Integration of Teleradiology and Artificial Intelligence: Opportunities and Challenges
Main Article Content
Abstract
Over the past few decades, radiology has become an increasingly important part of diagnosis and healthcare delivery in general, and correspondingly the burden on radiologists continues to increase with greater imaging volumes and stringent reporting turnaround time expectations, resulting in radiology reporting delays and errors. To bridge the gap, teleradiology has played an important role over the past two decades, but given the mismatch between the dramatically increased utilization of radiologic imaging and the relatively constant number of trained radiologists entering practice, additional solutions are necessary. Teleradiology and Artificial Intelligence have exceptional synergies from the perspective of both being technology enabled healthcare delivery mechanisms that address the same fundamental issue of radiologist shortages. These synergies lead to a multiplier effect in terms of Teleradiology enabling Artificial Intelligence algorithms to be deployed at scale globally over a short time frame to rapidly achieve greater impact than either solution can individually. This article will review the opportunities whereby Artificial Intelligence can further enhance Teleradiology practice, as well as explore the challenges that exist at this time that may potentially delay this process of integration.
Keywords:
Teleradiology, Artificial Intelligence, Medical Imaging, Emergency Radiology, Elective Radiology, Opportunities, Challenges
Article Details
How to Cite
KALYANPUR, Arjun; MATHUR, Neetika.
Integration of Teleradiology and Artificial Intelligence: Opportunities and Challenges.
Medical Research Archives, [S.l.], v. 12, n. 10, oct. 2024.
ISSN 2375-1924.
Available at: <https://esmed.org/MRA/mra/article/view/5904>. Date accessed: 21 dec. 2024.
doi: https://doi.org/10.18103/mra.v12i10.5904.
Section
Research Articles
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
References
1. Shanafelt TD, Hasan O, Dyrbye LN, et al. Changes in Burnout and Satisfaction with Work-Life Balance in Physicians and the General US Working Population Between 2011 and 2014. Mayo Clinic Proceedings. 2015; 90(12): 1600-1613. doi: 10.1016/j.mayocp.2015.08.023
2. Kalyanpur A, Mathur N. Offshore reporting of radiologic examinations supplementing healthcare delivery worthy of Medicare reimbursement. Imaging Radiat Res. 2024 Jun 19;6(1):6404. https://systems.enpress-publisher.com/index.php/IRR/article/view/6404
3. https://arjunkalyanpur.in/radiologist-burnout-and-related-issues/
4. Chetlen AL, Chan TL, Ballard DH, et al. Addressing Burnout in Radiologists. Academic Radiology. 2019; 26(4): 526-533. doi: 10.1016/j.acra.2018.07.001
5. Kalyanpur A. The Role of Teleradiology in Emergency Radiology Provision. Health management. 2014; 14(1).
6. Kalyanpur A, Weinberg J, Neklesa V, Brink JA, Forman HP. Emergency radiology coverage: technical and clinical feasibility of an international teleradiology model. Emergency Radiology. 2003 Dec 1;10(3):115–8.
7. Kalyanpur A and Mathur N. The Role of Teleradiology in Interpretation of Ultrasounds Performed in the Emergency Setting. Digital diagnostics, Jan 2024. https://doi.org/10.17816/DD624586
8. Agrawal A, Sharma M, Sriram S, Blanco A, Nicola R, Kalyanpur A. Imaging of acute scrotal infections, complications and mimics. Emerg Radiol [Internet]. 2024 Jul 11 [cited 2024 Aug 16]; Available from: https://link.springer.com/10.1007/s10140-024-02263-9
9. Kalyanpur, A., Sudhindra, R. R., & Rao, P. (2022). The Role of Mobile Van Mammography Supported by Teleradiology in the Early Diagnosis of Breast Cancer: An Innovative Approach to a Growing Public Health Problem. International Journal of Health Technology and Innovation, 1(03), 2–8. Retrieved from https://ijht.org.in/index.php/ijhti/article/view/30 https://doi.org/10.60142/ijhti.v1i03.30
10. Kalyanpur A., Meka S., Joshi K., TS H., Mathur N. Teleradiology in Tripura: Effectiveness of a Telehealth Model for the Rural Health Sector. International Journal of Health Technology and Innovation. 2022;1(2):7-12. https://ijht.org.in/index.php/ijhti/article/view/36 https://doi.org/10.60142/ijhti.v1i02.36
11. Rudisill KE, Mathur N, Kalyanpur A. A teleradiology network for the improvement of healthcare and patient management in the developing countries of the African continent. Clinical Imaging. 2024 Jul;111:110188. https://doi.org/10.1016/j.clinimag.2024.110188.
