Ethical considerations in telehealth and artificial intelligence for work related musculoskeletal disorders: A scoping review

Abstract

BACKGROUND:

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to perform tasks that typically require human intelligence. The integration of AI and telehealth applications in healthcare raises ethical concerns such as bias, transparency, data privacy, and accountability for errors. Several studies have assessed this topic, particularly with regard to musculoskeletal disorders, which will be the focus of this manuscript.

OBJECTIVE:

We will examine key ethical concepts including informed consent, data protection, confidentiality, physician malpractice, liability, and telemedicine regulations.

METHODS:

Ethical issues pertaining to the topic were explored through a review paper. The primary objective of this scoping review was to map and synthesize the existing literature concerning ethical considerations in telehealth and AI for work-related musculoskeletal disorders.

RESULTS:

Research demonstrates that medication effectiveness, patient and physician satisfaction, and accessibility costs are higher with telemedicine and AI methods compared to in-person approaches, particularly for work-related musculoskeletal disorders. Therefore, addressing ethical issues, including patient data privacy and security, is crucial in this field. By considering these factors, the adoption of emerging AI and telemedicine applications, especially for work-related musculoskeletal disorders, is likely to increase.

CONCLUSION:

AI and telemedicine offer significant advantages, particularly in addressing work-related musculoskeletal disorders. However, ethical and legal issues surrounding their practice require standardized rules to ensure equitable access, quality care, sustainable costs, professional liability, patient privacy, data protection, and confidentiality. Further practical research studies are needed to address these considerations more effectively.

Keywords

Ethics telehealth digital health telemedicine musculoskeletal disease artificial intelligence mHealth

1 Introduction

Work-related musculoskeletal disorders (MSDs) stem from occupational factors such as repetitive motions and awkward postures, leading to conditions affecting muscles, bones, tendons, ligaments, and other parts of the musculoskeletal system. These disorders can result from various workplace activities, repetitive motions, awkward postures, forceful exertions, and other ergonomic or biomechanical stressors [1 –3]. Examples of work-related MSDs include carpal tunnel syndrome, tendonitis, back pain, and neck strain. Preventing and addressing these disorders is crucial for a healthy workplace, involving collaboration between employers, employees, and healthcare professionals [4 –6].

Telehealth, also known as telemedicine, employs digital technologies for remote healthcare delivery, enabling consultations, diagnosis, treatment, and education via electronic communication channels [7]. It improves healthcare access, particularly valuable when in-person visits are impractical, and plays a significant role in managing work-related MSDs, offering convenient and effective remote healthcare solutions [8]. Telehealth is versatile across medical specialties and especially valuable when in-person visits are difficult. It enhances healthcare accessibility, promotes preventive care, and facilitates timely interventions. With advancements in digital communication technologies, the prevalence of telehealth has increased, addressing the growing demand for remote healthcare solutions. In managing work-related musculoskeletal disorders, (MSDs), telehealth offers a convenient and effective means of delivering healthcare services remotely[9, 10].

Artificial Intelligence (AI) replicates human intelligence in machines, enabling tasks such as learning, reasoning, problem-solving, perception, speech recognition, and language understanding [11]. AI systems analyze data, adapt, and improve performance over time without explicit programming. In healthcare, AI is utilized for medical imaging interpretation, drug discovery, personalized medicine, and predictive analytics to enhance patient care and streamline processes [12]. As AI becomes more widespread, ethical considerations are paramount, including addressing algorithm bias, privacy, transparency, and responsible use of AI systems [13, 14].

Ethics delves into the fundamental principles underlying moral values. It can be described as moral philosophy, offering a systematic framework for discerning between good and evil, right and wrong [15]. While legal frameworks like laws and regulations serve as essential guidelines, they may not always suffice. Hence, the establishment of codes of ethics, both written and verbal, becomes imperative. These codes function as compasses for professionals within their respective fields, fostering ethical behavior and enhancing their professionalism [16]. Professional ethics, therefore, encompasses a set of principles and rules dictating appropriate conduct within a specific professional community [17]. Its primary objective lies in cultivating public trust and ensuring adherence to prescribed standards of behavior [18]. Within the realm of medicine, ethical considerations govern the interactions among professionals and between professionals and patients, guided by moral principles[19, 20].

Ethical considerations are pivotal in the fusion of telehealth and artificial intelligence (AI) to combat work-related musculoskeletal disorders (MSDs). As these technologies become more prevalent in the healthcare setting, it is essential to ensure that they are implemented in a manner that prioritizes patient well-being, privacy, and equitable access to care [21, 22].

Thus, the objective of this study is to implement telehealth and AI for work-related musculoskeletal disorders, focusing on addressing privacy concerns, promoting equitable access, ensuring technological reliability, respecting patient autonomy, maintaining professional oversight, and proactively mitigating biases.

2 Method

2.1 Study design

A scoping review is a form of literature review designed to chart the existing literature on a specific topic, identify key concepts, and determine the breadth and depth of available research evidence. In contrast to systematic reviews, scoping reviews encompass a wider range in scope and do not typically assess the quality of individual studies. Instead, they provide a comprehensive overview of the literature to identify gaps in knowledge, highlight areas for future research, and inform policy or practice.

The primary aim of this scoping review was to map and synthesize the existing literature on the ethical considerations in telehealth and artificial intelligence for work-related musculoskeletal disorders. A comprehensive search was conducted across multiple electronic databases, including PubMed, and Google Scholar. In this study, a total of 46 references were retrieved from these databases, 28 from Web of Science, 12 from PubMed, and 6 from Google Scholar.

The search strategy involved a combination of keywords and controlled vocabulary terms related to telehealth, telemedicine, ethical consideration, musculoskeletal disorders, work-related musculoskeletal disorders, artificial intelligence, and various combinations thereof. The search terms were tailored for each database to ensure optimalcoverage.

2.2 Eligibility criteria

2.1 Studies were deemed eligible for inclusion if they met the following criteria:

Published in peer-reviewed journals or full-text in a proceeding book.

Written in English.

Published within the last seven years, with a focus on those published within the last year.

Pertinent to the intersection of telehealth or artificial intelligence and ethical considerations regarding work-related musculoskeletal disorders.

2.3 Search strategy

The initial screening of titles and abstracts was performed independently by two reviewers. Full-text articles were then assessed for eligibility based on the inclusion criteria. Discrepancies were resolved through discussion or by consulting a thirdreviewer.

2.4 Data items

A standardized data extraction form was developed to capture relevant information, including author(s), publication year, study design, key findings, and any other pertinent details.

2.5 Data synthesis

In a scoping review article, the synthesis of results entails collating and summarizing the findings from the included studies coherently and comprehensively. Unlike systematic reviews, which frequently employ statistical meta-analysis, scoping reviews typically utilize a narrative synthesis to present and interpret the results. Data were synthesized using a narrative approach, emphasizing key themes, patterns, and gaps identified in the literature.

2.6 Quality appraisal

Given the scoping nature of this review, a formal quality appraisal was not conducted. However, the methodological rigor of individual studies was considered in the synthesis and interpretation offindings.

3 Results

3.1 Study selection

As outlined in the Supplementary Material, employing the search strategy across relevant databases yielded a total of 230 articles. Initially, 19 articles were excluded due to their publication before the year 2011. Upon closer examination, it was determined that articles predating 2018 were not directly related to the search criteria, resulting in the exclusion of 43 additional records. Among the remaining 168 records published between 2018–2023, 152 articles were excluded as they solely focused on musculoskeletal disorders without relevance to telehealth or artificial intelligence. Consequently, only 16 articles were deemed suitable for inclusion in the study on work-related musculoskeletal disorders.

