How does artificial intelligence in radiology improve efficiency and health outcomes?
Tóm tắt
Since the introduction of artificial intelligence (AI) in radiology, the promise has been that it will improve health care and reduce costs. Has AI been able to fulfill that promise? We describe six clinical objectives that can be supported by AI: a more efficient workflow, shortened reading time, a reduction of dose and contrast agents, earlier detection of disease, improved diagnostic accuracy and more personalized diagnostics. We provide examples of use cases including the available scientific evidence for its impact based on a hierarchical model of efficacy. We conclude that the market is still maturing and little is known about the contribution of AI to clinical practice. More real-world monitoring of AI in clinical practice is expected to aid in determining the value of AI and making informed decisions on development, procurement and reimbursement.
Tài liệu tham khảo
Crew B (2020) A closer look at a revered robot. Nature 580:S5–S7
Wilensky GR (2016) Robotic surgery: an example of when newer is not always better but clearly more expensive. Milbank Q 94:43–46
Diagnostic Imaging Analysis Group (2020) AI for radiology. Products. Radboud University Medical Center. https://www.aiforradiology.com. Accessed 15 Jan 2021
Tariq A, Purkayastha S, Padmanaban GP et al (2020) Current clinical applications of artificial intelligence in radiology and their best supporting evidence. J Am Coll Radiol 17:1371–1381
van Leeuwen KG, Schalekamp S, Rutten MJCM et al (2021) Artificial intelligence in radiology: 100 commercially available products and their scientific evidence. Eur Radiol 31:3797–3804. https://doi.org/10.1007/s00330-021-07892-z
Fryback DG, Thornbury JR (1991) The efficacy of diagnostic imaging. Med Decis Mak 11:88–94
Wolff J, Pauling J, Keck A, Baumbach J (2020) The economic impact of artificial intelligence in health care: systematic review. J Med Internet Res 22:e16866
Porter ME (2010) What is value in health care? N Engl J Med 363:2477–2481
Chong LR, Tsai KT, Lee LL et al (2020) Artificial intelligence predictive analytics in the management of outpatient MRI appointment no-shows. AJR Am J Roentgenol 215:1155–1162
Khan FA, Majidulla A, Tavaziva G et al (2020) Chest X-ray analysis with deep learning-based software as a triage test for pulmonary tuberculosis: a prospective study of diagnostic accuracy for culture-confirmed disease. Lancet Digit Health 2:e573–e581
Murphy K, Habib SS, Zaidi SMA et al (2020) Computer aided detection of tuberculosis on chest radiographs: an evaluation of the CAD4TB v6 system. Sci Rep 10:5492
Philipsen RHHM, Sánchez CI, Maduskar P et al (2015) Automated chest-radiography as a triage for Xpert testing in resource-constrained settings: a prospective study of diagnostic accuracy and costs. Sci Rep 5:12215
Qin ZZ, Sander MS, Rai B et al (2019) Using artificial intelligence to read chest radiographs for tuberculosis detection: a multi-site evaluation of the diagnostic accuracy of three deep learning systems. Sci Rep 9:15000
Dembrower K, Wåhlin E, Liu Y et al (2020) Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload: a retrospective simulation study. Lancet Digit Health 2:e468–e474
Lång K, Dustler M, Dahlblom V et al (2021) Identifying normal mammograms in a large screening population using artificial intelligence. Eur Radiol 31:1687–1692. https://doi.org/10.1007/s00330-020-07165-1
Ritchie AJ, Sanghera C, Jacobs C et al (2016) Computer vision tool and technician as first reader of lung cancer screening CT scans. J Thorac Oncol 11:709–717
The Royal College of Radiologists (2018) Clinical radiology UK workforce census report 2018. RCR website. https://www.rcr.ac.uk/publication/clinical-radiology-uk-workforce-census-report-2018. Accessed 4 May 2021
Desai S (2019) Can artificial intelligence help pediatric radiologist burnout? Imaging Technology News. https://www.itnonline.com/article/can-artificial-intelligence-help-pediatric-radiologist-burnout. Accessed 3 Dec 2020
Rodríguez-Ruiz A, Krupinski E, Mordang J-J et al (2018) Detection of breast cancer with mammography: effect of an artificial intelligence support system. Radiology 290:305–314
Martini K, Blüthgen C, Eberhard M et al (2020) Impact of vessel suppressed-CT on diagnostic accuracy in detection of pulmonary metastasis and reading time. Acad Radiol. https://doi.org/10.1016/j.acra.2020.01.014
Kim H, Park CM, Hwang EJ et al (2018) Pulmonary subsolid nodules: value of semi-automatic measurement in diagnostic accuracy, diagnostic reproducibility and nodule classification agreement. Eur Radiol 28:2124–2133
Kim JR, Shim WH, Yoon HM et al (2017) Computerized bone age estimation using deep learning based program: evaluation of the accuracy and efficiency. AJR Am J of Roentgenol 209:1374–1380
Martin DD, Deusch D, Schweizer R et al (2009) Clinical application of automated Greulich-Pyle bone age determination in children with short stature. Pediatr Radiol 39:598–607
Hassan AE, Ringheanu VM, Rabah RR et al (2020) Early experience utilizing artificial intelligence shows significant reduction in transfer times and length of stay in a hub and spoke model. Interv Neuroradiol 26:615–622