12. Kalyanpur A, Rao P, Mathur N. Utilization of Teleradiology by Intensive Care Units: A Cohort Study. Indian Journal of Critical Care Medicine. 2023 Dec 31;28(1):20–5. https://www.ijccm.org/abstractArticleContentBrowse/IJCCM/64/28/1/34854/abstractArticle/Article
13. Hanna TN, Steenburg SD, Rosenkrantz AB, Pyatt RS, Duszak R, Friedberg EB. Emerging Challenges and Opportunities in the Evolution of Teleradiology. American Journal of Roentgenology. 2020 Dec;215(6):1411–6.
14. Kalyanpur A and Mathur N. A Teleradiology System for Early Ischemic and Hemorrhagic Stroke Evaluation and Management. Journal of Clinical Interventional Radiology, 25 July, 2023. ISSN 2457-0214. DOI: 10.1055/s-0043-1771379 https://www.thieme-connect.com/products/ejournals/html/10.1055/s-0043-1771379
15. Najjar, R. (2023). Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging. Diagnostics, 13(17), 2760. https://doi.org/10.3390/diagnostics13172760
16. Khalifa, M., & Albadawy, M. (2024). AI in diagnostic imaging: Revolutionising accuracy and efficiency. Computer Methods and Programs in Biomedicine Update, 5, 100146. https://doi.org/10.1016/j.cmpbup.2024.100146
17. Shoshan, Y., Bakalo, R., Gilboa-Solomon, F., Ratner, V., Barkan, E., Ozery-Flato, M., Amit, M., Khapun, D., Ambinder, E. B., Oluyemi, E. T., Panigrahi, B., DiCarlo, P. A., Rosen-Zvi, M., & Mullen, L. A. (2022). Artificial Intelligence for Reducing Workload in Breast Cancer Screening with Digital Breast Tomosynthesis. Radiology, 303(1), 69–77. https://doi.org/10.1148/radiol.211105
18. Katzman, B. D., Van Der Pol, C. B., Soyer, P., & Patlas, M. N. (2023). Artificial intelligence in emergency radiology: A review of applications and possibilities. Diagnostic and Interventional Imaging, 104(1), 6–10. https://doi.org/10.1016/j.diii.2022.07.005
19. Kolossváry, M., Raghu, V. K., Nagurney, J. T., Hoffmann, U., & Lu, M. T. (2023). Deep Learning Analysis of Chest Radiographs to Triage Patients with Acute Chest Pain Syndrome. Radiology, 306(2), e221926. https://doi.org/10.1148/radiol.221926
20. Weisberg, E. M., Chu, L. C., & Fishman, E. K. (2020). The first use of artificial intelligence (AI) in the ER: Triage not diagnosis. Emergency Radiology, 27(4), 361–366. https://doi.org/10.1007/s10140-020-01773-6
21. Goo, J. M. (2023). Triaging: Another Vital Application of the Deep Learning Technique on Chest Radiographs at the Emergency Department. Radiology, 306(2), e223112. https://doi.org/10.1148/radiol.223112
22. Murphy, D. J., & Tee, S. R. (2023). Expectation Meets Reality: AI-powered CT Pulmonary Angiogram Triage in the Real World. Radiology, 309(1), e232389. https://doi.org/10.1148/radiol.232389
23. Topff, L., Ranschaert, E. R., Bartels-Rutten, A., Negoita, A., Menezes, R., Beets-Tan, R. G. H., & Visser, J. J. (2023). Artificial Intelligence Tool for Detection and Worklist Prioritization Reduces Time to Diagnosis of Incidental Pulmonary Embolism at CT. Radiology: Cardiothoracic Imaging, 5(2), e220163. https://doi.org/10.1148/ryct.220163
