexibility and operational performance. This development is also going to have a positive impact in the field of orthopaedics. The purpose of this paper is to present various advancements in orthopaedics by the implementation of Industry 4.0. To undertake this study, we have studied the available literature extensively on Industry 4.0, technologies of Industry 4.0 and their role in orthopaedics. Paper briefly explains about Industry 4.0, identifies and discusses the major technologies of Industry 4.0, which will support development in orthopaedics. Finally, from the available literature, the paper identifies twelve significant advancements of Industry 4.0 in orthopaedics. Industry 4.0 uses various types of digital manufacturing and information technologies to create orthopaedics implants, patient-specific tools, devices and innovative way of treatment. This revolution is to be useful to perform better spinal surgery, knee and hip replacement, and invasive surgeries.

Keywords: Industry 4.0, Medical, Orthopaedics, Information, Treatment, Surgery

1. Introduction

Industry 4.0 is to provide intelligent orthopaedics with a combination of various advanced technologies. This industrial revolution easily handles the emergency and the proper management of healthcare service. It is applicable for social care service and integrated health. The advanced manufacturing and information technologies used in this revolution play an essential role in decision making during complex surgery. It makes innovative advancements in orthopaedics for better treatment and clinical outcomes. Industry 4.0 creates digitisation in the manufacturing process to meet customised demand. It enables digital support to orthopaedics doctors and surgeon by sharing the information and patient data.1,2

In this revolution, the Internet of Things (IoT) enabled smart manufacturing helps provide significant development for the manufacturing of orthopaedics implants, tools and devices. It also helps for quality control in the ongoing manufacturing of medical or other products. It will create a smart healthcare industry to change the way of treatment and surgery.

Industry 4.0 uses additive manufacturing as an essential component for the manufacturing of customised orthopaedics implants and tools. It helps the surgeon to perform pre-surgical planning and practice before the actual surgery. 3D digital images are used in this technology helps to improve implant positioning precisely. These technologies provide a new shape to the orthopaedics field with the innovative treatment and surgical procedure.3,4

Industry 4.0 improves the efficiency and quality in orthopaedics practice. It has a better ability to share the ideas and enhancement of creativity and brings together various software, sensors, smart machines and network connectivity to achieve a better result. Sensors are used to gain patient information and capable of checking blood pressure, sugar, glucose level and other related diseases. It correctly updates the orthopaedics devices as per the requirement of new diseases. The research and development is easily carryout by the predefined requirement.5,6 With the help of digital technologies, it creates automation in orthopaedics for quality practices and solutions.

2. What is industry 4.0?

The concept of Industry 4.0 was first introduced in 2011 to enhance the competitiveness and efficiency in manufacturing industries. There is better connectivity in the system using different sensors, Internet of Things, Artificial Intelligence (AI) and other innovative technologies. In production lines, these technologies are used to provide appropriate information and decision during the complexity. This revolution enhances the smart factory and smart manufacturing system to manufacture any customised products.7, 8, 9 Industry 4.0 creates interconnected advanced manufacturing and information system which can adequately communicate, analyse and provide useful information in the physical world. Previously three industrial revolutions have happened so far; these include Industry 1.0, Industry 2.0 and Industry 3.0. Fig. 1 shows the evolution of Industrial revolutions.

Fig. 1.

Fig. 1

Evolution of Industrial revolutions.

With the invention of the steam engine, Industry 1.0 introduced basic machines in the seventeen century. These machines helped human to use the manufacturing process. Industry 2.0 was introduced in the eighteenth century and provided a technological revolution with the introductions of mass production systems. Here electricity powered the machines, both basic and special purposes machines. Industry 3.0 occurred in nineteen century, which provided an automatic and digital revolution in the different fields. There was advance development in information and communication system. Now, Industry 4.0 has started, and we understand that this revolution will completely restructure the production and information processes to fulfil the customised requirements of the customer. Due to its customised capability, Industry 4.0 takes place extensive customization and thus suited to manufacture patient-specific bone implants, tools and devices and proper health monitoring system. It provides development in the existing products to create innovative changes in the market. It also creates innovative changes in the business and current digital trends. There are interconnectivity, automation and smart supply chain in the manufacturing industries. The technologies used in this revolution increases manufacturing efficiency and productivity.10, 11, 12 There are self-monitoring and flexibility in the manufacturing system to perform the required customised task. In the orthopaedics field, there are various requirements which can quickly be undertaken by Industry 4.0 in lesser time and cost.

3. Research objectives of the paper

There is an improvement in research and development in orthopaedics field with the applications of digital technologies. Industry 4.0 utilises an integrated network of IoT and computer-controlled system to create smart factories. It connects people, doctors, patient, processes, system, data and service with IoT to utilise the information for better collaboration. It helps to plan, monitor, track, trace and creates real-time alert of the disease level. Thus, Industry 4.0 creates flexibility during the manufacturing of any individualized part as per the requirement of the patient. It provides intelligent service to the patients and health professional.13,14 By using a cyber-physical system, it adopts a new business model in the field of orthopaedics. This industrial revolution may create an optimized operational and business process to improve patient satisfaction. There is a lesser wastage of materials and better capability to prevent ongoing error and delay. The four primary research objectives of the paper revolve around, how the introduction of industry 4.0 may bring change in the field of orthopaedics.

  • RO1: To study the requirements of Industry 4.0 in orthopaedics;

  • RO2: to identify major supportive technologies of Industry 4.0 for orthopaedics;

  • RO3: to study the significant benefits of Industry 4.0 in orthopaedics;

  • RO4: to identify advancements in orthopaedics with Industry 4.0.

4. Requirements of industry 4.0 in orthopaedics

Industry 4.0 to manufacture quality orthopaedics implants/parts/components as per the need of a patient. This industrial innovation uses electronic chips, wearable devices, smart medical devices and electronic health data to increase the overall performance of the healthcare. It uses innovative technologies like virtual reality/augmented reality for the training of orthopaedics doctors and surgeon. This helps to perform invasive and complex orthopaedics surgery without any risk. Artificial Intelligence can develop an understanding of the requirement of healthcare professional and patient. It allows detecting the disease or any other abnormalities at an earlier stage. This reduces the cost and time of the medical trial.

IoT is used to manage the medical devices automatically and provide better treatment process of the patient. Different digital technology has a high capacity to store and process data to impact the daily life of a human.15 This easily connects human to machine and machine to machine. By a proper digital health monitoring system, this provides advances in medicine, better health care and longer life expectation. Industry 4.0 quickly sorts out various new ongoing problems in healthcare and its associated field. It provides customised service with better safety and efficiency.16,17 This initiates to grow business by creating innovation in manufacturing and service sector. It enables to collaborate medical institutions with industry better.

5. Major supportive technologies of industry 4.0 for orthopaedics

Various technologies used in Industrial fourth revolution provide positive impacts in human health and environment as they also fulfil various requirements in orthopaedics. By the implementation of these digital technologies, there is an improvement in complex surgery. It improves the skill of the doctors in their specific field by providing proper learning, assisting research and development process.18,19 Table 1 pointwise elaborates, how the primary technologies of Industry 4.0 will support the broad areas of orthopaedics.

Table 1.

Major supportive technologies of Industry 4.0 for orthopaedics.