3.2 Study characteristics

Half of the studies examined telehealth applications, while the remaining focused on artificial intelligence interventions for work-related MSDs. Among the telehealth-related studies, three explored the significance of telehealth amid the COVID-19 pandemic, emphasizing infection prevention and cost reduction. Some articles investigated WMDs such as hip and knee arthroplasty, wrist, arm, waist, and back pain [2 , 13]. One study reported a WMDs prevalence of 66.8% and assessed influential factors like physical activities, posture changes, vibration exposure [8]. Another study evaluated social pressures from colleagues regarding telemedicine integration in sports medicine, identifying both challenges and opportunities [12].

In the realm of artificial intelligence, studies primarily examined risk factors and clustering of WMDs. Some attempted to predict patient exercise programs using deep learning or artificial neural networks [1 , 14]. Wearable technologies emerged as crucial aids in WMDs-related therapies [23]. Several telehealth articles detailed the usage rates of telephone, video conferencing, and computers for telehealth services [9, 10]. An article addressing the ethical and legal challenges in telemedicine during the COVID-19 pandemic, emphasized the need for standardized codes of practice [24]. Another article highlighted the ethical considerations of musculoskeletal physiotherapy and telemedicine, advocating for adherence to established ethical norms [25]. Additionally, a study underscored the ethical gaps in the current legislation, particularly concerning patient data privacy and interaction [26]. The results of all studies included in this scoping review are presented in Table 1.

Table 1
Summary of Findings

Details of article Summary Conclusions

‘It’s Not Hands-on Therapy, So It’s Very Limited’: Telehealth Use and Views among Allied Health Clinicians during the Coronavirus Pandemic
Study type: Cross-sectional survey
Number of subjects: 587 + 240
Country: Australia [2] Telehealth services have helped enable continuity of care during the
coronavirus pandemic. It was aimed to investigate use and views
towards telehealth among allied health clinicians treating people
with musculoskeletal conditions during the pandemic. This study explores telehealth use among allied health
clinicians treating musculoskeletal conditions during
COVID-19. It also concerns included accurate diagnosis
and management due to the lack of physical contact,
suggesting potential barriers to sustained telehealth use
beyond the pandemic.

How is Telehealth Being Utilized in the Context of
Rehabilitation for Lower Limb Musculoskeletal Disorders:
A Scoping Review
Study type: Scoping review
Number of subjects: None
Country: New Zealand
[3] This study aimed to determine which lower limb conditions
telehealth has been used, and to describe the delivery mode and the content of that telehealth, when provided by physiotherapists.
A computerized search of Cochrane Library, PEDro, Medline,
Scopus, SPORTDiscus, Web of Science, PubMed, and CINAHL
was conducted up to the 5 April 2020. A search strategy was
developed and adapted to each database. Studies were included if
they investigated assessment or rehabilitation of a lower limb
musculoskeletal disorder and utilized real-time telehealth
physiotherapy sessions. Synchronous internet-based video conferencing was the
primary method for assessment in most studies, while
rehabilitation via telehealth was more specifically
targeted, primarily towards patients who had undergone
hip or knee arthroplasty.

Increased Prevalence of Work-Related Musculoskeletal
Disorders among Physiotherapists during the COVID-19
Pandemic: A Commentary
Study type: Commentary
Number of subjects: None
Country: India
[4] This study examines the impact of the COVID-19 pandemic on
physiotherapists in terms of increased demands and altered
approaches to patient care. It explores the elevated anxiety and
stress levels among physiotherapists and highlights a rise in
work-related musculoskeletal disorders (WMDs). The study emphasizes the importance of caution and
diligence among physiotherapists to protect themselves
from infection and prevent WMDs while managing
patients during the pandemic.

Telehealth Applications for Outpatients with
Neuromuscular or Musculoskeletal Disorders
Study type: Review
Number of subjects: None
Country: USA
[6] This study explores the expanding field of telehealth in neurology,
particularly for neuromuscular and musculoskeletal disorders. It
discusses current technology, applications, outcomes, and
limitations, emphasizing telehealth’s potential to improve access,
reduce travel costs, and offer flexible practice opportunities. The study underscores the importance of providers
being aware of legal restrictions to minimize risk and liability
in telehealth care.

Patients are Willing to Use Telehealth for the
Multidisciplinary Management of Chronic Musculoskeletal
Conditions: A Cross-Sectional Survey
Study type: Cross-sectional survey
Number of subjects: 85
Country: Australia
[7] This study explores the viability of telehealth for patients with
chronic musculoskeletal conditions facing access barriers to
specialized healthcare. Surveying patients at Neurosurgical and
Orthopaedic Physiotherapy Screening Clinic and Multidisciplinary
Service (N/OPSC &MDS) in Queensland, Australia, the majority
express satisfaction with the current service but note barriers. Over
half are willing to use telehealth, especially if it reduces costs and time. The findings support considering telehealth as an
additional method of service delivery for these patients
within the existing healthcare system.

Telemedicine Experiences of Athletic Trainers And
Orthopaedic Physicians For Patients with
Musculoskeletal Conditions
Study type: cross-sectional survey
Number of subjects: 17 + 5
Country: Indiana
[12] This study explores healthcare professionals’ perceptions and
experiences with telemedicine in the context of sports medicine. A
purposeful sample of 17 athletic trainers and 5 orthopaedic physicians
participated in a 5-month telemedicine intervention. Clinicians were
categorized as adopters or non-adopters based on interviews, with
adopters showing higher agreement on the Theory of Planned Behavior
and Technology Acceptance Model. Integration challenges,
opportunities, collaborative practices, anticipatory socialization, and
benefits were identified as key domains. The study emphasizes the social pressures of colleagues
as influential factors in the integration of telemedicine in
sports medicine, with both challenges and opportunities
identified in the collaborative efforts between athletic
trainers and physicians.

The Current Accuracy, Cost-Effectiveness, and Uses Od
Musculoskeletal Telehealth and Telerehabilitation
Services
Study type: Special communication
Number of subjects: None
Country: United Kingdom
[13] Global morbidity is largely caused by musculoskeletal disorders.
Telehealth services have become more popular as a result of clinicians’
need to quickly adapt to their patients’ changing needs during the
COVID-19 pandemic. Since there are currently few studies in this field
and telehealth methods are constantly evolving, more study is needed
in this area. The objective of this study is to assess the accuracy, health
economics, and ethics of the available telehealth services for
musculoskeletal conditions. Three original research are combined in
this article: a scoping review on cost-effectiveness, a narrative review
on biopsychosocial factors, and a systematic review on telehealth
accuracy. The findings of this study indicate an average agreement
of 62% for knee assessments and 31% for shoulder
assessments compared to in-person evaluations.
Telerehabilitation services show average cost savings of
£38.57 per patient, primarily due to reduced travel
needs. The study highlights the need for further research
in this evolving field of telehealth.