24. Makeeva, V., Gichoya, J., Hawkins, C. M., Towbin, A. J., Heilbrun, M., & Prater, A. (2019). The Application of Machine Learning to Quality Improvement Through the Lens of the Radiology Value Network. Journal of the American College of Radiology, 16(9), 1254–1258. https://doi.org/10.1016/j.jacr.2019.05.039
25. Jorg, T., Halfmann, M. C., Stoehr, F., Arnhold, G., Theobald, A., Mildenberger, P., & Müller, L. (2024). A novel reporting workflow for automated integration of artificial intelligence results into structured radiology reports. Insights into Imaging, 15(1), 80. https://doi.org/10.1186/s13244-024-01660-5
26. Sacoransky, E., Kwan, B. Y. M., & Soboleski, D. (2024). ChatGPT and assistive AI in structured radiology reporting: A systematic review. Current Problems in Diagnostic Radiology, S0363018824001130. https://doi.org/10.1067/j.cpradiol.2024.07.007
27. Bhayana, R. (2024). Chatbots and Large Language Models in Radiology: A Practical Primer for Clinical and Research Applications. Radiology, 310(1), e232756. https://doi.org/10.1148/radiol.232756
28. Agrawal D, Joshi S, Poonamallee L. Automated Midline Shift Detection and Quantification in Traumatic Brain Injury: A Comprehensive Review. Indian Journal of Neurotrauma. 2024;21(01):006-012. doi:10.1055/s-0043-1777676
29. Seyam M, Weikert T, Sauter A, Brehm A, Psychogios MN, Blackham KA. Utilization of Artificial Intelligence–based Intracranial Hemorrhage Detection on Emergent Noncontrast CT Images in Clinical Workflow. Radiology: Artificial Intelligence. 2022;4(2):e210168. doi:10.1148/ryai.210168
30. Shinohara Y, Takahashi N, Lee Y, Ohmura T, Kinoshita T. Development of a deep learning model to identify hyperdense MCA sign in patients with acute ischemic stroke. Jpn J Radiol. 2020;38(2):112-117. doi:10.1007/s11604-019-00894-4
31. Sugibayashi T, Walston SL, Matsumoto T, Mitsuyama Y, Miki Y, Ueda D. Deep learning for pneumothorax diagnosis: a systematic review and meta-analysis. Eur Respir Rev. 2023;32(168):220259. doi:10.1183/16000617.0259-2022
32. Allena N, Khanal S. The Algorithmic Lung Detective: Artificial Intelligence in the Diagnosis of Pulmonary Embolism. Cureus. 2023;15(12):e51006. doi:10.7759/cureus.51006
33. Mastrodicasa D, Codari M, Bäumler K, et al. Artificial Intelligence Applications in Aortic Dissection Imaging. Seminars in Roentgenology. 2022;57(4):357-363. doi:10.1053/j.ro.2022.07.001
34. Stewart, J., Lu, J., Goudie, A., Bennamoun, M., Sprivulis, P., Sanfillipo, F., & Dwivedi, G. (2021). Applications of machine learning to undifferentiated chest pain in the emergency department: A systematic review. PLOS ONE, 16(8), e0252612. https://doi.org/10.1371/journal.pone.0252612
35. Shen X, Zhou Y, Shi X, Zhang S, Ding S, Ni L, et al. The application of deep learning in abdominal trauma diagnosis by CT imaging. World J Emerg Surg. 2024 May 6;19(1):17.
36. Yao J, Chu LC, Patlas M. Applications of Artificial Intelligence in Acute Abdominal Imaging. Can Assoc Radiol J. 2024 May 7;08465371241250197.