SNo Technologies Description References
1 Big data
  • Big data store large and complex data by the applications of IoT devices

  • Provide the best service in the orthopaedics field for providing an excellent health information system

  • A large amount of stored data for personalized and predictive medical treatment

  • Helps to store heterogeneous data of patients which may be unstructured, structured and semi-structured

  • Easily store large and complex data, which was not manageable by the previous various data storage techniques

  • Helpful to identify a wrong medicine, treatment, dose thus, to reduce the errors

Nishimura et al., 201620; Dimitrov et al., 201621; Ehrenstein et al., 201722; Fisher et al., 201823; Cahan et al., 201924
2 Machine learning
  • It uses a robust computational algorithm for massive data set processing

  • Undertakes an advanced scientific study of the statistical and algorithmic model using the computer system to perform specific tasks

  • Applied for the drug discovery process at an early stage and assess its helpfulness in precise treatment in orthopaedics

  • To play an essential role for billing, medical record and patient care

  • Make the orthopaedics field smart by minimizing administrative and supply costs

  • The significant applications of this technology would be the identification of disease, diagnosis, drug delivery, clinical trial, research and development and commercialization

Cabitza et al., 201825; Mohanty et al., 201826; Kuo et al., 201827; Gunaratne et al., 201928
3 Cloud computing
  • Stored data is processed at remote servers using computer sources

  • Customized applications of internet access to use the information stored in the cloud

  • Hospital and clinician can store the medical data to help patients and researchers

  • Deploys access and networked pieces of information and resources by offering on-demand computing using cloudases, this helps effective sharing of patient inform

  • In emergency cation with the doctor/surgeon at different locations

  • Easy connect of healthcare centres through the use of electronic documents and quickly providing support to physician enquiry, pharmacy order and patient management system

Whaiduzzaman et al., 201429; Khan et al., 201430; Griebel et al., 201531; Gao et al., 201832
4 Advance robotics
  • These are used to create innovation in the industry and many other fields, especially to perform a delicate and challenging task accurately

  • Robots can perform repetitive and dangerous tasks in difficult environmental conditions

  • Surgical robots and rehabilitation robots to be used to perform a variety of tasks in healthcare for human treatments

  • These robots help physicians at rural or remote locations to examine and give treatment to a patient

  • Useful to reliably supply meals, medications and other essential items to patients and the staff

  • A Robotic-assisted surgical system allows the surgeon to make precise, delicate motions while controlling the machine

  • It helps a surgeon to achieve some of the delicate surgeries in hard-to-reach places

Santello et al., 201633; Gifari et al., 201934; Bing et al., 201835; Zhang et al., 201836
5 Internet of Things
  • Here all devices used to perform the required tasks are connected to the internet which conveys the information appropriately

  • IoT helps to perform the world-class treatment and surgical procedure

  • Provide a guided course of action to improve the overall performance in the digitally controlled management system

  • Assist in efficient performing of successful bone, knee, hip and other treatment in lesser time

  • Track status of various diseases like heart failure, diabetes, blood pressure etc. during treatment

  • With the help of internet facilities, it maintains all clinical record, and in an emergency, it helps doctors and surgeon

Dimitrov et al., 201621; Silva et al., 201937; Lysogor et al., 201938; Homaei et al., 201939
6 Cyber-Physical Systems
  • This provides development to physical processes using proper monitoring of network and computer technology and involves the integration of networking, computation and physical processes

  • Doctors and researcher can better understand the problem and solve various challenges

  • Health practitioners can now coordinate better among all medical devices to ensure the safety of their interaction

  • Used for the research & development of highly reliable smart devices and tools

  • Improves patient safety with the help of networked physical devices

Bradley and Atkins, 201540; Lee, 201541; Dawson and Thomson, 201842; Burns et al., 201843; Labrado et al., 201944
7 Artificial Intelligence
  • Artificial Intelligence provides human-like intelligence to the devices with the help of available data by using algorithms and specialized software

  • Provides exceptional ability to improve physician accuracy and help him/her to focus on specific treatment of the patient

  • AI is integrated with other technologies (AR/VR/MR) to provides enhanced virtual support between doctors and patients

  • Increase the service quality in healthcare by improving task performance

  • Uses computer-based technique to suggest better alternatives for advance treatment, diagnosis and prediction

  • Useful for proper decision making during complicated cases

Olczak et al., 201745; Elkin et al., 201846; Gan et al., 201947; Han and Tian, 201948; Weng et al., 201949; Haleem et al., 202050
8
  • Video streaming

  • This is live streaming of video for improving the communications among the various stakeholders

  • Helps orthopaedics consultant to provide better supervision of surgeries in remote places

  • Video conferencing in local health service provides telemedicine, online consultation and virtual training supports as it is cost-effective

  • Helpful for remote locations in providing regular access to a medical specialist for treatment and surgery

  • Helps to track patient history for proper medical support

Kwon et al., 201651; Nguyen et al., 201852; Abenza et al., 201853; Wang et al., 201954
9
  • Cybersecurity

  • This technology provides security to software, hardware and any other electronics data/service from any theft using computer systems and networks

  • Connect internet, hospital network and other medical tools and devices to treat patients

  • Protects health care network databases to achieve promising results

  • Prevents electronic health information from any other unauthorized access and theft and reduces the risk factor

  • Provides a new network for the medical field for dealing with the security of sensitive healthcare information

Kramer et al., 201255; Papoutsi et al., 201556; Cobb et al., 201857; Veksler et al., 201858
10
  • 3D printing

  • It is a layer by layer addition of material to create parts from the input of computer-aided design (CAD) data

  • Useful for the manufacturing of customised implants, bone, prosthetics and pharmaceuticals

  • Used for the printing of stems cells, organs and tissues

  • Used for developing technology for skin printing

  • In orthopaedics, this technology is now available for bone tissue engineering

  • Useful to identify the proper bone defect and recreation the missing part of the patient body

  • Useful for surgical planning and intervention in complex cases

Vaishya et al., 201859; Lal and Patralekh, 201860; Javaid and Haleem, 201861; Fang et al., 201962; Wang et al., 202063
11
  • Wireless brain sensors

  • Wireless brain sensors are used to check brain-related disease, sleep disorder, dementia, traumatic brain injury and other brain-related conditions

  • Parkinson’s disease can check effectively

  • Helpful to improve cognitive functionalities and proper monitoring of neurological fluctuations

  • Used to monitor brain temperature, intracranial pressure and record signalling of the brain using brain waves

  • Easy checking of intracranial pressure of the brain and inside the skull

  • Helpful to identify the risk of infection through the skin/organs

Klosterhoff et al., 201764; Cui, 201765; Han et al., 201866; Park et al., 201967
12
  • Nanomedicine

  • Nanomedicine applies the knowledge of nanotechnology for the treatment and prevention of disease

  • Uses nanoscale material having biocompatible nanoparticles for a living organism

  • Helpful for diagnosis, delivery and sense regarding the medical treatment of the patient

  • Successfully applied for the treatment of cancer and another clinical research

  • Used for tissue engineering to repair/reshape the damaged tissue

  • Suitable for enhancing the process of manufacturing of scaffold and used as a growth factor

  • Used for the development of analytical tools, drug delivery, diagnostic devices and physical therapy applications

Armstead and Li, 201168; Mazaheri et al., 201569; Sweeney, 201570; Smith et al., 201871

These technologies are used to track patient health records and medical history. Diagnosis is made in a better manner, by viewing the patient’s health picture holistically. It helps develop a health information system and personalized orthopaedics treatment. Patient data are stored digitally, which helps to reduces errors in the treatment procedure. Computer sources are used to connect all documents electronically for proper patient management. Advanced robots are used to assist total knee arthroplasty.72 Industry 4.0 uses IoT technology, by which all devices are connected to the internet which provides appropriate information. This information can help proper decision making by using artificial intelligence.73 By the successful implementation of these technologies, orthopaedics practitioners can increase their confidence and make an innovative way of treatments to provide better satisfaction to the patients.