Telemedicine for Patients with Rheumatic and
Musculoskeletal Diseases During The COVID-19
Pandemic; A Positive Experience in the Netherlands
Study type: Cross-sectional survey
Number of subjects: 75
Country: The Netherlands
[22] The aim of this study is to provide an overview of the care that
rheumatologists in the Netherlands provided to patients with rheumatic
and musculoskeletal illnesses (RMDs) in the initial months of the
COVID-19 epidemic. Quantitative and qualitative data from a
cross-sectional survey distributed to all Dutch Rheumatology Society
members in May 2020 were analyzed using a mixed methods strategy.
The study asked questions on demographics, the way treatment is now
provided, and the use, acceptance, and obstacles of telemedicine. Rheumatologists in the Netherlands adapted care for
RMD patients during the early COVID-19 months,
relying on telephone (99%) and limited video (9%)
consultations. Over 80% of outpatient visits occurred by
phone, guided by patient-reported outcomes in 25% of
cases. Telemedicine’s top facilitators were reduced
patient travel time and system ease, while barriers
included the inability to perform physical exams.
Overall satisfaction was high, though concerns centered
on the lack of physical examinations and nonverbal
communication.

Artificial Intelligence (AI) Powered Precise
Classification of Recuperation Exercises for
Musculoskeletal Disorders
Study type: Experimental Study
Number of subjects: 15
Country: India
[1] People working in the IT industry today must spend long hours (around
three to four hours) sitting still. This may result in paralysis and causes
excruciating hip, neck, and shoulder discomfort. The process of
accurately classifying trunk extension and
flexion, wrist extension and flexion workout posture images involves the convergence of a
three-dimensional (3D) image into a plane-based projection. A deep
learning algorithm is a better method for increasing identification
accuracy and processing speed because the predictions in distinct
planes are mistakenly identified during the convergence process. 200
image datasets are produced at different planes showing the wrist,
trunk extension, and flexion exercise posture. This study introduces a deep learning algorithm to
address musculoskeletal pain in the IT industry caused
by prolonged sitting. The algorithm accurately classifies
wrist, trunk, and flexion exercise postures with a high
accuracy of 98.12% in training and 95% in validation.
Compared to existing methods, this approach proves
effective in musculoskeletal rehabilitation therapy,
providing precise tracking of patients’ training with
superior performance.

Machine Learning Based Risk Classification of
Musculoskeletal Disorder among the Garment Industry
Operators
Study type: Case-Control Study
Number of subjects: 10 + 10
Country: India
[5] This study addresses the high occurrence of work-related
musculoskeletal disorders in the garment industry, often
overlooked, impacting both physical health and work efficiency.
The study proposes an automated diagnosis system for
musculoskeletal disorders among sewing machine operators. It
records working videos of healthy and unhealthy participants,
applies the OpenPose algorithm for posture evaluation, extracts
joint angles, normalizes them, and uses a KNN Classifier for
classification. With 91.3% accuracy through stratified k-fold
cross-validation, the system proves effective in diagnosing
musculoskeletal disorders among sewing machine operators. The research project offers a special and useful remedy
for a major issue facing the apparel sector. It would be
quite simple to regulate the operator’s workload while
taking his physical health into account based on the
classification.

Prediction of Work-Related Risk Factors Among Bus
Drivers Using Machine Learning
Study type: Cross-sectional survey
Number of subjects: 400
Country: India
[8] This study employs machine learning to predict work-related risk
factors for musculoskeletal diseases (WMSDs) in 400 male bus
drivers from Bengaluru Metropolitan Transport Corporation. Key
findings include a high WMSD prevalence (66.75%) and influential
factors like physical activities, posture changes, vibration exposure,
egress/ingress, on-duty breaks, and seat adaptability. The recommendations of the article include promoting
physical activities, healthy lifestyles, and ergonomic
cabin design to mitigate WMSDs among bus drivers.

Prevention of Work-Related Musculoskeletal Disorders
Supported by Artificial Intelligence
Study type: Narrative review
Number of subjects: None
Country: Canada
[9] This study explores the application of artificial intelligence (AI) in
reducing the risk of work-related musculoskeletal
disorders (WMSDs) in industrial settings. Despite technological
advancements and automation, workers in industries still face tasks
involving heavy lifting and movements associated with WMSD risk
factors. The review analyzes 28 papers, focusing on dimensions
such as WMSD risk factors, ergonomic criteria, and the use of AI.
The aim is to assess the current state of AI in preventing WMSDs
in the context of physical ergonomics. The application of AI for WMSD prevention is
reviewed narratively in this article, which draws on 28
pertinent research from the scholarly literature. The
investigation revealed a strong interest in employing AI
algorithms, primarily ML and DL, to track the physical
risk factors that employees are exposed to while performing their jobs. This interest has expanded in
recent years. The most recent uses of AI to lower
the risk of having WMSDs are summarized for the
researchers in this review.

Role of Internet of Things (IoT), Artificial Intelligence and
Machine Learning in Musculoskeletal Pain: A Scoping
Review
Study type: Scoping review
Number of subjects: None
Country: India
[10] The study highlights the growing influence of Artificial Intelligence
(AI), Internet of Things (IoT), and machine learning (ML) in
medical sectors, focusing on their impact on musculoskeletal
conditions. It explores how AI-driven digital health interventions,
utilizing IoT, brain imaging, and ML technologies, can reduce pain
and improve functionality in patients with chronic neck and back
pain. The study also examines whether these AI-driven solutions
enhance exercise compliance, potentially becoming lifestyle
choices for individuals with musculoskeletal diseases. Although there has been a lot of positive comments on
the usage of digital therapies, some people might not be
quite satisfied. In spite of this, further study and better
generalization of its use are still required to enhance
patient outcomes.

Search For Medical Information and Treatment Opions
For Musculoskeletal Disorders Though An
Artificial Intellingence Chatbot: Focusing on Shoulder
Impingement Syndrome
Study type: Improving Descriptive Study
Number of subjects: None
Country: Korea
[11] The study explores ChatGPT’s capacity, based on GPT-3.5, to provide
medical information and treatment options for Supraspinatus
Tendinopathy (SIS). Utilizing the chatbot in a free beta test, the results
indicate that ChatGPT can offer insights into SIS, including definition,
prevalence, risk factors, symptoms, related diseases, orthopedic tests,
and a list of treatment options and exercises. While it has potential for
delivering useful medical information, caution is advised due to
potential biases or inappropriate content. Advancements in natural language processing may
further enhance ChatGPT’s ability to provide detailed
and accurate medical information.

Use of Artifical Neural Networks in the Prognosis of
Musculoskeletal Diseases-A Scoping Review
Study type: Scoping review
Number of subjects: None
Country: Germany
[14] The study assesses the current evidence on the use of Artificial Neural
Networks (ANN) in prognostic studies of musculoskeletal diseases
(MSD) and evaluates the accuracy of ANN predictions for MSD
prognosis. Eighteen studies covering 16 different types of MSD were
included. The accuracy of ANN model predictions ranged from 0.542
to 0.947, surpassing traditional logistic regression models. The findings suggest that ANN holds potential for predicting the prognosis of various MSD types, highlighting its versatility in the field.

Human-Centered Explainable Artificial Intelligence:
Automotive Occupational Health Protection Profiles in
Prevention Musculoskeletal Symptoms
Study type: Data Science Study
Number of subjects: 638
Country: Portugal
[21] In this novel longitudinal study within automotive and industrial
settings, occupational physicians employ Functional Work Ability
(FWA) to establish Occupational Health Protection Profiles (OHPP) for
workers. Unlike previous research relying on human-understandable
models, this study integrates artificial intelligence, specifically
CatBoost regression models, to predict workers’ next medical
appointments and body part protection severity. The RMSLE values of
0.84 and 1.23 weeks indicate the effectiveness of CatBoost in
prognosis. The findings contribute to understanding risk factors for
OHPP, allowing early identification of musculoskeletal
symptoms and work-related absenteeism.