37. Shoshan, Y., Bakalo, R., Gilboa-Solomon, F., Ratner, V., Barkan, E., Ozery-Flato, M., Amit, M., Khapun, D., Ambinder, E. B., Oluyemi, E. T., Panigrahi, B., DiCarlo, P. A., Rosen-Zvi, M., & Mullen, L. A. (2022). Artificial Intelligence for Reducing Workload in Breast Cancer Screening with Digital Breast Tomosynthesis. Radiology, 303(1), 69–77. https://doi.org/10.1148/radiol.211105
38. Lehman, C. D., Yala, A., Schuster, T., Dontchos, B., Bahl, M., Swanson, K., & Barzilay, R. (2019). Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation. Radiology, 290(1), 52–58. https://doi.org/10.1148/radiol.2018180694
39. Ghorbian, M., & Ghorbian, S. (2023). Usefulness of machine learning and deep learning approaches in screening and early detection of breast cancer. Heliyon, 9(12), e22427. https://doi.org/10.1016/j.heliyon.2023.e22427
40. Alelyani, T., Alshammari, M. M., Almuhanna, A., & Asan, O. (2024). Explainable Artificial Intelligence in Quantifying Breast Cancer Factors: Saudi Arabia Context. Healthcare, 12(10), 1025. https://doi.org/10.3390/healthcare12101025
41. Youk, J. H., Gweon, H. M., Son, E. J., & Kim, J.-A. (2016). Automated Volumetric Breast Density Measurements in the Era of the BI-RADS Fifth Edition: A Comparison With Visual Assessment. American Journal of Roentgenology, 206(5), 1056–1062. https://doi.org/10.2214/AJR.15.15472
42. Huber, F. A., & Guggenberger, R. (2022). AI MSK clinical applications: Spine imaging. Skeletal Radiology, 51(2), 279–291. https://doi.org/10.1007/s00256-021-03862-0
43. Martín-Noguerol, T., Oñate Miranda, M., Amrhein, T. J., Paulano-Godino, F., Xiberta, P., Vilanova, J. C., & Luna, A. (2023). The role of Artificial intelligence in the assessment of the spine and spinal cord. European Journal of Radiology, 161, 110726. https://doi.org/10.1016/j.ejrad.2023.110726
44. Cui, Y., Zhu, J., Duan, Z., Liao, Z., Wang, S., & Liu, W. (2022). Artificial Intelligence in Spinal Imaging: Current Status and Future Directions. International Journal of Environmental Research and Public Health, 19(18), 11708. https://doi.org/10.3390/ijerph191811708
45. Tang, J. S. N., Lai, J. K. C., Bui, J., Wang, W., Simkin, P., Gai, D., Chan, J., Pascoe, D. M., Heinze, S. B., Gaillard, F., & Lui, E. (2023). Impact of Different Artificial Intelligence User Interfaces on Lung Nodule and Mass Detection on Chest Radiographs. Radiology: Artificial Intelligence, 5(3), e220079. https://doi.org/10.1148/ryai.220079
46. Nam, J. G., Hwang, E. J., Kim, J., Park, N., Lee, E. H., Kim, H. J., Nam, M., Lee, J. H., Park, C. M., & Goo, J. M. (2023). AI Improves Nodule Detection on Chest Radiographs in a Health Screening Population: A Randomized Controlled Trial. Radiology, 307(2), e221894. https://doi.org/10.1148/radiol.221894
47. Kim, R. Y., Oke, J. L., Pickup, L. C., Munden, R. F., Dotson, T. L., Bellinger, C. R., Cohen, A., Simoff, M. J., Massion, P. P., Filippini, C., Gleeson, F. V., & Vachani, A. (2022). Artificial Intelligence Tool for Assessment of Indeterminate Pulmonary Nodules Detected with CT. Radiology, 304(3), 683–691. https://doi.org/10.1148/radiol.212182
48. Yanagawa, M. (2022). Artificial Intelligence Improves Radiologist Performance for Predicting Malignancy at Chest CT. Radiology, 304(3), 692–693. https://doi.org/10.1148/radiol.220571
49. Annarumma, M., Withey, S. J., Bakewell, R. J., Pesce, E., Goh, V., & Montana, G. (2019). Automated Triaging of Adult Chest Radiographs with Deep Artificial Neural Networks. Radiology, 291(1), 196–202. https://doi.org/10.1148/radiol.2018180921
50. Ranschaert, E., Topff, L., & Pianykh, O. (2021). Optimization of Radiology Workflow with Artificial Intelligence. Radiologic Clinics of North America, 59(6), 955–966. https://doi.org/10.1016/j.rcl.2021.06.006
