Artificial Intelligence Triage of Musculoskeletal Referrals: Accuracy, Equity and Where a Clinician Must Still Look
DOI:
https://doi.org/10.5281/zenodo.22549268Keywords:
Artificial intelligence, Machine learning, Musculoskeletal referral triage, Diagnostic accuracy, Algorithmic bias, Health equityAbstract
Artificial intelligence tools for triaging musculoskeletal referrals, from fracture-detection algorithms
applied to plain radiographs to symptom-checker triage systems, are increasingly proposed as a route to
faster, more consistent referral sorting under sustained demand pressure. Reported diagnostic accuracy
for narrow, well-defined tasks such as fracture detection is often high and, in several studies, comparable
to or better than unassisted clinician performance. This narrative review examines that accuracy
evidence alongside a growing literature documenting algorithmic equity gaps arising from skewed
training data, and considers where a clinician must still look: at the full clinical context these systems
were not trained on, and at the populations these systems are least well validated for.
References
Olczak J, Fahlberg N, Maki A, et al. "Artificial intelligence for analyzing orthopedic trauma radiographs". Acta Orthop 88(2017):581-586.
Chung SW, Han SS, Lee JW, et al. "Automated detection and classification of the proximal humerus fracture by using deep learning algorithm". Acta Orthop 89(2018):468-473.
Kim DH, MacKinnon T. "Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks". Clin Radiol 73(2018):439-445.
Lindsey R, Daluiski A, Chopra S, et al. "Deep neural network improves fracture detection by clinicians". Proc Natl Acad Sci U S A 115(2018):11591-11596.
Duron L, Ducarouge A, Gillibert A, et al. "Assessment of an AI Aid in Detection of Adult Appendicular Skeletal Fractures by Emergency Physicians and Radiologists: A Multicenter Cross-sectional Diagnostic Study". Radiology 300(2021):120-129.
Kuo RYL, Harrison C, Curran TA, et al. "Artificial Intelligence in Fracture Detection: A Systematic Review and Meta-Analysis". Radiology 304(2022):50-62.
Langlotz CP. "Will Artificial Intelligence Replace Radiologists?" Radiol Artif Intell 1(2019):e190058.
Topol EJ. "High-performance medicine: the convergence of human and artificial intelligence". Nat Med 25(2019):44-56.
Obermeyer Z, Powers B, Vogeli C, Mullainathan S. "Dissecting racial bias in an algorithm used to manage the health of populations". Science 366(2019):447-453.
Larrazabal AJ, Nieto N, Peterson V, Milone DH, Ferrante E. "Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis". Proc Natl Acad Sci U S A 117(2020):12592-12594.
Seyyed-Kalantari L, Zhang H, McDermott MBA, Chen IY, Ghassemi M. "Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations". Nat Med 27(2021):2176-2182.
Kaushal A, Altman R, Langlotz C. "Geographic Distribution of US Cohorts Used to Train Deep Learning Algorithms". JAMA 324(2020):1212-1213.
Gichoya JW, Banerjee I, Bhimireddy AR, et al. "AI recognition of patient race in medical imaging: a modelling study". Lancet Digit Health 4(2022):e406-e414.
Char DS, Shah NH, Magnus D. "Implementing Machine Learning in Health Care - Addressing Ethical Challenges". N Engl J Med 378(2018):981-983.
Cabitza F, Rasoini R, Gensini GF. "Unintended Consequences of Machine Learning in Medicine". JAMA 318(2017):517-518.
Sendak MP, Ratliff W, Sarro D, et al. "Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care: Implementation Study". JMIR Med Inform 8(2020):e15182.
Wiens J, Saria S, Sendak M, et al. "Do no harm: a roadmap for responsible machine learning for health care". Nat Med 25(2019):1337-1340.
Vasey B, Nagendran M, Campbell B, et al. "Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI". Nat Med 28(2022):924-933.
Liu X, Faes L, Kale AU, et al. "A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis". Lancet Digit Health 1(2019):e271-e297.
Nagendran M, Chen Y, Lovejoy CA, et al. "Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies". BMJ 368(2020):m689.
Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. "Key challenges for delivering clinical impact with artificial intelligence". BMC Med 17(2019):195.
Challen R, Denny J, Pitt M, Gompels L, Edwards T, Tsaneva-Atanasova K. "Artificial intelligence, bias and clinical safety". BMJ Qual Saf 28(2019):231-237.
Rajpurkar P, Chen E, Banerjee O, Topol EJ. "AI in health and medicine". Nat Med 28(2022):31-38.
Beam AL, Kohane IS. "Big Data and Machine Learning in Health Care". JAMA 319(2018):1317-1318.
Semigran HL, Linder JA, Gidengil C, Mehrotra A. "Evaluation of symptom checkers for self diagnosis and triage: audit study". BMJ 351(2015):h3480.
Razzaki S, Baker A, Perov Y, et al. "A Comparative Study of Artificial Intelligence and Human Doctors for the Purpose of Triage and Diagnosis". arXiv preprint (2018):1806.10698.
Fraser H, Crossland D, Bacher I, Ranney M, Madsen T, Hilliard R. "Comparison of diagnostic and triage accuracy of Ada health and WebMD symptom checkers, ChatGPT, and physicians for patients in an emergency department: clinical data analysis study". JMIR Mhealth Uhealth 11(2023):e49995.
Winn AN, Somai M, Fergestrom N, Crotty BH. "Association of Use of Online Symptom Checkers With Patients' Plans for Seeking Care". JAMA Netw Open 2(2019):e1918561.
Middleton K, Butt M, Hammerla N, Hamblin S, Mehta K, Parsa A. "Sorting out symptoms: design and evaluation of the 'babylon check' automated triage system". arXiv preprint (2016):1606.02041.
Meskó B, Görög M. "A short guide for medical professionals in the era of artificial intelligence". NPJ Digit Med 3(2020):126.