The Risk Classification of Ergonomic Musculoskeletal
Disorders in Work-Related Repetitive Manual Handling
Operations With Deep Learning Approaches
Study type: Data Science Study
Number of subjects: 126
Country: Taiwan
[31] This study addresses work-related musculoskeletal disorders (WMSD)
from repetitive tasks, a major factor in labor insurance payments in
Taiwan. Utilizing deep learning and 2D human pose estimation, it aims
to assess WMSD risk efficiently. This study achieves over 80% accuracy, the model
classifies manual handling tasks, with a goal of 90%
accuracy for rapid and precise ergonomic risk
assessment and feedback.

Wearable Motion Capture Devices for the Prevention of
Work-Related Musculoskeletal Disorders In
Ergonomic— An Overview of Current Applications,
Challenges, And Future Opportunities
Study type: Narrative review
Number of subjects: None
Country: Sweden
[23] The study discusses the role of wearable motion capture devices in
preventing work-related musculoskeletal disorders (WMSDs). The
technology is noted for its potential to enhance exposure and risk
assessments, improving effectiveness in work technique training.
Despite the versatile applications, challenges such as miniaturization,
improved usability, and the establishment of international standards
need to be addressed for widespread use in occupational settings. Wearable technology offers a number of benefits over
traditional observational assessment tools in the areas of
exposure measurement and risk assessment. These
benefits include enhanced measurement accuracy and
precision, the potential for longer data collection
periods, real-time exposure visualization, and automated
risk assessment that incorporates exposures from the
entire workday using a variety of current tools as inputs.