6. Significant benefits of industry 4.0 for orthopaedics

Industry 4.0 creates transparency in information, creates better technical assistance and decision making. Smart sensors are used to collect real-time data during medical processes. In the manufacturing of required orthopaedics parts, the traditional method of manufacturing will be replaced with cheaper, faster and with better quality automated systems.2,74 Fig. 2 shows the major benefits of Industry4.0 for orthopaedics.

Fig. 2.

Fig. 2

Major benefits of Industry 4.0 for orthopaedics.

In orthopaedics, Industry 4.0 is used to make successful treatments by the applications of real-time data. Thus, research, development and commercialization process get quicker, and the teaching and learning process becomes better.75,76

7. Advancements of industry 4.0 in orthopaedics

Customization is the primary challenge taken by Industry 4.0. It creates an adequate physical environment with the help of connected and automated devices and is used to focus on the patient-specific health system, clinical setting and a better outcome. Industry 4.0 also helps to reduce waste and time in the manufacturing of any product. This reduces the critical risk to enhance the capability of doctors and the whole hospital management system.17,77 It is used for safety, medical device regulation and privacy protection by the properly self-managing information system. Table 2 discusses the significant advancements of Industry 4.0 in orthopaedics.

Table 2.

Major advancements of Industry 4.0 in orthopaedics.

S No Advancements Description References
1 Customised treatment
  • This revolution is popular as it provides customization, by which individual requirements of patients can be assessed & fulfilled in lesser time

  • In orthopaedics, there is an essential requirement of customised patient-specific implants which now are quickly manufactured by using AM/3D printing technology

  • Perform customised patient treatment with advanced and digital technologies

  • Improves treatment quality with the proper management of medical devices

  • Used to address various symptoms, the discomfort of the legs and feet

  • Customised treatment of foot deformities and other bone injuries

Chen et al., 201278; Mok et al., 201679; Javaid and Haleem, 201880; Padilla-Castañeda et al., 201881
2 Real-time information and its management
  • All data and information are stored digitally, which reduces the paperwork

  • 24 × 7 access to information for better treatment

  • There is a reduction in inventory and inventory holding cost, as patient-specific implants and devices are printed only at the time of requirement

  • Provides proper information about bone fracture which helps to improve the surgical procedure and better recovery of the patient

  • Study and management of the injury during any sports activities

  • Proper navigation, tracking and management of data for better analysis

Çetinkaya et al., 201782; Qudsi et al., 201883; Lübbeke, 201884; Lindsey et al., 201885
3 Digitisation/Intelligence in manufacturing
  • Industry 4.0 converts the information of non-digital format to digital formats

  • In orthopaedics, it helps to intelligently create innovation in the manufacturing of the required part in lesser time and cost

  • Intelligently provides the opportunity of auto-check and adopt the better problem-solving technique

  • Sensors provide proper feedback of manufacturing and other maintenance requirements

  • Ability to increase the efficiency of treatment with the proper communication system

  • Fulfil the challenges automatically during any design changes of product/orthopaedics part

Vavken et al., 201586; Assaf et al., 201687; Jennings et al., 201688; Kubicek et al., 201989
4 Planning and decision making
  • Industry 4.0 uses various innovative technologies for planning and decision making during the complex orthopaedics cases

  • These technologies provide fundamental changes to increase the capabilities of researchers, doctors and surgeons

  • Create a flexible network using a cyber-physical system to increase self-optimising performance

  • There is a massive exchange of real-time data for the decision making and proper management of ongoing situations

  • Innovative information technologies used in this revolution reduces the errors of planning and decision-making process

Land et al., 201790; Khan and Muehlschlegel, 201891; Boland et al., 201992; Ierano et al., 201993
5 Transparency
  • Advance information technologies are used to provide transparency during the treatment

  • Useful for advanced health monitoring system with proper treatment

  • Create transparency to make a proper decision of surgery with lesser risk

  • Opens a truthful communication between doctors, patient and family

Karam et al., 201294; Duymus et al., 201795; Sener et al., 201996; Sobel et al., 201997
6 Health monitoring
  • A microcontroller is used to track and access data for proper monitoring of health parameters

  • Proper monitoring of body temperature, blood pressure and heart rate using advanced sensors

  • Used to suggest the actions on patient conditions based on the integrated algorithms

  • Notifies specialists during emergency cases

  • Develop a useful tool for a health monitoring system to save human lives

  • Provides actionable intelligence during the complicated cases

Naslund et al., 201798; Ten Haken et al., 201899; Weiss, 2019100; Hall et al., 2019101; Krick et al., 2019102
7 Risk management
  • New innovative technologies can identify the diseases at an early stage, which helps to reduce the risk of patient

  • Due to the higher capacity of digital data storage, there is better management of risk at many stages of treatment

  • Better selection of a strategy for patient safety and other financial losses

  • Analyses the dangerous side effect during the specific medical condition of the patient

  • Keep accurate patient background information of the disease to reduce risk during treatment

Morris et al., 2003103; Etges et al., 2018104; Chen, 2018105; Jafari et al., 2018106
8 Intricate designing of orthopaedics tools
  • In orthopaedics, there is an essential requirement of intricate design to make high-quality products

  • Various software, 3D scanners are used to improve the design of tools and devices as being used in this field

  • New technologies provide a positive impact on the development and innovation of the product

  • Due to its ability, the surgeon can perform bone surgery precisely in lesser cost and time

  • With the applications of various designing software and scanning technology, it can design and develop complex and intricate designs

Gallo et al., 2014107; Dorozhkin et al., 2015108; Groen et al., 2017109; Martinez-Marquez et al., 2018110
9 Patient-specific implants
  • Designing and printing of patient-specific implants is the primary challenge in the orthopaedics field and undertakes this challenge by the applications of Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) and 3D printing technology

  • Scanning technologies are used to capture data of the patient and 3D printing easily print this data in lesser time and cost

  • Provide an innovative solution with the help of medical devices and service

  • These technologies are also applicable for custom fit prosthetic to create comfort to the patient

MacBarb et al., 2017111; Haleem and Javaid, 2018112; Mangano et al., 2020113; Memari et al., 2020114
10 Increase the performance of the surgeon
  • The surgeon can make a brief idea of the surgery before even performing

  • These technologies are used to perform mock surgery on the patient-specific bone model to increase surgical performance

  • During surgery, sensors are used to measure surgeon movement to make it successful

  • Suggests necessary procedure to increase the quality of treatment

Liu et al., 2014115; Barrett et al., 2019116; Apramian et al., 2018117; Dalager et al., 2019118
11 Rapid development
  • This revolution uses advance manufacturing technology which can easily create rapid development in orthopaedics tools and devices

  • All information’s and processes are digitally controlled to create advancement rapidly