51. Kapoor, N., Lacson, R., & Khorasani, R. (2020). Workflow Applications of Artificial Intelligence in Radiology and an Overview of Available Tools. Journal of the American College of Radiology, 17(11), 1363–1370. https://doi.org/10.1016/j.jacr.2020.08.016
52. Ritenour, E. R. (2023). Hacking and Artificial Intelligence in Radiology: Basic Principles of Data Integrity and Security. Contemporary Diagnostic Radiology, 46(5), 1–7. https://doi.org/10.1097/01.CDR.0000920216.83604.ce
53. Giansanti D. The Regulation of Artificial Intelligence in Digital Radiology in the Scientific Literature: A Narrative Review of Reviews. Healthcare. 2022;10(10):1824. doi:10.3390/healthcare10101824
54. Zhang K, Khosravi B, Vahdati S, Erickson BJ. FDA Review of Radiologic AI Algorithms: Process and Challenges. Radiology. 2024;310(1):e230242. doi:10.1148/radiol.230242
55. Rowell C, Sebro R. Who Will Get Paid for Artificial Intelligence in Medicine? Radiology: Artificial Intelligence. 2022;4(5):e220054. doi:10.1148/ryai.220054
56. Chen MM, Golding LP, Nicola GN. Who Will Pay for AI? Radiology: Artificial Intelligence. 2021;3(3):e210030. doi:10.1148/ryai.2021210030
2. Kalyanpur A, Mathur N. Offshore reporting of radiologic examinations supplementing healthcare delivery worthy of Medicare reimbursement. Imaging Radiat Res. 2024 Jun 19;6(1):6404. https://systems.enpress-publisher.com/index.php/IRR/article/view/6404
3. https://arjunkalyanpur.in/radiologist-burnout-and-related-issues/
4. Chetlen AL, Chan TL, Ballard DH, et al. Addressing Burnout in Radiologists. Academic Radiology. 2019; 26(4): 526-533. doi: 10.1016/j.acra.2018.07.001
5. Kalyanpur A. The Role of Teleradiology in Emergency Radiology Provision. Health management. 2014; 14(1).
6. Kalyanpur A, Weinberg J, Neklesa V, Brink JA, Forman HP. Emergency radiology coverage: technical and clinical feasibility of an international teleradiology model. Emergency Radiology. 2003 Dec 1;10(3):115–8.
7. Kalyanpur A and Mathur N. The Role of Teleradiology in Interpretation of Ultrasounds Performed in the Emergency Setting. Digital diagnostics, Jan 2024. https://doi.org/10.17816/DD624586
8. Agrawal A, Sharma M, Sriram S, Blanco A, Nicola R, Kalyanpur A. Imaging of acute scrotal infections, complications and mimics. Emerg Radiol [Internet]. 2024 Jul 11 [cited 2024 Aug 16]; Available from: https://link.springer.com/10.1007/s10140-024-02263-9
9. Kalyanpur, A., Sudhindra, R. R., & Rao, P. (2022). The Role of Mobile Van Mammography Supported by Teleradiology in the Early Diagnosis of Breast Cancer: An Innovative Approach to a Growing Public Health Problem. International Journal of Health Technology and Innovation, 1(03), 2–8. Retrieved from https://ijht.org.in/index.php/ijhti/article/view/30 https://doi.org/10.60142/ijhti.v1i03.30
10. Kalyanpur A., Meka S., Joshi K., TS H., Mathur N. Teleradiology in Tripura: Effectiveness of a Telehealth Model for the Rural Health Sector. International Journal of Health Technology and Innovation. 2022;1(2):7-12. https://ijht.org.in/index.php/ijhti/article/view/36 https://doi.org/10.60142/ijhti.v1i02.36
11. Rudisill KE, Mathur N, Kalyanpur A. A teleradiology network for the improvement of healthcare and patient management in the developing countries of the African continent. Clinical Imaging. 2024 Jul;111:110188. https://doi.org/10.1016/j.clinimag.2024.110188.
12. Kalyanpur A, Rao P, Mathur N. Utilization of Teleradiology by Intensive Care Units: A Cohort Study. Indian Journal of Critical Care Medicine. 2023 Dec 31;28(1):20–5. https://www.ijccm.org/abstractArticleContentBrowse/IJCCM/64/28/1/34854/abstractArticle/Article
13. Hanna TN, Steenburg SD, Rosenkrantz AB, Pyatt RS, Duszak R, Friedberg EB. Emerging Challenges and Opportunities in the Evolution of Teleradiology. American Journal of Roentgenology. 2020 Dec;215(6):1411–6.