Details of article	Summary	Conclusions
‘It’s Not Hands-on Therapy, So It’s Very Limited’: Telehealth Use and Views among Allied Health Clinicians during the Coronavirus Pandemic Study type: Cross-sectional survey Number of subjects: 587 + 240 Country: Australia [2]	Telehealth services have helped enable continuity of care during the coronavirus pandemic. It was aimed to investigate use and views towards telehealth among allied health clinicians treating people with musculoskeletal conditions during the pandemic.	This study explores telehealth use among allied health clinicians treating musculoskeletal conditions during COVID-19. It also concerns included accurate diagnosis and management due to the lack of physical contact, suggesting potential barriers to sustained telehealth use beyond the pandemic.
How is Telehealth Being Utilized in the Context of Rehabilitation for Lower Limb Musculoskeletal Disorders: A Scoping Review Study type: Scoping review Number of subjects: None Country: New Zealand [3]	This study aimed to determine which lower limb conditions telehealth has been used, and to describe the delivery mode and the content of that telehealth, when provided by physiotherapists. A computerized search of Cochrane Library, PEDro, Medline, Scopus, SPORTDiscus, Web of Science, PubMed, and CINAHL was conducted up to the 5 April 2020. A search strategy was developed and adapted to each database. Studies were included if they investigated assessment or rehabilitation of a lower limb musculoskeletal disorder and utilized real-time telehealth physiotherapy sessions.	Synchronous internet-based video conferencing was the primary method for assessment in most studies, while rehabilitation via telehealth was more specifically targeted, primarily towards patients who had undergone hip or knee arthroplasty.
Increased Prevalence of Work-Related Musculoskeletal Disorders among Physiotherapists during the COVID-19 Pandemic: A Commentary Study type: Commentary Number of subjects: None Country: India [4]	This study examines the impact of the COVID-19 pandemic on physiotherapists in terms of increased demands and altered approaches to patient care. It explores the elevated anxiety and stress levels among physiotherapists and highlights a rise in work-related musculoskeletal disorders (WMDs).	The study emphasizes the importance of caution and diligence among physiotherapists to protect themselves from infection and prevent WMDs while managing patients during the pandemic.
Telehealth Applications for Outpatients with Neuromuscular or Musculoskeletal Disorders Study type: Review Number of subjects: None Country: USA [6]	This study explores the expanding field of telehealth in neurology, particularly for neuromuscular and musculoskeletal disorders. It discusses current technology, applications, outcomes, and limitations, emphasizing telehealth’s potential to improve access, reduce travel costs, and offer flexible practice opportunities.	The study underscores the importance of providers being aware of legal restrictions to minimize risk and liability in telehealth care.
Patients are Willing to Use Telehealth for the Multidisciplinary Management of Chronic Musculoskeletal Conditions: A Cross-Sectional Survey Study type: Cross-sectional survey Number of subjects: 85 Country: Australia [7]	This study explores the viability of telehealth for patients with chronic musculoskeletal conditions facing access barriers to specialized healthcare. Surveying patients at Neurosurgical and Orthopaedic Physiotherapy Screening Clinic and Multidisciplinary Service (N/OPSC &MDS) in Queensland, Australia, the majority express satisfaction with the current service but note barriers. Over half are willing to use telehealth, especially if it reduces costs and time.	The findings support considering telehealth as an additional method of service delivery for these patients within the existing healthcare system.
Telemedicine Experiences of Athletic Trainers And Orthopaedic Physicians For Patients with Musculoskeletal Conditions Study type: cross-sectional survey Number of subjects: 17 + 5 Country: Indiana [12]	This study explores healthcare professionals’ perceptions and experiences with telemedicine in the context of sports medicine. A purposeful sample of 17 athletic trainers and 5 orthopaedic physicians participated in a 5-month telemedicine intervention. Clinicians were categorized as adopters or non-adopters based on interviews, with adopters showing higher agreement on the Theory of Planned Behavior and Technology Acceptance Model. Integration challenges, opportunities, collaborative practices, anticipatory socialization, and benefits were identified as key domains.	The study emphasizes the social pressures of colleagues as influential factors in the integration of telemedicine in sports medicine, with both challenges and opportunities identified in the collaborative efforts between athletic trainers and physicians.
The Current Accuracy, Cost-Effectiveness, and Uses Od Musculoskeletal Telehealth and Telerehabilitation Services Study type: Special communication Number of subjects: None Country: United Kingdom [13]	Global morbidity is largely caused by musculoskeletal disorders. Telehealth services have become more popular as a result of clinicians’ need to quickly adapt to their patients’ changing needs during the COVID-19 pandemic. Since there are currently few studies in this field and telehealth methods are constantly evolving, more study is needed in this area. The objective of this study is to assess the accuracy, health economics, and ethics of the available telehealth services for musculoskeletal conditions. Three original research are combined in this article: a scoping review on cost-effectiveness, a narrative review on biopsychosocial factors, and a systematic review on telehealth accuracy.	The findings of this study indicate an average agreement of 62% for knee assessments and 31% for shoulder assessments compared to in-person evaluations. Telerehabilitation services show average cost savings of £38.57 per patient, primarily due to reduced travel needs. The study highlights the need for further research in this evolving field of telehealth.
Telemedicine for Patients with Rheumatic and Musculoskeletal Diseases During The COVID-19 Pandemic; A Positive Experience in the Netherlands Study type: Cross-sectional survey Number of subjects: 75 Country: The Netherlands [22]	The aim of this study is to provide an overview of the care that rheumatologists in the Netherlands provided to patients with rheumatic and musculoskeletal illnesses (RMDs) in the initial months of the COVID-19 epidemic. Quantitative and qualitative data from a cross-sectional survey distributed to all Dutch Rheumatology Society members in May 2020 were analyzed using a mixed methods strategy. The study asked questions on demographics, the way treatment is now provided, and the use, acceptance, and obstacles of telemedicine.	Rheumatologists in the Netherlands adapted care for RMD patients during the early COVID-19 months, relying on telephone (99%) and limited video (9%) consultations. Over 80% of outpatient visits occurred by phone, guided by patient-reported outcomes in 25% of cases. Telemedicine’s top facilitators were reduced patient travel time and system ease, while barriers included the inability to perform physical exams. Overall satisfaction was high, though concerns centered on the lack of physical examinations and nonverbal communication.
Artificial Intelligence (AI) Powered Precise Classification of Recuperation Exercises for Musculoskeletal Disorders Study type: Experimental Study Number of subjects: 15 Country: India [1]	People working in the IT industry today must spend long hours (around three to four hours) sitting still. This may result in paralysis and causes excruciating hip, neck, and shoulder discomfort. The process of accurately classifying trunk extension and flexion, wrist extension and flexion workout posture images involves the convergence of a three-dimensional (3D) image into a plane-based projection. A deep learning algorithm is a better method for increasing identification accuracy and processing speed because the predictions in distinct planes are mistakenly identified during the convergence process. 200 image datasets are produced at different planes showing the wrist, trunk extension, and flexion exercise posture.	This study introduces a deep learning algorithm to address musculoskeletal pain in the IT industry caused by prolonged sitting. The algorithm accurately classifies wrist, trunk, and flexion exercise postures with a high accuracy of 98.12% in training and 95% in validation. Compared to existing methods, this approach proves effective in musculoskeletal rehabilitation therapy, providing precise tracking of patients’ training with superior performance.
Machine Learning Based Risk Classification of Musculoskeletal Disorder among the Garment Industry Operators Study type: Case-Control Study Number of subjects: 10 + 10 Country: India [5]	This study addresses the high occurrence of work-related musculoskeletal disorders in the garment industry, often overlooked, impacting both physical health and work efficiency. The study proposes an automated diagnosis system for musculoskeletal disorders among sewing machine operators. It records working videos of healthy and unhealthy participants, applies the OpenPose algorithm for posture evaluation, extracts joint angles, normalizes them, and uses a KNN Classifier for classification. With 91.3% accuracy through stratified k-fold cross-validation, the system proves effective in diagnosing musculoskeletal disorders among sewing machine operators.	The research project offers a special and useful remedy for a major issue facing the apparel sector. It would be quite simple to regulate the operator’s workload while taking his physical health into account based on the classification.
Prediction of Work-Related Risk Factors Among Bus Drivers Using Machine Learning Study type: Cross-sectional survey Number of subjects: 400 Country: India [8]	This study employs machine learning to predict work-related risk factors for musculoskeletal diseases (WMSDs) in 400 male bus drivers from Bengaluru Metropolitan Transport Corporation. Key findings include a high WMSD prevalence (66.75%) and influential factors like physical activities, posture changes, vibration exposure, egress/ingress, on-duty breaks, and seat adaptability.	The recommendations of the article include promoting physical activities, healthy lifestyles, and ergonomic cabin design to mitigate WMSDs among bus drivers.
Prevention of Work-Related Musculoskeletal Disorders Supported by Artificial Intelligence Study type: Narrative review Number of subjects: None Country: Canada [9]	This study explores the application of artificial intelligence (AI) in reducing the risk of work-related musculoskeletal disorders (WMSDs) in industrial settings. Despite technological advancements and automation, workers in industries still face tasks involving heavy lifting and movements associated with WMSD risk factors. The review analyzes 28 papers, focusing on dimensions such as WMSD risk factors, ergonomic criteria, and the use of AI. The aim is to assess the current state of AI in preventing WMSDs in the context of physical ergonomics.	The application of AI for WMSD prevention is reviewed narratively in this article, which draws on 28 pertinent research from the scholarly literature. The investigation revealed a strong interest in employing AI algorithms, primarily ML and DL, to track the physical risk factors that employees are exposed to while performing their jobs. This interest has expanded in recent years. The most recent uses of AI to lower the risk of having WMSDs are summarized for the researchers in this review.
Role of Internet of Things (IoT), Artificial Intelligence and Machine Learning in Musculoskeletal Pain: A Scoping Review Study type: Scoping review Number of subjects: None Country: India [10]	The study highlights the growing influence of Artificial Intelligence (AI), Internet of Things (IoT), and machine learning (ML) in medical sectors, focusing on their impact on musculoskeletal conditions. It explores how AI-driven digital health interventions, utilizing IoT, brain imaging, and ML technologies, can reduce pain and improve functionality in patients with chronic neck and back pain. The study also examines whether these AI-driven solutions enhance exercise compliance, potentially becoming lifestyle choices for individuals with musculoskeletal diseases.	Although there has been a lot of positive comments on the usage of digital therapies, some people might not be quite satisfied. In spite of this, further study and better generalization of its use are still required to enhance patient outcomes.
Search For Medical Information and Treatment Opions For Musculoskeletal Disorders Though An Artificial Intellingence Chatbot: Focusing on Shoulder Impingement Syndrome Study type: Improving Descriptive Study Number of subjects: None Country: Korea [11]	The study explores ChatGPT’s capacity, based on GPT-3.5, to provide medical information and treatment options for Supraspinatus Tendinopathy (SIS). Utilizing the chatbot in a free beta test, the results indicate that ChatGPT can offer insights into SIS, including definition, prevalence, risk factors, symptoms, related diseases, orthopedic tests, and a list of treatment options and exercises. While it has potential for delivering useful medical information, caution is advised due to potential biases or inappropriate content.	Advancements in natural language processing may further enhance ChatGPT’s ability to provide detailed and accurate medical information.
Use of Artifical Neural Networks in the Prognosis of Musculoskeletal Diseases-A Scoping Review Study type: Scoping review Number of subjects: None Country: Germany [14]	The study assesses the current evidence on the use of Artificial Neural Networks (ANN) in prognostic studies of musculoskeletal diseases (MSD) and evaluates the accuracy of ANN predictions for MSD prognosis. Eighteen studies covering 16 different types of MSD were included. The accuracy of ANN model predictions ranged from 0.542 to 0.947, surpassing traditional logistic regression models.	The findings suggest that ANN holds potential for predicting the prognosis of various MSD types, highlighting its versatility in the field.
Human-Centered Explainable Artificial Intelligence: Automotive Occupational Health Protection Profiles in Prevention Musculoskeletal Symptoms Study type: Data Science Study Number of subjects: 638 Country: Portugal [21]	In this novel longitudinal study within automotive and industrial settings, occupational physicians employ Functional Work Ability (FWA) to establish Occupational Health Protection Profiles (OHPP) for workers. Unlike previous research relying on human-understandable models, this study integrates artificial intelligence, specifically CatBoost regression models, to predict workers’ next medical appointments and body part protection severity. The RMSLE values of 0.84 and 1.23 weeks indicate the effectiveness of CatBoost in prognosis.	The findings contribute to understanding risk factors for OHPP, allowing early identification of musculoskeletal symptoms and work-related absenteeism.
The Risk Classification of Ergonomic Musculoskeletal Disorders in Work-Related Repetitive Manual Handling Operations With Deep Learning Approaches Study type: Data Science Study Number of subjects: 126 Country: Taiwan [31]	This study addresses work-related musculoskeletal disorders (WMSD) from repetitive tasks, a major factor in labor insurance payments in Taiwan. Utilizing deep learning and 2D human pose estimation, it aims to assess WMSD risk efficiently.	This study achieves over 80% accuracy, the model classifies manual handling tasks, with a goal of 90% accuracy for rapid and precise ergonomic risk assessment and feedback.
Wearable Motion Capture Devices for the Prevention of Work-Related Musculoskeletal Disorders In Ergonomic— An Overview of Current Applications, Challenges, And Future Opportunities Study type: Narrative review Number of subjects: None Country: Sweden [23]	The study discusses the role of wearable motion capture devices in preventing work-related musculoskeletal disorders (WMSDs). The technology is noted for its potential to enhance exposure and risk assessments, improving effectiveness in work technique training. Despite the versatile applications, challenges such as miniaturization, improved usability, and the establishment of international standards need to be addressed for widespread use in occupational settings.	Wearable technology offers a number of benefits over traditional observational assessment tools in the areas of exposure measurement and risk assessment. These benefits include enhanced measurement accuracy and precision, the potential for longer data collection periods, real-time exposure visualization, and automated risk assessment that incorporates exposures from the entire workday using a variety of current tools as inputs.