  • Helpful to execute post-trauma surgeries using patient-specific implants

  • Uses 3D printing to create a prototype which creates rapid development in the field of orthopaedics

  • Research and development of product become more accessible with the use of advanced manufacturing and information technologies

Munshi et al., 2012119; Fayaz et al., 2013120; Hoang et al., 2016121; Zhou, 2017122
12 Daily patient routine
  • Industry 4.0 provides innovative opportunities to follow a daily routine to stay fit

  • Helps patients to follow up proper exercise in day to day life

  • Suggest proper food, nutrition service and proper menu to the patient

  • Predicts motion of the fractured bone and helps treatment at home with proper exercise

  • The patient can learn better activities and follow up on the daily routine and daily medication

Al Shahraniet al., 2018123; Gershengorn et al., 2018124; Van der Willik et al., 2019125; Kirk et al., 2019126

Industry 4.0 is expected to create major advancements in the field of orthopaedics. This revolution is to fulfil the demand for customised implants, tools and devices quickly as per the individual requirements. It provides real-time information and its proper management during an emergency. It is helpful for planning and decision making of a complicated case. It creates transparency among the doctors, surgeons and patients to avoid any confusion. It is used for the proper monitoring of health by managing all risk factors. These technologies are useful for design and manufacturing of patient-specific implants to create rapid development in the field of orthopaedics.127,128 It suggests routine exercise and medication for the patient to stay fit.

Industry 4.0 uses different computational algorithms for prediction of diseases and surgical outcomes. This revolution uses AI technology to provide improvement in the imaging pathway and medical recordkeeping. It can be used to detect a fracture in the wrist, hand and ankle. Industry 4.0 will become an important revolution for total knee arthroplasty, unilateral knee arthroplasty and total hip arthroplasty.129,130 This will create significant advancement to perform orthopaedics surgery in a better way. Sensor-based smart implants can provide real-time information to surgeons throughout the entire treatment process. These implants are to identify the ongoing problems and help perform a unique procedure. Physical orthopaedics devices would be digitally interconnected to exchange data by internet sources. It enhances communication in healthcare practice to manage, tracks and control medical supplies to perform the specific procedure. Industry 4.0 can be used to effectively track chronic diseases so that the patient receives proper treatment and timely care. Overall it seems to fulfil the difficult challenges of orthopaedics.

7.1. Significant contributions of the study

Industry 4.0 provides rapid changes in manufacturing reality. It is the combination of significant technological innovation which integrates and interlink with one another. The customized prosthetics are made as per the requirements of the patient. These revolutions mixed manufacturing and new industrial practice in the technological world. Hospital staff can access everything and information when they required. It handles all ongoing activities in healthcare to increase safety and quality of the patient life. This increase the efficiency and innovation of the whole health management process. It holds excellent promising applications to identify high-risk patients through appropriate screening. The significant contributions of this paper are as under:

  • Industry 4.0 is the fourth industrial revolution used to meet the customised demand of the customer

  • Focus on ‘on-demand manufacturing’ and the need for rapid change in the production processes of medical and orthopaedics products

  • In orthopaedics, this revolution will create implants, tool, devices and another instrument as per patient match and also manage all ongoing activities during the treatment

  • Industry 4.0 is implemented in orthopaedics to fulfil various innovative requirements like performance, efficiency, complex orthopaedics surgery without any error or any risk

  • Major supportive technologies of Industry 4.0 for orthopaedics are Big data, Machine learning, Cloud computing, Advance robotics, Internet of Things, Cyber-Physical Systems, Artificial Intelligence, Video streaming, Cybersecurity, 3D printing, Wireless brain sensors and Nanomedicine

  • The major benefits of Industry 4.0 in orthopaedics is the easy availability of real-time data, maximize patient outcome, orthopaedics research, improve treatment quality, connected information, automation, innovative teaching and learning process

  • Industry 4.0 creates various advancements in orthopaedics like Customised treatment, Real-time information and its management, Digitisation/Intelligence in manufacturing, Planning and decision making, Transparency, Health monitoring, Risk management, Intricate designing of orthopaedics tools, Patient-specific implants, Increase performance of surgeon, Rapid development and Daily patient routine

  • In future, Industry 4.0 will bring major changes in orthopaedics by changing the way of information, treatment and surgical procedure more efficiently

8. Future scope

Industry 4.0 will provide vast development in orthopaedics by the applications of digital technologies. Smart machines used in this revolution can precisely capture and communicate real-time data for a better decision-making process. It rapidly brings new innovative orthopaedics tools which will reduce surgical risks. Patients can gain their knowledge, and upcoming devices will be helpful for the prevention of diseases at an early stage. Industry 4.0 will emerge in bioelectronics medicine which will be helpful for better treatment of illness. This can easily be performed robot assist surgery precisely in lesser time. In the upcoming years, Industry 4.0 will provide new opportunities and innovative treatment for patient care.

9. Conclusion

Industry 4.0 adopts new technologies to improve competitiveness in the manufacturing of the product. It offers significant potential to provide innovative changes in treatment and surgical procedure. With the advancement in automation and digitisation, it provides a flexible solution in the field of orthopaedics. These new technologies suggest proper exercise for the active motion of bones. It focuses on optimising the time of hospital management system. This revolution provides the development of predictive and personalized services. It has an excellent capability for better information system and diverse experience to the doctors. This efficiently manages the day to day quality process and performance of the orthopaedics surgeon. It quickly minimizes the risk and time-consuming activities during the bone fracture of the patient. This revolution appropriately manages the clinical and personal data of a patient, which helps to support the decision-making process. It provides awareness to the doctors, surgeon and monitors all activities during the whole treatment process. This brings significant development for personalized patient treatment by storing a tremendous amount of background patient data. It creates development for treatment, new drug and improves the quality of healthcare. This makes the production line more efficient by effective utilization for all the resources. In upcoming years, Industry 4.0 will open new possibilities in the field of orthopaedics by the implementation of advanced automation technologies and processes.

Declaration of competing interest

None.

Contributor Information

Mohd Javaid, Email: mjavaid@jmi.ac.in.

Abid Haleem, Email: ahaleem@jmi.ac.in.