14. Kalyanpur A and Mathur N. A Teleradiology System for Early Ischemic and Hemorrhagic Stroke Evaluation and Management. Journal of Clinical Interventional Radiology, 25 July, 2023. ISSN 2457-0214. DOI: 10.1055/s-0043-1771379 https://www.thieme-connect.com/products/ejournals/html/10.1055/s-0043-1771379
15. Najjar, R. (2023). Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging. Diagnostics, 13(17), 2760. https://doi.org/10.3390/diagnostics13172760
16. Khalifa, M., & Albadawy, M. (2024). AI in diagnostic imaging: Revolutionising accuracy and efficiency. Computer Methods and Programs in Biomedicine Update, 5, 100146. https://doi.org/10.1016/j.cmpbup.2024.100146
17. Shoshan, Y., Bakalo, R., Gilboa-Solomon, F., Ratner, V., Barkan, E., Ozery-Flato, M., Amit, M., Khapun, D., Ambinder, E. B., Oluyemi, E. T., Panigrahi, B., DiCarlo, P. A., Rosen-Zvi, M., & Mullen, L. A. (2022). Artificial Intelligence for Reducing Workload in Breast Cancer Screening with Digital Breast Tomosynthesis. Radiology, 303(1), 69–77. https://doi.org/10.1148/radiol.211105
18. Katzman, B. D., Van Der Pol, C. B., Soyer, P., & Patlas, M. N. (2023). Artificial intelligence in emergency radiology: A review of applications and possibilities. Diagnostic and Interventional Imaging, 104(1), 6–10. https://doi.org/10.1016/j.diii.2022.07.005
19. Kolossváry, M., Raghu, V. K., Nagurney, J. T., Hoffmann, U., & Lu, M. T. (2023). Deep Learning Analysis of Chest Radiographs to Triage Patients with Acute Chest Pain Syndrome. Radiology, 306(2), e221926. https://doi.org/10.1148/radiol.221926
20. Weisberg, E. M., Chu, L. C., & Fishman, E. K. (2020). The first use of artificial intelligence (AI) in the ER: Triage not diagnosis. Emergency Radiology, 27(4), 361–366. https://doi.org/10.1007/s10140-020-01773-6
21. Goo, J. M. (2023). Triaging: Another Vital Application of the Deep Learning Technique on Chest Radiographs at the Emergency Department. Radiology, 306(2), e223112. https://doi.org/10.1148/radiol.223112
22. Murphy, D. J., & Tee, S. R. (2023). Expectation Meets Reality: AI-powered CT Pulmonary Angiogram Triage in the Real World. Radiology, 309(1), e232389. https://doi.org/10.1148/radiol.232389
23. Topff, L., Ranschaert, E. R., Bartels-Rutten, A., Negoita, A., Menezes, R., Beets-Tan, R. G. H., & Visser, J. J. (2023). Artificial Intelligence Tool for Detection and Worklist Prioritization Reduces Time to Diagnosis of Incidental Pulmonary Embolism at CT. Radiology: Cardiothoracic Imaging, 5(2), e220163. https://doi.org/10.1148/ryct.220163
24. Makeeva, V., Gichoya, J., Hawkins, C. M., Towbin, A. J., Heilbrun, M., & Prater, A. (2019). The Application of Machine Learning to Quality Improvement Through the Lens of the Radiology Value Network. Journal of the American College of Radiology, 16(9), 1254–1258. https://doi.org/10.1016/j.jacr.2019.05.039
25. Jorg, T., Halfmann, M. C., Stoehr, F., Arnhold, G., Theobald, A., Mildenberger, P., & Müller, L. (2024). A novel reporting workflow for automated integration of artificial intelligence results into structured radiology reports. Insights into Imaging, 15(1), 80. https://doi.org/10.1186/s13244-024-01660-5
26. Sacoransky, E., Kwan, B. Y. M., & Soboleski, D. (2024). ChatGPT and assistive AI in structured radiology reporting: A systematic review. Current Problems in Diagnostic Radiology, S0363018824001130. https://doi.org/10.1067/j.cpradiol.2024.07.007