Grunwald IQ, Ragoschke-Schumm A, Kettner M et al (2016) First automated stroke imaging evaluation via electronic Alberta stroke program early CT score in a mobile stroke unit. Cerebrovasc Dis 42:332–338
O’Connor SD, Bhalla M (2021) Should artificial intelligence tell radiologists which study to read next? Radiol Artif Intell 3:e210009
Baltruschat I, Steinmeister L, Nickisch H et al (2021) Smart chest X-ray worklist prioritization using artificial intelligence: a clinical workflow simulation. Eur Radiol 31:3837–3845. https://doi.org/10.1007/s00330-020-07480-7
O’Neill TJ, Xi Y, Stehel E et al (2021) Active reprioritization of the reading worklist using artificial intelligence has a beneficial effect on the turnaround time for interpretation of head CT with intracranial hemorrhage. Radiol Artif Intell 3:e200024
Dagan N, Elnekave E, Barda N et al (2020) Automated opportunistic osteoporotic fracture risk assessment using computed tomography scans to aid in FRAX underutilization. Nat Med 26:77–82
Brody AS, Frush DP, Huda W, Brent RL (2007) Radiation risk to children from computed tomography. Pediatrics 120:677–682
Hsieh J, Liu E, Nett B et al (2019) A new era of image reconstruction: TrueFidelity™ technical white paper on deep learning image reconstruction. GE Healthcare online document. https://www.gehealthcare.com/-/jssmedia/040dd213fa89463287155151fdb01922.pdf. Accessed 18 Jan 2021
Willemink MJ, Noël PB (2019) The evolution of image reconstruction for CT — from filtered back projection to artificial intelligence. Eur Radiol 29:2185–2195
Jans LBO, Chen M, Elewaut D et al (2020) MRI-based synthetic CT in the detection of structural lesions in patients with suspected sacroiliitis: comparison with MRI. Radiology 298:343–349
Alshamrani K, Offiah AC (2020) Applicability of two commonly used bone age assessment methods to twenty-first century UK children. Eur Radiol 30:504–513
Chilamkurthy S, Ghosh R, Tanamala S et al (2018) Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. Lancet 392:2388–2396
Rodriguez-Ruiz A, Lång K, Gubern-Merida A et al (2019) Stand-alone artificial intelligence for breast cancer detection in mammography: comparison with 101 radiologists. J Natl Cancer Inst 111:916–922
Schalekamp S, Karssemeijer N, Cats AM et al (2016) The effect of supplementary bone-suppressed chest radiographs on the assessment of a variety of common pulmonary abnormalities: results of an observer study. J Thorac Imaging 31:119–125
Nehrer S, Ljuhar R, Steindl P et al (2019) Automated knee osteoarthritis assessment increases physicians’ agreement rate and accuracy: data from the osteoarthritis initiative. Cartilage. https://doi.org/10.1177/1947603519888793
Rhodius-Meester HFM, van Maurik IS, Koikkalainen J et al (2020) Selection of memory clinic patients for CSF biomarker assessment can be restricted to a quarter of cases by using computerized decision support, without compromising diagnostic accuracy. PLoS One 15:e0226784
Lu Y, Shi XQ, Zhao X et al (2019) Value of computer software for assisting sonographers in the diagnosis of thyroid imaging reporting and data system grade 3 and 4 thyroid space-occupying lesions. J Ultrasound Med 38:3291–3300
Astley SM, Harkness EF, Sergeant JC et al (2018) A comparison of five methods of measuring mammographic density: a case-control study. Breast Cancer Res 20:10
Bakker MF, de Lange SV, Pijnappel RM et al (2019) Supplemental MRI screening for women with extremely dense breast tissue. N Engl J Med 381:2091–2102
French DP, Astley S, Brentnall AR et al (2020) What are the benefits and harms of risk stratified screening as part of the NHS breast screening programme? Study protocol for a multi-site non-randomised comparison of BC-predict versus usual screening (NCT04359420). BMC Cancer 20:570
Hey T, Tansley S, Tolle K (2009) The fourth paradigm: data-intensive scientific discovery. Microsoft website. https://www.microsoft.com/en-us/research/publication/fourth-paradigm-data-intensive-scientific-discovery/. Accessed 4 May 2021
Gerke S, Babic B, Evgeniou T, Cohen IG (2020) The need for a system view to regulate artificial intelligence/machine learning-based software as medical device. NPJ Digit Med 3:53
Larson DB, Harvey H, Rubin DL et al (2020) Regulatory frameworks for development and evaluation of artificial intelligence-based diagnostic imaging algorithms: summary and recommendations. J Am Coll Radiol 18:413–424
United States Food and Drug Administration (2021) Artificial intelligence/machine learning (AI/ML) software as a medical device action plan. FDA website. https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device. Accessed 17 Jan 2021
Lehman CD, Wellman RD, Buist DSM et al (2015) Diagnostic accuracy of digital screening mammography with and without computer-aided detection. JAMA Intern Med 175:1828–1837
Fenton JJ, Taplin SH, Carney PA et al (2007) Influence of computer-aided detection on performance of screening mammography. N Engl J Med 356:1399–1409
Hassan AE (2020) New technology add-on payment (NTAP) for Viz LVO: a win for stroke care. J Neurointerv Surg 13:406–408