References

  • 1.Haleem A., Javaid M., Vaishya R. Industry 4.0 and its applications in orthopaedics. J Clin Orthop Trauma. 2019;10(3):615–616. doi: 10.1016/j.jcot.2018.09.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Chute C., French T. Introducing care 4.0: an integrated care paradigm built on industry 4.0 capabilities. Int J Environ Res Publ Health. 2019;16(12):2247. doi: 10.3390/ijerph16122247. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Chen B., Wan J., Shu L., Li P., Mukherjee M., Yin B. Smart factory of industry 4.0: key technologies, application, and challenges. IEEE Access. 2017;6:6505–6519. [Google Scholar]
  • 4.Villalba-Diez J., Schmidt D., Gevers R., Ordieres-Meré J., Buchwitz M., Wellbrock W. Deep learning for industrial computer vision quality control in the printing industry 4.0. Sensors. 2019;19(18):3987. doi: 10.3390/s19183987. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Schütze A., Helwig N., Schneider T. Sensors 4.0—smart sensors and measurement technology enable Industry 4.0. J. Sens. Sens. Syst. 2018;7:359–371. [Google Scholar]
  • 6.Villalba-Diez J., Zheng X., Schmidt D., Molina M. Characterization of industry 4.0 lean management problem-solving behavioral patterns using EEG sensors and deep learning. Sensors. 2019;19(13):2841. doi: 10.3390/s19132841. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Weyer S., Schmitt M., Ohmer M., Gorecky D. Towards Industry 4.0-Standardization as the crucial challenge for highly modular, multi-vendor production systems. Ifac-Papers online. 2015;48:579–584. [Google Scholar]
  • 8.Lu J. Industry 4.0: a survey on technologies, applications and open research issues. J. Ind. Inf. Integr. 2017;6:1–10. [Google Scholar]
  • 9.Xu L.D., Xu E.L., Li E. Industry 4.0: state of the art and future trends. Int J Prod Res. 2018;56:2941–2962. [Google Scholar]
  • 10.Zawadzki P., Żywicki K. Smart product design and production control for effective mass customization in the industry 4.0 concept. Manag Prod Eng Rev. 2016;7:105–112. [Google Scholar]
  • 11.Angelopoulos A., Michailidis E.T., Nomikos N. Tackling faults in the industry 4.0 era-A survey of machine-learning solutions and key aspects. Sensors. 2019;20(1):109. doi: 10.3390/s20010109. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Haleem A., Javaid M. Additive manufacturing applications in industry 4.0: a review. Journal of Industrial Integration and Management. 2019 doi: 10.1142/S2424862219300011. [DOI] [Google Scholar]
  • 13.Trappey A.J.C., Trappey C.V., Govindarajan U.H., Chuang A.C., Sun J.J. A review of essential standards and patent landscapes for internet of Things: a key enabler for industry 4.0. Adv Eng Inf. 2017;33:208–229. [Google Scholar]
  • 14.García-Garza M.A., Ahuett-Garza H., Lopez M.G. A case about the upgrade of manufacturing equipment for insertion into an industry 4.0 environment. Sensors. 2019;19(15):3304. doi: 10.3390/s19153304. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Pieroni A., Scarpato N., Brilli M. Industry 4.0 revolution in autonomous and connected vehicle a now-conventional approach to manage Big Data. J Theor Appl Inf Technol. 2018;96:10–18. [Google Scholar]
  • 16.Silva M., Vieira E., Signoretti G., Silva I., Silva D., Ferrari P. A customer feedback platform for vehicle manufacturing compliant with industry 4.0 vision. Sensors. 2018;18(10):3298. doi: 10.3390/s18103298. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Javaid M., Haleem A. Industry 4.0 applications in medical field: a brief review. Current Medicine Research and Practice. 2019;9(3):102–109. [Google Scholar]
  • 18.Stock T., Seliger G. Opportunities of sustainable manufacturing in industry 4.0. Procedia CIRP. 2016;40:536–541. [Google Scholar]
  • 19.Zheng X., Wang M., Ordieres-Mere J. Comparison of data preprocessing approaches for applying deep learning to human activity recognition in the context of industry 4.0. Sensors. 2018;18:2146. doi: 10.3390/s18072146. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Nishimura A., Nishimura K., Kada A., Iihara K. J-ASPECT study GROUP. Status and future perspectives of utilizing big data in neurosurgical and stroke research. Neurol Med -Chir. 2016;56(11):655–663. doi: 10.2176/nmc.ra.2016-0174. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Dimitrov D.V. Medical internet of Things and big data in healthcare. Healthc Inform Res. 2016;22(3):156–163. doi: 10.4258/hir.2016.22.3.156. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Ehrenstein V., Nielsen H., Pedersen A.B., Johnsen S.P., Pedersen L. Clinical epidemiology in the era of big data: new opportunities, familiar challenges. Clin Epidemiol. 2017;9:245–250. doi: 10.2147/CLEP.S129779. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Fisher C.B., Layman D.M. Genomics, big data, and broad consent: a new ethics frontier for prevention science. Prev Sci. 2018;19(7):871–879. doi: 10.1007/s11121-018-0944-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Cahan E.M., Hernandez-Boussard T., Thadaney-Israni S., Rubin D.L. Putting the data before the algorithm in big data addressing personalized healthcare. NPJ Digit Med. 2019;2:78. doi: 10.1038/s41746-019-0157-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Cabitza F., Locoro A., Banfi G. Machine learning in orthopedics: a literature review. Front BioengBiotechnol. 2018;6:75. doi: 10.3389/fbioe.2018.00075. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Mohanty R., Sinha A.M., Remsik A.B. Early findings on functional connectivity correlates of behavioral outcomes of brain-computer interface stroke rehabilitation using machine learning. Front Neurosci. 2018;12:624. doi: 10.3389/fnins.2018.00624. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Kuo C.Y., Yu C.L., Chen H.C., Chan C.L. Comparison of models for the prediction of medical costs of spinal fusion in taiwan diagnosis-related groups by machine learning algorithms. Healthc Inform Res. 2018;24(1):29–37. doi: 10.4258/hir.2018.24.1.29. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Gunaratne R., Monteath I., Goncalves J. Machine learning classification of human joint tissue from diffuse reflectance spectroscopy data. Biomed Optic Express. 2019;10(8):3889–3898. doi: 10.1364/BOE.10.003889. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Whaiduzzaman M., Haque M.N., Karim Rejaul, Chowdhury M., Gani A. A study on strategic provisioning of cloud computing services. Sci World J. 2014;2014 doi: 10.1155/2014/894362. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Khan S., Shiraz M., Wahab A.W., Gani A., Han Q., Rahman Z.B. A comprehensive review on adaptability of network forensics frameworks for mobile cloud computing. Sci World J. 2014;2014 doi: 10.1155/2014/547062. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Griebel L., Prokosch H.U., Köpcke F. A scoping review of cloud computing in healthcare. BMC Med Inform DecisMak. 2015;15:17. doi: 10.1186/s12911-015-0145-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Gao F., Thiebes S., Sunyaev A. Rethinking the meaning of cloud computing for health care: a taxonomic perspective and future research directions. J Med Internet Res. 2018;20(7) doi: 10.2196/10041. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Santello M., Bianchi M., Gabiccini M. Hand synergies: integration of robotics and neuroscience for understanding the control of biological and artificial hands. Phys Life Rev. 2016;17:1–23. doi: 10.1016/j.plrev.2016.02.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Gifari M.W., Naghibi H., Stramigioli S., Abayazid M. A review on recent advances in soft surgical robots for endoscopic applications. Int J Med Robot. 2019;15(5) doi: 10.1002/rcs.2010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Bing Z., Meschede C., Röhrbein F., Huang K., Knoll A.C. A survey of robotics control based on learning-inspired spiking neural networks. Front Neurorob. 