27. Bhayana, R. (2024). Chatbots and Large Language Models in Radiology: A Practical Primer for Clinical and Research Applications. Radiology, 310(1), e232756. https://doi.org/10.1148/radiol.232756
28. Agrawal D, Joshi S, Poonamallee L. Automated Midline Shift Detection and Quantification in Traumatic Brain Injury: A Comprehensive Review. Indian Journal of Neurotrauma. 2024;21(01):006-012. doi:10.1055/s-0043-1777676
29. Seyam M, Weikert T, Sauter A, Brehm A, Psychogios MN, Blackham KA. Utilization of Artificial Intelligence–based Intracranial Hemorrhage Detection on Emergent Noncontrast CT Images in Clinical Workflow. Radiology: Artificial Intelligence. 2022;4(2):e210168. doi:10.1148/ryai.210168
30. Shinohara Y, Takahashi N, Lee Y, Ohmura T, Kinoshita T. Development of a deep learning model to identify hyperdense MCA sign in patients with acute ischemic stroke. Jpn J Radiol. 2020;38(2):112-117. doi:10.1007/s11604-019-00894-4
31. Sugibayashi T, Walston SL, Matsumoto T, Mitsuyama Y, Miki Y, Ueda D. Deep learning for pneumothorax diagnosis: a systematic review and meta-analysis. Eur Respir Rev. 2023;32(168):220259. doi:10.1183/16000617.0259-2022
32. Allena N, Khanal S. The Algorithmic Lung Detective: Artificial Intelligence in the Diagnosis of Pulmonary Embolism. Cureus. 2023;15(12):e51006. doi:10.7759/cureus.51006
33. Mastrodicasa D, Codari M, Bäumler K, et al. Artificial Intelligence Applications in Aortic Dissection Imaging. Seminars in Roentgenology. 2022;57(4):357-363. doi:10.1053/j.ro.2022.07.001
34. Stewart, J., Lu, J., Goudie, A., Bennamoun, M., Sprivulis, P., Sanfillipo, F., & Dwivedi, G. (2021). Applications of machine learning to undifferentiated chest pain in the emergency department: A systematic review. PLOS ONE, 16(8), e0252612. https://doi.org/10.1371/journal.pone.0252612
35. Shen X, Zhou Y, Shi X, Zhang S, Ding S, Ni L, et al. The application of deep learning in abdominal trauma diagnosis by CT imaging. World J Emerg Surg. 2024 May 6;19(1):17.
36. Yao J, Chu LC, Patlas M. Applications of Artificial Intelligence in Acute Abdominal Imaging. Can Assoc Radiol J. 2024 May 7;08465371241250197.
37. Shoshan, Y., Bakalo, R., Gilboa-Solomon, F., Ratner, V., Barkan, E., Ozery-Flato, M., Amit, M., Khapun, D., Ambinder, E. B., Oluyemi, E. T., Panigrahi, B., DiCarlo, P. A., Rosen-Zvi, M., & Mullen, L. A. (2022). Artificial Intelligence for Reducing Workload in Breast Cancer Screening with Digital Breast Tomosynthesis. Radiology, 303(1), 69–77. https://doi.org/10.1148/radiol.211105
38. Lehman, C. D., Yala, A., Schuster, T., Dontchos, B., Bahl, M., Swanson, K., & Barzilay, R. (2019). Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation. Radiology, 290(1), 52–58. https://doi.org/10.1148/radiol.2018180694
39. Ghorbian, M., & Ghorbian, S. (2023). Usefulness of machine learning and deep learning approaches in screening and early detection of breast cancer. Heliyon, 9(12), e22427. https://doi.org/10.1016/j.heliyon.2023.e22427
40. Alelyani, T., Alshammari, M. M., Almuhanna, A., & Asan, O. (2024). Explainable Artificial Intelligence in Quantifying Breast Cancer Factors: Saudi Arabia Context. Healthcare, 12(10), 1025. https://doi.org/10.3390/healthcare12101025
41. Youk, J. H., Gweon, H. M., Son, E. J., & Kim, J.-A. (2016). Automated Volumetric Breast Density Measurements in the Era of the BI-RADS Fifth Edition: A Comparison With Visual Assessment. American Journal of Roentgenology, 206(5), 1056–1062. https://doi.org/10.2214/AJR.15.15472