3.3 Synthesis of results

Effectiveness: Evaluation studies consistently demonstrated telehealth’s efficacy and reliability in assessing common WMSDs. The absence of contradictory results underscores the importance of provider awareness regarding legal limitations to mitigate risk and responsibility when delivering telehealth care. These findings advocate for the integration of telehealth as an additional modality within the current healthcare system to better servepatients.

Satisfaction: Several studies reported high levels of satisfaction with telehealth programs, particularly noting the success of exercises compared to traditional clinical methods. Positive outcomes were observed in posture correction, sitting ergonomics, and post-operative flexion and extension exercises following arthroplasty. Additionally, a majority of participants expressed satisfaction with telehealth services, highlighting reductions in both time and cost.

Cost and access: Cost reduction emerges as a significant benefit of telehealth, accompanied by shortened therapy durations for patients. Telehealth methods, predominantly involving exercises delivered via video-conferencing or digital platforms, enable patients to receive treatments in their familiar environments while allowing physicians to deliver services more efficiently.

4 Discussion

The compilation of literature presented herein encompasses a diverse array of studies investigating the use and perspectives of telehealth services, particularly within the realm of musculoskeletal conditions. These studies collectively contribute to a nuanced understanding of the evolving landscape of healthcare, with a focus on continuity of care during the COVID-19 pandemic and the broader implications for allied health clinicians.

For instance, multiple studies emphasize the need for robust safeguards to protect sensitive health information transmitted and processed through telehealth platforms and AI algorithms. Several studies have emphasized how crucial it is to have robust security measures in place to secure private health data that is processed and transferred via AI algorithms and telehealth platforms. These security measures are essential to guaranteeing the confidentiality, integrity, and privacy of patient health data as it travels across digital platforms and is processed by AI systems. Using encryption techniques, access controls, safe data storage procedures, and adherence to pertinent privacy laws are all part of putting strong measures in place. To allay worries about data security and privacy and preserve confidence in telehealth services and AI-driven healthcare solutions, these steps are crucial. Strong security measures are necessary to secure sensitive health information as telemedicine and artificial intelligence continue to transform healthcare. This entails putting in place cutting-edge security measures, making sure that regulations are followed, preserving data integrity, and encouraging openness and patient consent [27 –29]. As the adoption of wearables and remote monitoring devices increases [30], ensuring the confidentiality of patient data becomes paramount. Striking a balance between maintaining patient autonomy and fostering meaningful human interaction amidst AI-driven interventions is imperative. Clinicians must remain vigilant in interpreting AI-generated insights and hold themselves accountable for the decisions made based on these technologies. Continuous education and training are crucial to ensure healthcare providers are well-equipped to navigate the ethical challenges associated with the integration of AI into their practice.

Furthermore, a majority of the studies indicate that telehealth interventions delivered at home are as effective as traditional therapy methods. Rehabilitation methods for work-related musculoskeletal disorders have demonstrated success with the integration of telehealth and AI [1 , 23]. Moreover, satisfaction levels among both therapists and patients regarding telehealth and AI-driven technologies for work-related musculoskeletal disorders vary from good to high levels. Telehealth services have been reported to aid in cost savings for patients, particularly in reducing transportation expenses [3 , 31].

The examination of the impact of the COVID-19 pandemic on physiotherapists provides a critical perspective on the challenges confronting healthcare professionals. Discussions revolve around increased demands, alterations in patient care approaches, and heightened levels of anxiety and stress among physiotherapists, with a notable surge in work-related musculoskeletal disorders. This highlights the imperative need for support mechanisms within the healthcare system to address the well-being of frontline workers [2].

Exploration into telehealth within neurology, particularly concerning neuromuscular and musculoskeletal disorders, offers valuable insights into technological applications, outcomes, and limitations. Discussions highlight the potential to enhance access, diminish travel costs, and provide flexible practice opportunities, emphasizing telehealth’s broader applicability beyond the immediate scope of musculoskeletal conditions [6].

The investigation into the utilization and perceptions telehealth among allied health clinicians treating musculoskeletal conditions during the pandemic highlights the instrumental role of telehealth in ensuring the continuity of care. This finding aligns with a broader trend observed in the literature, where telehealth emerges as a pivotal tool for adapting to patients’ changing needs during thepandemic [22].

The focus on lower limb conditions and the delivery of telehealth by physiotherapists adds granularity to our understanding of telehealth applications. The literature search, spanning multiple databases, underscores the significance of real-time telehealth physiotherapy sessions, elucidating the specific conditions addressed and the modes of delivery employed [4].

Exploring healthcare professionals’ perceptions and experiences with telemedicine in sports medicine provides a comprehensive analysis of adopters and non-adopters. Identification of key domains such as integration challenges, collaborative practices, and benefits contributes to the understanding of factors influencing the uptake of telehealth in specialized medical fields [12].

The proposal for an automated diagnosis system aimed at detecting musculoskeletal disorders among sewing machine operators, utilizing video recordings and machine learning, showcases the potential for technology-driven solutions in overlooked industries. The high accuracy achieved in diagnosing musculoskeletal disorders underlines the feasibility of incorporating automation in occupational health [5]. A review of the application of artificial intelligence in preventing work-related musculoskeletal disorders in industrial settings provides a comprehensive overview. The analysis of risk factors, ergonomic criteria, and the current state of AI in physical ergonomics contributes to our understanding of technological advancements in occupational health. The exploration of AI, Internet of Things (IoT), and machine learning in medical sectors, specifically for musculoskeletal conditions, presents a forward-looking perspective. The potential for AI-driven digital health interventions to reduce pain and improve functionality in patients with chronic neck and back pain highlights the transformative impact of technology on lifestyle choices [32].

Another domain where the utility of artificial intelligence aids in healthcare information dissemination is leveraging ChatGPT’s capacity to provide medical information and treatment options for Supraspinatus Tendinopathy. While the study indicates potential usefulness, caution is advised due to the model’s inherent limitations and potential biases [11]. Moreover, the evaluation of Artificial Neural Networks (ANN) in prognostic studies of musculoskeletal diseases contributes to the growing body of literature on predictive analytics in healthcare. The varying accuracy observed in ANN predictions underscores the necessity for ongoing refinement and validation in prognostic modeling [14].