2018;12:35. doi: 10.3389/fnbot.2018.00035. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Zhang K., Chen X., Liu F., Tang H., Wang J., Wen W. System framework of robotics in upper limb rehabilitation on poststroke motor recovery. Behav Neurol. 2018;2018 doi: 10.1155/2018/6737056. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Silva J.C., Rodrigues J.J.P.C., Al-Muhtadi J., Rabêlo R.A.L., Furtado V. Management platforms and protocols for internet of Things: a survey. Sensors. 2019;19(3):676. doi: 10.3390/s19030676. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Lysogor I., Voskov L., Rolich A., Efremov S. Study of data transfer in a heterogeneous LoRa-satellite network for the internet of remote Things. Sensors. 2019;19(15):3384. doi: 10.3390/s19153384. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Homaei M.H., Salwana E., Shamshirband S. An enhanced distributed data aggregation method in the internet of Things. Sensors. 2019;19(14):3173. doi: 10.3390/s19143173. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Bradley J.M., Atkins E.M. Optimization and control of cyber-physical vehicle systems. Sensors. 2015;15(9):23020–23049. doi: 10.3390/s150923020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Lee E.A. The past, present and future of cyber-physical systems: a focus on models. Sensors. 2015;15(3):4837–4869. doi: 10.3390/s150304837. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Dawson J., Thomson R. The future cybersecurity workforce: going beyond technical skills for successful cyber performance. Front Psychol. 2018;9:744. doi: 10.3389/fpsyg.2018.00744. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Burns M., Manganelli J., Wollman D. Elaborating the human aspect of the NIST framework for cyber-physical systems. Proc Hum Factors Ergon Soc Annu Meet. 2018;62(1):450–454. doi: 10.1177/1541931218621103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Labrado C., Thapliyal H., Prowell S., Kuruganti T. Use of thermistor temperature sensors for cyber-physical system security. Sensors. 2019;19(18):3905. doi: 10.3390/s19183905. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Olczak J., Fahlberg N., Maki A. Artificial intelligence for analyzing orthopaedic trauma radiographs. Acta Orthop. 2017;88(6):581–586. doi: 10.1080/17453674.2017.1344459. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Elkin P.L., Schlegel D.R., Anderson M., Komm J., Ficheur G., Bisson L. Artificial intelligence: bayesian versus heuristic method for diagnostic decision support. Appl Clin Inf. 2018;9(2):432–439. doi: 10.1055/s-0038-1656547. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Gan K., Xu D., Lin Y. Artificial intelligence detection of distal radius fractures: a comparison between the convolutional neural network and professional assessments. Acta Orthop. 2019;90(4):394–400. doi: 10.1080/17453674.2019.1600125. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Han X.G., Tian W. Artificial intelligence in orthopaedic surgery: current state and future perspective. Chin Med J. 2019;132(21):2521–2523. doi: 10.1097/CM9.0000000000000479. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Weng C.H., Wang C.L., Huang Y.J. Artificial intelligence for automatic measurement of sagittal vertical Axis using ResUNet framework. J Clin Med. 2019;8(11):1826. doi: 10.3390/jcm8111826. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Haleem A., Vaishya R., Javaid M., Khan M.I. Artificial Intelligence (AI) applications in orthopaedics: an innovative technology to embrace. J ClinOrthop Trauma. 2020;11:580–581. doi: 10.1016/j.jcot.2019.06.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Kwon D., Je H., Kim H., Ju H., An D. Scalable Video streaming relay for smart mobile devices in wireless networks. PloS One. 2016;11(12) doi: 10.1371/journal.pone.0167403. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Nguyen D.V., Le T.T., Lee S., Ryu E.S. SHVC tile-based 360-degree Video streaming for mobile VR: PC offloading over mmWave. Sensors. 2018;18(11):3728. doi: 10.3390/s18113728. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Abenza P.P.G., Malumbres M.P., Piñol P., López-Granado O. Source coding options to improve HEVC Video streaming in vehicular networks. Sensors. 2018;18(9):3107. doi: 10.3390/s18093107. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Wang X., Zhang W., Gao X., Wang J., Du H., Zheng Q. Toward cost-effective mobile Video streaming through environment-aware watching state prediction. Sensors. 2019;19(17):3654. doi: 10.3390/s19173654. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Kramer D.B., Baker M., Ransford B. Security and privacy qualities of medical devices: an analysis of FDA postmarket surveillance. PloS One. 2012;7(7) doi: 10.1371/journal.pone.0040200. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Papoutsi C., Reed J.E., Marston C., Lewis R., Majeed A., Bell D. Patient and public views about the security and privacy of Electronic Health Records (EHRs) in the UK: results from a mixed-methods study. BMC Med Inform DecisMak. 2015;15:86. doi: 10.1186/s12911-015-0202-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Cobb C., Sudar S., Reiter N., Anderson R., Roesner F., Kohno T. Computer security for data collection technologies. Dev Eng. 2018;3:1–11. doi: 10.1016/j.deveng.2017.12.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Veksler V.D., Buchler N., Hoffman B.E., Cassenti D.N., Sample C., Sugrim S. Simulations in cyber-security: a review of cognitive modeling of network attackers, defenders, and users. Front Psychol. 2018;9:691. doi: 10.3389/fpsyg.2018.00691. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Vaishya R., Patralekh M.K., Vaish A., Agarwal A.K., Vijay V. Publication trends and knowledge mapping in 3D printing in orthopaedics. J ClinOrthop Trauma. 2018;9(3):194–201. doi: 10.1016/j.jcot.2018.07.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Lal H., Patralekh M.K. 3D printing and its applications in orthopaedic trauma: a technological marvel. J ClinOrthop Trauma. 2018;9(3):260–268. doi: 10.1016/j.jcot.2018.07.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.Javaid M., Haleem A. Additive manufacturing applications in medical cases: a literature-based review. Alexandria Journal of Medicine. 2018;54(4):411–422. [Google Scholar]
  • 62.Fang C., Cai H., Kuong E. Surgical applications of three-dimensional printing in the pelvis and acetabulum: from models and tools to implants. Unfallchirurg. 2019;122(4):278–285. doi: 10.1007/s00113-019-0626-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Wang C., Huang W., Zhou Y. 3D printing of bone tissue engineering scaffolds. Bioact Mater. 2020;5(1):82–91. doi: 10.1016/j.bioactmat.2020.01.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64.Klosterhoff B.S., Tsang M., She D. Implantable sensors for regenerative medicine. J Biomech Eng. 2017;139(2) doi: 10.1115/1.4035436. 0210091–02100911. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65.Cui Y. Wireless biological electronic sensors. Sensors. 2017;17(10):2289. doi: 10.3390/s17102289. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Han S., Kim J., Won S.M. Battery-free, wireless sensors for full-body pressure and temperature mapping. Sci Transl Med. 2018;10(435) doi: 10.1126/scitranslmed.aan4950. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67.Park Y.G., Lee S., Park J.U. Recent progress in wireless sensors for wearable electronics. Sensors. 2019;19(20):4353. doi: 10.3390/s19204353. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68.Armstead A.L., Li B. Nanomedicine as an emerging approach against intracellular pathogens. Int J Nanomed. 2011;6:3281–3293. doi: 10.2147/IJN.S27285. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Mazaheri M., Eslahi N., Ordikhani F., Tamjid E., Simchi A. Nanomedicine applications in orthopaedic medicine: state of the art. Int J Nanomed. 2015;10:6039–6053. doi: 10.2147/IJN.S73737. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.Sweeney A.E. Nanomedicine concepts in the general medical curriculum: initiating a discussion. Int J Nanomed. 2015;10:7319–7331. doi: 10.2147/IJN.S96480. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71.Smith W.R., Hudson P.W., Ponce B.A., RajaramManoharan S.R. Nanotechnology in orthopedics: a clinically oriented review. BMC Muscoskel Disord. 2018;19(1):67. doi: 10.1186/s12891-018-1990-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 72.Sodhi N., Khlopas A., Piuzzi N.S. The learning curve associated with robotic total knee arthroplasty. J Knee Surg. 2018;31(1):17–21. doi: 10.1055/s-0037-1608809. [DOI] [PubMed] [Google Scholar]