42. Huber, F. A., & Guggenberger, R. (2022). AI MSK clinical applications: Spine imaging. Skeletal Radiology, 51(2), 279–291. https://doi.org/10.1007/s00256-021-03862-0
43. Martín-Noguerol, T., Oñate Miranda, M., Amrhein, T. J., Paulano-Godino, F., Xiberta, P., Vilanova, J. C., & Luna, A. (2023). The role of Artificial intelligence in the assessment of the spine and spinal cord. European Journal of Radiology, 161, 110726. https://doi.org/10.1016/j.ejrad.2023.110726
44. Cui, Y., Zhu, J., Duan, Z., Liao, Z., Wang, S., & Liu, W. (2022). Artificial Intelligence in Spinal Imaging: Current Status and Future Directions. International Journal of Environmental Research and Public Health, 19(18), 11708. https://doi.org/10.3390/ijerph191811708
45. Tang, J. S. N., Lai, J. K. C., Bui, J., Wang, W., Simkin, P., Gai, D., Chan, J., Pascoe, D. M., Heinze, S. B., Gaillard, F., & Lui, E. (2023). Impact of Different Artificial Intelligence User Interfaces on Lung Nodule and Mass Detection on Chest Radiographs. Radiology: Artificial Intelligence, 5(3), e220079. https://doi.org/10.1148/ryai.220079
46. Nam, J. G., Hwang, E. J., Kim, J., Park, N., Lee, E. H., Kim, H. J., Nam, M., Lee, J. H., Park, C. M., & Goo, J. M. (2023). AI Improves Nodule Detection on Chest Radiographs in a Health Screening Population: A Randomized Controlled Trial. Radiology, 307(2), e221894. https://doi.org/10.1148/radiol.221894
47. Kim, R. Y., Oke, J. L., Pickup, L. C., Munden, R. F., Dotson, T. L., Bellinger, C. R., Cohen, A., Simoff, M. J., Massion, P. P., Filippini, C., Gleeson, F. V., & Vachani, A. (2022). Artificial Intelligence Tool for Assessment of Indeterminate Pulmonary Nodules Detected with CT. Radiology, 304(3), 683–691. https://doi.org/10.1148/radiol.212182
48. Yanagawa, M. (2022). Artificial Intelligence Improves Radiologist Performance for Predicting Malignancy at Chest CT. Radiology, 304(3), 692–693. https://doi.org/10.1148/radiol.220571
49. Annarumma, M., Withey, S. J., Bakewell, R. J., Pesce, E., Goh, V., & Montana, G. (2019). Automated Triaging of Adult Chest Radiographs with Deep Artificial Neural Networks. Radiology, 291(1), 196–202. https://doi.org/10.1148/radiol.2018180921
50. Ranschaert, E., Topff, L., & Pianykh, O. (2021). Optimization of Radiology Workflow with Artificial Intelligence. Radiologic Clinics of North America, 59(6), 955–966. https://doi.org/10.1016/j.rcl.2021.06.006
51. Kapoor, N., Lacson, R., & Khorasani, R. (2020). Workflow Applications of Artificial Intelligence in Radiology and an Overview of Available Tools. Journal of the American College of Radiology, 17(11), 1363–1370. https://doi.org/10.1016/j.jacr.2020.08.016
52. Ritenour, E. R. (2023). Hacking and Artificial Intelligence in Radiology: Basic Principles of Data Integrity and Security. Contemporary Diagnostic Radiology, 46(5), 1–7. https://doi.org/10.1097/01.CDR.0000920216.83604.ce
53. Giansanti D. The Regulation of Artificial Intelligence in Digital Radiology in the Scientific Literature: A Narrative Review of Reviews. Healthcare. 2022;10(10):1824. doi:10.3390/healthcare10101824
54. Zhang K, Khosravi B, Vahdati S, Erickson BJ. FDA Review of Radiologic AI Algorithms: Process and Challenges. Radiology. 2024;310(1):e230242. doi:10.1148/radiol.230242
55. Rowell C, Sebro R. Who Will Get Paid for Artificial Intelligence in Medicine? Radiology: Artificial Intelligence. 2022;4(5):e220054. doi:10.1148/ryai.220054
56. Chen MM, Golding LP, Nicola GN. Who Will Pay for AI? Radiology: Artificial Intelligence. 2021;3(3):e210030. doi:10.1148/ryai.2021210030