Furthermore, the discussion on the role of wearable motion capture devices in preventing work-related musculoskeletal disorders reflects ongoing technological advancements in occupational health. The potential for enhanced exposure and risk assessments, coupled with challenges such as miniaturization and international standards, underscores the need for continued research and development [23].

Similarly, the survey of patients in Queensland, Australia, provides a valuable patient-centric perspective on telehealth for chronic musculoskeletal conditions. While the majority expressed satisfaction with the current service, identified barriers underscore the need for targeted interventions. The willingness of over half the participants to use telehealth, particularly if it reduces costs and time, signals a receptiveness to innovative healthcare delivery methods [7].

Moreover, the use of machine learning to predict work-related risk factors for musculoskeletal diseases among bus drivers underscores the potential for proactive intervention. The identification of influential factors and the prevalence of musculoskeletal disorders highlights the importance of personalized strategies for occupational health [9].

Additionally, the novel longitudinal study employing CatBoost regression models in occupational health settings demonstrates the integration of artificial intelligence for predicting workers’ medical appointments and body part protection severity. The study’s effectiveness, as indicated by RMSLE values, highlights the potential for advanced modeling techniques in prognosis [21].

Furthermore, the utilization of deep learning and 2D human pose estimation to assess work-related musculoskeletal disorder (WMSD) risk in repetitive tasks adds a technological dimension to occupational health. The emphasis on efficient risk assessment aligns with the broader goal of preventive healthcare in occupational settings [31]. Lastly, there are many other studies related to ergonomics, injuries and musculoskeletal disorders treatment. Some of them are directly related to telehealth applications, and one of the articles is related to ethical principles in telehealth practices. However, this article is a systematic review [30 , 33–45].

When considering the ethical and professional aspects of the issue, the concept that telehealth is merely an alternative means of providing healthcare services becomes prominent. Therefore, it is imperative for clinicians, professional associations, and authorized bodies to define the scope of practice [25].

According to the World Medical Association’s Statement on Telemedicine Ethics, “Face-to-face consultation between physician and patient” is still the gold standard of clinical care. However, telemedicine has become increasingly widespread, especially recently. It should not be forgotten that telemedicine has a very important facilitating role in reducing the distance between the “patient and the doctor”, especially in geographical areas where it is difficult to access medical services or in individuals such as ship crews who are far from settlements [46].

Dizzying developments in healthcare delivery may open new doors for legal, ethical, and other regulations. All these can have a strong impact on decision-makers. Two prominent issues in telemedicine applications are ethics and cost. Patient privacy, informed consent, and the fact that a patient is waiting for healing are the ethical dimensions of the issue. Issues such as the use of technology in telemedicine applications, security, and legality are the topics that increase the cost [26].

The COVID-19 pandemic, which has affected the whole world, has caused a surge in telemedicine applications. This, of course, has brought the issue of telehealth and ethics, which is already being discussed, to the agenda more strongly this time. Regardless of the application area of telehealth, ethical principles such as “autonomy”, “beneficence”, “do no harm” and “justice” should be respected in the same way as “privacy and confidentiality of patient data” [24].

This scoping review has several limitations. Firstly, accessing relevant articles in databases posed a challenge due to the multitude of scientific databases available, making it possible to find valuable articles across different indices. Secondly, the time constraint for searching and evaluating articles within a limited timeframe was a limitation. The rapid turnover of information necessitates swift assessment, as findings may become outdated or irrelevant if not addressed promptly. Another final limitation is the absence of other studies directly related to the subject matter and addressing its ethical dimensions.

5 Conclusion

In conclusion, the literature concerning ethical considerations in the convergence of telehealth and AI for WRMSDs underscores the multifaceted nature of these concerns. Key themes such as privacy, informed consent, equity, autonomy, and professional accountability demand ongoing attention and collaborative efforts from researchers, policymakers, and healthcare practitioners to ensure the responsible and ethical deployment of these technologies in the management of work-related musculoskeletal disorders.

In our swiftly evolving world, the provision of health services is undergoing rapid development. As in all fields, it is imperative for professionals to come together and make decisions about ethical practices for real-life applications. Another crucial aspect is the continual updating of these practices based on experiences gained. We believe that increasing studies on this subject is of paramount importance.

Furthermore, this compilation of literature provides a comprehensive overview of telehealth and technology applications in the context of musculoskeletal conditions. From the perspectives of healthcare professionals, patients, and technological advancements, the studies collectively contribute to a nuanced understanding of the challenges, opportunities, and ethical considerations in the rapidly evolving landscape of musculoskeletal healthcare. As the field continues to evolve, further research is warranted to enhance the evidence base and guide the ethical, accurate, and cost-effective implementation of telehealth services for musculoskeletal conditions.

Ethical approval

Tehran University of Medical Sciences, IR.TUMS.MEDICINE.REC.1403.133.

Informed consent

Not applicable.

Conflict of interest

None.

Funding

There was no funding for this study.

Editorial for special series

“Advancements in Telehealth: Empowering Occupational Health through Teleconsultation, Telerehabilitation, and Teleexercise for the Prevention and Treatment of Musculoskeletal Disorders across Occupations”.

Footnotes

The supplementary material is available in the electronic version of this article: .

References

Ekambaram

, Ponnusamy

, Natarajan

, Firos Khan

MFS

. Artificial Intelligence (AI) Powered Precise Classification of Recuperation Exercises for Musculoskeletal Disorders. Trait du Signal. 2023;40(2):767–73.

Malliaras

, Merolli

, Williams

, Caneiro

, Haines

, Barton

. ‘It’s not hands-on therapy, so it’s very limited’: Telehealth use and views among allied health clinicians during the coronavirus pandemic. Musculoskelet Sci Pract. 2021;52:102340.

Wong

, Ward

, Gemmell

, Bright

, Blackman

, Sole

, vdHow is telehealth being utilized in the context of rehabilitation for lower limb musculoskeletal disorders: A scoping review. Phys Ther Rev. 2020;25(5-6):350–60.

Kakaraparthi

, Vishwanathan

. Increased prevalence of work-related musculoskeletal disorders among physiotherapists during the COVID-19 pandemic: A Commentary. Work. 2022;72(4):1191–3.

Arora

, Vijayvargiya

, Kumar

, Tiwari

. Machine learning based risk classification of Musculoskeletal disorder among the garment industry operators. Içinde: 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA). IEEE; 2021. s. 1193–1198.

Howard

, Kaufman

. Telehealth applications for outpatients with neuromuscular or musculoskeletal disorders. Muscle Nerve. 2018;58(4):475–85.

Cottrell

, Hill

, O’Leary

, Raymer

, Russell

. Patients are willing to use telehealth for the multidisciplinary management of chronic musculoskeletal conditions: A cross-sectional survey. J Telemed Telecare. 2018;24(7):445–52.

Hanumegowda

, Gnanasekaran

. Prediction of work-related risk factors among bus drivers using machine learning. Int J Environ Res Public Health. 2022;19(22):15179.

Sekkay

. Prevention of Work-Related Musculoskeletal Disorders supported by Artificial Intelligence. Hum Interact Emerg Technol (IHIET-AI Artif Intell Futur Appl. 2023;70(70).

10.

Hasan

, Mudey

, Joshi

. Role of Internet of Things (IoT), Artificial Intelligence and Machine Learning in Musculoskeletal Pain: A Scoping ReviewCureus. 2023;15(4):e37352.

11.