  • 73.Zhe L., Wang K.S. Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers: industry 4.0 scenario. Adv Manuf. 2017;5:377–387. [Google Scholar]
  • 74.Aazam M., Zeadally S., Harras K.A. Deploying fog computing in industrial internet of Things and industry 4.0. IEEE Trans. Ind. Inf. 2018;14:4674–4682. [Google Scholar]
  • 75.Diez-Olivan A., Del Ser J., Galar D., Sierra B. Data fusion and machine learning for industrial prognosis: trends and perspectives towards industry 4.0. Inf Fusion. 2019;50:92–111. [Google Scholar]
  • 76.Haleem A., Javaid M. Industry 5.0 and its applications in orthopaedics. Journal of Clinical Orthopaedics and Trauma. 2019;10(4):807–808. doi: 10.1016/j.jcot.2018.12.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 77.Alcácer V., Cruz-Machado V. Scanning the industry 4.0: a literature review on technologies for manufacturing systems. Eng. Sci. Technol. Int. J. 2019 doi: 10.1016/j.jestch.2019.01.006. [DOI] [Google Scholar]
  • 78.Chen Y.X., Zhang K., Hao Y.N., Hu Y.C. Research status and application prospects of digital technology in orthopaedics. Orthop Surg. 2012;4(3):131–138. doi: 10.1111/j.1757-7861.2012.00184.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 79.Mok S.W., Nizak R., Fu S.C. From the printer: potential of three-dimensional printing for orthopaedic applications. J OrthopTranslat. 2016;6:42–49. doi: 10.1016/j.jot.2016.04.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 80.Javaid M., Haleem A. Additive manufacturing applications in orthopaedics: a review. J ClinOrthop Trauma. 2018;9(3):202–206. doi: 10.1016/j.jcot.2018.04.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 81.Padilla-Castañeda M.A., Sotgiu E., Barsotti M. An orthopaedic robotic-assisted rehabilitation method of the forearm in virtual reality physiotherapy. JHealthc Eng. 2018;2018 doi: 10.1155/2018/7438609. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 82.Çetinkaya E., Çift H., Aybar A., Erçin E., Güler G.B., Poyanlı O. The timing and importance of motor skills course in knee arthroscopy training. ActaOrthopTraumatolTurc. 2017;51(4):273–277. doi: 10.1016/j.aott.2017.01.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 83.Qudsi R.A., Roberts H.J., Bhashyam A.R. A self-reported needs assessment survey of pediatric orthopaedic education in Haiti. J Surg Educ. 2018;75(1):140–146. doi: 10.1016/j.jsurg.2017.06.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 84.Lübbeke A. Research methodology for orthopaedic surgeons, with a focus on outcome. EFORT Open Rev. 2018;3(5):160–167. doi: 10.1302/2058-5241.3.170064. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 85.Lindsey R., Daluiski A., Chopra S. Deep neural network improves fracture detection by clinicians. Proc Natl AcadSci U S A. 2018;115(45):11591–11596. doi: 10.1073/pnas.1806905115. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 86.Vavken P., Ganal-Antonio A.K., Quidde J., Shen F.H., Chapman J.R., Samartzis D. Fundamentals of clinical outcomes assessment for spinal disorders: clinical outcome instruments and applications. Global Spine J. 2015;5(4):329–338. doi: 10.1055/s-0034-1396046. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 87.Assaf D., Amar E., Marwan N., Neuman Y., Salai M., Rath E. Dynamic patterns of expertise: the case of orthopedic medical diagnosis. PloS One. 2016;11(7) doi: 10.1371/journal.pone.0158820. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 88.Jennings J.D., Ciaravino S.G., Ramsey F.V., Haydel C. Physicians’ attire influences patients’ perceptions in the urban outpatient orthopaedic surgery setting. Clin Orthop Relat Res. 2016;474(9):1908–1918. doi: 10.1007/s11999-016-4855-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 89.Kubicek J., Tomanec F., Cerny M., Vilimek D., Kalova M., Oczka D. Recent trends, technical concepts and components of computer-assisted orthopedic surgery systems: a comprehensive review. Sensors. 2019;19(23):5199. doi: 10.3390/s19235199. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 90.Land V., Parry R., Seymour J. Communication practices that encourage and constrain shared decision making in health-care encounters: systematic review of conversation analytic research. Health Expect. 2017;20(6):1228–1247. doi: 10.1111/hex.12557. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 91.Khan M.W., Muehlschlegel S. Shared decision making in NeurocriticalCare. Neurosurg Clin. 2018;29(2):315–321. doi: 10.1016/j.nec.2017.11.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 92.Boland L., Graham I.D., Légaré F. Barriers and facilitators of pediatric shared decision-making: a systematic review. Implement Sci. 2019;14(1):7. doi: 10.1186/s13012-018-0851-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 93.Ierano C., Thursky K., Peel T., Rajkhowa A., Marshall C., Ayton D. Influences on surgical antimicrobial prophylaxis decision making by surgical craft groups, anaesthetists, pharmacists and nurses in public and private hospitals. PloS One. 2019;14(11) doi: 10.1371/journal.pone.0225011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 94.Karam M.D., Kho J.Y., Yehyawi T.M. Application of surgical skill simulation training and assessment in orthopaedic trauma. Iowa Orthop J. 2012;32:76–82. [PMC free article] [PubMed] [Google Scholar]
  • 95.Duymus T.M., Karadeniz H., Çaçan M.A. Internet and social media usage of orthopaedic patients: a questionnaire-based survey. World J Orthoped. 2017;8(2):178–186. doi: 10.5312/wjo.v8.i2.178. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 96.Sener M., Davulcu C.D., Tahta M., Gunal I. Predatory journal preference in the field of Orthopaedics and Traumatology in Turkey. ActaOrthopTraumatolTurc. 2019;53(5):390–393. doi: 10.1016/j.aott.2019.05.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 97.Sobel A.D., Hartnett D., Hernandez D., Eltorai A.E.M., Daniels A.H. Global variability in orthopedic surgery training. Orthop Rev. 2019;11(3):8152. doi: 10.4081/or.2019.8152. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 98.Naslund J.A., Aschbrenner K.A., Kim S.J. Health behavior models for informing digital technology interventions for individuals with mental illness. Psychiatr Rehabil J. 2017;40(3):325–335. doi: 10.1037/prj0000246. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 99.Ten Haken I., Ben Allouch S., van Harten W.H. The use of advanced medical technologies at home: a systematic review of the literature. BMC Publ Health. 2018;18(1):284. doi: 10.1186/s12889-018-5123-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 100.Weiss D. Round hole, square peg: a discourse analysis of social inequalities and the political legitimization of health technology in Norway. BMC Publ Health. 2019;19(1):1691. doi: 10.1186/s12889-019-8023-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 101.Hall A., Brown Wilson C., Stanmore E., Todd C. Moving beyond ’safety’ versus ’autonomy’: a qualitative exploration of the ethics of using monitoring technologies in long-term dementia care. BMC Geriatr. 