Jun-hee

. Search for medical information and treatment options for musculoskeletal disorders through an artificial intelligence chatbot: Focusing on shoulder impingement syndrome. medRxiv. 2022;2012–22.

12.

Winkelmann

, Eberman

, Games

. Telemedicine experiences of athletic trainers and orthopaedic physicians for patients with musculoskeletal conditions. J Athl Train. 2020;55(8):768–79.

13.

Rennie

, Taylor

, Corriero

, Chong

, Sewell

, Hadley

, Arandi

. The current accuracy, cost-effectiveness, and uses of musculoskeletal telehealth and telerehabilitation services. Curr Sports Med Rep. 2022;21(7):247–60.

14.

Qiu

, Li

, Zhang

, Legerlotz

. Use of artificial neural networks in the prognosis of musculoskeletal diseases— a scoping review. BMC Musculoskelet Disord. 2023;24(1):86.

15.

Grünberg

. Etik ve Yaşamın Anlamı. Erdem. 2005;15(44):147–60.

16.

Butts

, Rich

. Nursing ethics: Across the curriculum and into practice. Jones & Bartlett Learning; 2022.

17.

Çabuk

, Işgüden

. Meslek Etiği ve Meslek Etiğinin Meslek Yaşami Üzerindeki Etkileri. Balıkesir Üniversitesi Sos Bilim Enstitüsü Derg. 2006;9(16):59–86.

18.

Erdirençelebi

, Filizöz

. Meslek etiği ve akademisyenlerin etik değerleri üzerine nicel bir araştırma. OPUS Int J Soc Res. 2019;14(20):1228–58.

19.

Janakiram

, Gardens

. Knowledge, attitudes and practices related to healthcare ethics among medical and dental postgraduate students in south India. Indian J Med Ethics. 2014;11(2):99–104.

20.

Özdilek

. Reliability and Validity of Ethical Attitude Scale on Dietitians. SDU Health Sciences Instute; 2023.

21.

Mollaei

, Fujao

, Silva

, Rodrigues

, Cepeda

, Gamboa

. Human-centered explainable artificial intelligence: Automotive occupational health protection profiles in prevention musculoskeletal symptoms. Int J Environ Res Public Health. 2022;19(15):9552.

22.

Bos

, van Tubergen

, Vonkeman

. Telemedicine for patients with rheumatic and musculoskeletal diseases during the COVID-19 pandemic; a positive experience in the Netherlands. Rheumatol Int. 2021;41(3):565–73.

23.

Lind

, Abtahi

, Forsman

. Wearable Motion Capture Devices for the Prevention of Work-Related Musculoskeletal Disorders in Ergonomics— An Overview of Current Applications, Challenges, and Future Opportunities. Sensors. 2023;23(9):4259.

24.

Yamaguchi

FSM

, Miyhara

H de S

, Silva

J dos S

, Rudelli

, Ejnisman

, Gurgel

HM de C

. Ethical and legal aspects of telemedicine applied in orthopedics. Acta Ortopédica Bras. 2022;30:253719.

25.

Cottrell

, Russell

. Telehealth for musculoskeletal physiotherapy. Musculoskelet Sci Pract. 2020;48:102193.

26.

Nittari

, Khuman

, Baldoni

, Pallotta

, Battineni

, Sirignano

, vdTelemedicine practice: Review of the current ethical and legal challenges. Telemed e-Health. 2020;26(12):1427–37.

27.

Cushing

. What Is Telemedicine, Telehealth, and Teletriage. Vet Clin Small Anim Pract. 2022;52(5):1069–80.

28.

Kaplan

. Revisiting health information technology ethical, legal, and social issues and evaluation: Telehealth/telemedicine and COVID-19. Int J Med Inform. 2020;143:104239.

29.

Beheshti

, Kalankesh

, Doshmangir

, Farahbakhsh

. Telehealth in primary health care: A scoping review of the literature. Perspect Heal Inf Manag. 2022;19(1).

30.

Román-Belmonte

, De la Corte-Rodríguez

, Rodríguez-Damiani

, Rodríguez-Merchán

. Artificial Intelligence in Musculoskeletal Conditions. 2021;26(11):1340–8. doi: 10.52586/5027.

31.

Chan

Y-W

, Huang

T-H

, Tsan

Y-T

, Chan

W-C

, Chang

C-H

, Tsai

Y-T

. The risk classification of ergonomic musculoskeletal disorders in work-related repetitive manual handling operations with deep learning approaches. Içinde: International Conference on Pervasive Artificial Intelligence (ICPAI). IEEE; 2020. s.268–71.

32.

Boris

, Abdallah

, Ahrens

, Chelimsky

, Fischer

, vdCreating a data dictionary for pediatric autonomic disorders. Clin Auton Res. 2023;33:301–77.

33.

Tuček

, Vaněček

. Musculoskeletal disorders and working risk factors. Cent Eur J Public Health. 2020;28:S6–11.

34.

Mansoor

, Al Arabia

, Rathore

. Ergonomics and musculoskeletal disorders among health care professionals: Prevention is better than cure. J Pak Med Assoc. 2022;72(6):1243–5.

35.

Soo

, Ang

, Chong

, Tew

, Yahya

. Occupational ergonomics and related musculoskeletal disorders among dentists: A systematic review. Work. 2023;74(2):469–76.

36.

Hämmig

. Work-and stress-related musculoskeletal and sleep disorders among health professionals: A cross-sectional study in a hospital setting in Switzerland. BMC Musculoskelet Disord. 2020;21(1):1–11.

37.

Garg

, Tomar

, Biswas

, Mehta

, Malhotra

. Understanding musculoskeletal disorders through next-generation sequencing. JBJS Rev. 2022;10(4):e21.

38.

Friedenberg

, Kalichman

, Ezra

, Wacht

, Alperovitch-Najenson

. Work-related musculoskeletal disorders and injuries among emergency medical technicians and paramedics: A comprehensive narrative review. Arch Environ Occup Health. 2022;77(1):9–17.

39.

Bucki

, Clay

, Tobiczyk

, Green

. Scoping review of telehealth for musculoskeletal disorders: Applications for the COVID-19 pandemic. J Manipulative Physiol Ther. 2021;44(7):558–65.

40.

Laur

, Wang

. Musculoskeletal trauma and artificial intelligence: Current trends and projections. Skeletal Radiol. 2022;51(2):257–69.

41.

Hull

, Oen-Hsiao

, Spatz

. Focus: Bioethics: Practical and Ethical Considerations in Telehealth: Pitfalls and Opportunities. Yale J Biol Med. 2022;95(3):367.

42.

Keenan

, Tsourtos

, Tieman

. The value of applying ethical principles in telehealth practices: Systematic review. J Med Internet Res. 2021;23(3):e25698.

43.

Özan

, Polat

, Gündüzalp

, Yaraş

. Etik kodlara ilişkin okul yöneticilerinin görüşleri ve etik kod önerileri. Fırat üniversitesi Sos Bilim Derg. 2017;27(1):43–52.

44.

Soer

. Artificial intelligence in musculoskeletal rehabilitation. J Back Musculoskelet Rehabil. 2023;36(5):993–4.

45.

Mutasa

, Paul

. Clinical artificial intelligence applications: Musculoskeletal. Radiol Clin. 2021;59(6):1013–26.

46.

Solimini

, Busardò

, Gibelli

, Sirignano

, Ricci

. Ethical and Legal Challenges of Telemedicine in the Era of the COVID-19 Pandemic. Medicina (B Aires). 2021;57(12):1314.