2019;19(1):145. doi: 10.1186/s12877-019-1155-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 102.Krick T., Huter K., Domhoff D., Schmidt A., Rothgang H., Wolf-Ostermann K. Digital technology and nursing care: a scoping review on acceptance, effectiveness and efficiency studies of informal and formal care technologies. BMC Health Serv Res. 2019;19(1):400. doi: 10.1186/s12913-019-4238-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 103.Morris J.A., Jr., Carrillo Y., Jenkins J.M. Surgical adverse events, risk management, and malpractice outcome: morbidity and mortality review is not enough. Ann Surg. 2003;237(6):844–852. doi: 10.1097/01.SLA.0000072267.19263.26. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 104.Etges A.P.B.D.S., Grenon V., Lu M. Development of an enterprise risk inventory for healthcare. BMC Health Serv Res. 2018;18(1):578. doi: 10.1186/s12913-018-3400-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 105.Chen L. The risk management of medical device-related pressure ulcers based on the Australian/New Zealand Standard. J Int Med Res. 2018;46(10):4129–4139. doi: 10.1177/0300060518786902. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 106.Jafari M., Pourtaleb A., Khodayari-Zarnaq R. The impact of social capital on clinical risk management in nursing: a survey in Iranian public educational hospitals. Nurs Open. 2018;5(3):285–291. doi: 10.1002/nop2.141. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 107.Gallo J., Holinka M., Moucha C.S. Antibacterial surface treatment for orthopaedic implants. Int J Mol Sci. 2014;15(8):13849–13880. doi: 10.3390/ijms150813849. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 108.Dorozhkin S.V. Calcium orthophosphate-containing biocomposites and hybrid biomaterials for biomedical applications. J Funct Biomater. 2015;6(3):708–832. doi: 10.3390/jfb6030708. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 109.Groen W.M., Diloksumpan P., van Weeren P.R., Levato R., Malda J. From intricate to integrated: biofabrication of articulating joints. J Orthop Res. 2017;35(10):2089–2097. doi: 10.1002/jor.23602. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 110.Martinez-Marquez D., Mirnajafizadeh A., Carty C.P., Stewart R.A. Application of quality by design for 3D printed bone prostheses and scaffolds. PloS One. 2018;13(4) doi: 10.1371/journal.pone.0195291. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 111.MacBarb R.F., Lindsey D.P., Woods S.A., Lalor P.A., Gundanna M.I., Yerby S.A. Fortifying the bone-implant interface Part 2: an in vivo evaluation of 3D-printed and TPS-coated triangular implants. Internet J Spine Surg. 2017;11:16. doi: 10.14444/4016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 112.Haleem A., Javaid M. Role of CT and MRI in the design and development of orthopaedic model using additive manufacturing. Journal of Clinical Orthopaedics and Trauma. 2018;9(3):213–217. doi: 10.1016/j.jcot.2018.07.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 113.Mangano C., Bianchi A., Mangano F.G. Custom-made 3D printed subperiosteal titanium implants for the prosthetic restoration of the atrophic posterior mandible of elderly patients: a case series. 3D Print Med. 2020;6(1):1. doi: 10.1186/s41205-019-0055-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 114.Memari Y., Fattahi P., Fattahi A., Eskandarion S., Rakhshan V. Finite element analysis of stress distribution around short and long implants in mandibular overdenture treatment. Dent Res J. 2020;17(1):25–33. [PMC free article] [PubMed] [Google Scholar]
  • 115.Liu Y., Konrad P.E., Neimat J.S. Multisurgeon, multisite validation of a trajectory planning algorithm for deep brain stimulation procedures. IEEE Trans Biomed Eng. 2014;61(9):2479–2487. doi: 10.1109/TBME.2014.2322776. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 116.Barrett S., Begg S., Sloane A., Kingsley M. Surgeons and preventive health: a mixed methods study of current practice, beliefs and attitudes influencing health promotion activities amongst public hospital surgeons. BMC Health Serv Res. 2019;19(1):358. doi: 10.1186/s12913-019-4186-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 117.Apramian T., Cristancho S., Sener A., Lingard L. How do thresholds of principle and preference influence surgeon assessments of learner performance? Ann Surg. 2018;268(2):385–390. doi: 10.1097/SLA.0000000000002284. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 118.Dalager T., Højmark A., Jensen P.T., Søgaard K., Andersen L.N. Using an intervention mapping approach to develop prevention and rehabilitation strategies for musculoskeletal pain among surgeons. BMC Publ Health. 2019;19(1):320. doi: 10.1186/s12889-019-6625-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 119.Munshi N.V. Gene regulatory networks in cardiac conduction system development. Circ Res. 2012;110(11):1525–1537. doi: 10.1161/CIRCRESAHA.111.260026. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 120.Fayaz H.C., Haas N., Kellam J. Improvement of research quality in the fields of orthopaedics and trauma: a global perspective. Int Orthop. 2013;37(7):1205–1212. doi: 10.1007/s00264-013-1897-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 121.Hoang D., Perrault D., Stevanovic M., Ghiassi A. Surgical applications of three-dimensional printing: a review of the current literature & how to get started. Ann Transl Med. 2016;4(23):456. doi: 10.21037/atm.2016.12.18. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 122.Zhou Y. The recent development and applications of fluidic channels by 3D printing. J Biomed Sci. 2017;24(1):80. doi: 10.1186/s12929-017-0384-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 123.Al Shahrani A., Al-Surimi K. Daily routine versus on-demand chest radiograph policy and practice in adult ICU patients- clinicians’ perspective. BMC Med Imag. 2018;18(1):4. doi: 10.1186/s12880-018-0248-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 124.Gershengorn H.B., Wunsch H., Scales D.C., Rubenfeld G.D. Trends in use of daily chest radiographs among US adults receiving mechanical ventilation. JAMA Netw Open. 2018;1(4) doi: 10.1001/jamanetworkopen.2018.1119. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 125.Van der Willik E.M., Meuleman Y., Prantl K. Patient-reported outcome measures: selection of a valid questionnaire for routine symptom assessment in patients with advanced chronic kidney disease - a four-phase mixed methods study. BMC Nephrol. 2019;20(1):344. doi: 10.1186/s12882-019-1521-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 126.Kirk J.W., Bodilsen A.C., Sivertsen D.M., Husted R.S., Nilsen P., Tjørnhøj-Thomsen T. Disentangling the complexity of mobility of older medical patients in routine practise: an ethnographic study in Denmark. PloS One. 2019;14(4) doi: 10.1371/journal.pone.0214271. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 127.Haleem A., Javaid M., Khan R.H., Suman R. 3D printing applications in bone tissue engineering. Journal of Clinical Orthopaedics and Trauma. 2020;11:118–124. doi: 10.1016/j.jcot.2019.12.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 128.Javaid M., Haleem A. 3D printing applications towards the required challenge of stem cells printing. Clinical Epidemiology and Global Health. 2020 doi: 10.1016/j.cegh.2020.02.014. [DOI] [Google Scholar]
  • 129.Han X.G., Tian W. Artificial intelligence in orthopedic surgery: current state and future perspective. Chin Med J. 2019;132(21):2521–2523. doi: 10.1097/CM9.0000000000000479. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 130.Liao Y., Deschamps F., Loures E.D.F.R., Ramos L.F.P. Past, present and future of Industry 4.0—a systematic literature review and research agenda proposal. Int J Prod Res. 2017;55:3609–3629. [Google Scholar]

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