Home⇛International Journal of Transformative Multidisciplinary Studies⇛vol. 2 no. 2 (2026)

From Image Acquisition to Algorithmic Accountability: Reframing Research Methodology in Radiologic Technology for the Age of Artificial Intelligence

Mark Alipio

Discipline: healthcare science (non-specific)

 

Abstract:

1 Iligan Medical Center College, Philippines * mark.alipio@imcc. edu.ph Volume 2, Issue 2, June 2026 Artificial intelligence has progressed from experimental image classification to tools influencing acquisition, triage, reconstruction, image quality assessment, dose optimization, workflow, education, and departmental governance. However, medical imaging literature often remains model-centered, emphasizing area under the curve, sensitivity, specificity, or reader performance, while many Radiologic Technology studies focus on perceptions, readiness, and attitudes. This narrative review reframes AI-related Radiologic Technology research as a methodological issue rather than a technology-adoption topic. It argues that radiologic technologists shape how imaging data are produced, interpreted, repeated, rejected, archived, and used for algorithmic learning. Thus, AI-era research must treat acquisition protocols, positioning, exposure parameters, dose indices, patient preparation, equipment variation, image quality, workflow behavior, trust, override decisions, and post-deployment monitoring as core design elements. Synthesizing recent AI reporting standards and evaluation frameworks, the review identifies gaps in validation, human-AI interaction, implementation, generalizability, equity, accountability, and AI literacy. It proposes the RADIATE-AI framework to guide safer, locally valid, and methodologically mature studies.



References:

  1. Aldhafeeri, F. M. (2022). Perspectives of radiographers on the emergence of artificial intelligence in diagnostic imaging in Saudi Arabia. Insights into Imaging, 13(1), 178. https://doi.org/10.1186/s13244-022-01319-z
  2. Antun, V., Renna, F., Poon, C., Adcock, B., & Hansen, A. C. (2020). On instabilities of deep learning in image reconstruction and the potential costs of AI. Proceedings of the National Academy of Sciences, 117(48), 30088-30095. https://doi.org/10.1073/pnas.1907377117
  3. Beam, A. L., & Kohane, I. S. (2018). Big data and machine learning in health care. JAMA, 319(13), 1317-1318. https://doi.org/10.1001/jama.2017.18391
  4. Benjamens, S., Dhunnoo, P., & Mesko, B. (2020). The state of artificial intelligence-based FDA-approved medical devices and algorithms: An online database. npj Digital Medicine, 3, 118. https://doi.org/10.1038/s41746-020-00324-0
  5. Botwe, B. O., Antwi, W. K., Arkoh, S., & Akudjedu, T. N. (2021). Radiographers’ perspectives on the emerging integration of artificial intelligence into diagnostic imaging: The Ghana study. Journal of Medical Radiation Sciences, 68(3), 260-268. https://doi.org/10.1002/jmrs.460
  6. Cabitza, F., Rasoini, R., & Gensini, G. F. (2017). Unintended consequences of machine learning in medicine. JAMA, 318(6), 517-518. https://doi.org/10.1001/jama.2017.7797
  7. Challen, R., Denny, J., Pitt, M., Gompels, L., Edwards, T., & Tsaneva-Atanasova, K. (2019). Artificial intelligence, bias and clinical safety. BMJ Quality & Safety, 28(3), 231-237. https://doi.org/10.1136/bmjqs-2018-008370
  8. Char, D. S., Shah, N. H., & Magnus, D. (2018). Implementing machine learning in health care - Addressing ethical challenges. The New England Journal of Medicine, 378(11), 981-983. https://doi.org/10.1056/NEJMp1714229
  9. Chen, H., Zhang, Y., Kalra, M. K., Lin, F., Chen, Y., Liao, P., Zhou, J., & Wang, G. (2017). Low-dose CT with a residual encoder-decoder convolutional neural network. IEEE Transactions on Medical Imaging, 36(12), 2524-2535. https://doi.org/10.1109/TMI.2017.2715284
  10. Chen, M., Wang, Y., Wang, Q., Shi, J., Wang, H., Ye, Z., Xue, P., & Qiao, Y. (2024). Impact of human and artificial intelligence collaboration on workload reduction in medical image interpretation. npj Digital Medicine, 7, 349. https://doi.org/10.1038/s41746-024-01328-w
  11. Cohen, J. F., Korevaar, D. A., Altman, D. G., Bruns, D. E., Gatsonis, C. A., Hooft, L., Irwig, L., Levine, D., Reitsma, J. B., de Vet, H. C. W., & Bossuyt, P. M. M. (2016). STARD 2015 guidelines for reporting diagnostic accuracy studies: Explanation and elaboration. BMJ Open, 6(11), e012799. https://doi.org/10.1136/bmjopen-2016-012799
  12. Collins, G. S., Moons, K. G. M., Dhiman, P., Riley, R. D., Beam, A. L., Van Calster, B., Ghassemi, M., Liu, X., Reitsma, J. B., van Smeden, M., & others (2024). TRIPOD+AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ, 385, e078378. https://doi.org/10.1136/bmj-2023-078378
  13. Cross, J. L., Mansfield, K. E., Platt, R. W., & others (2024). Bias in medical artificial intelligence: Implications for health equity. PLOS Digital Health, 3(11), e0000651. https://doi.org/10.1371/journal.pdig.0000651
  14. Cruz Rivera, S., Liu, X., Chan, A. W., Denniston, A. K., Calvert, M. J., & SPIRIT-AI and CONSORT-AI Working Group (2020). Guidelines for clinical trial protocols for interventions involving artificial intelligence: The SPIRIT-AI extension. Nature Medicine, 26(9), 1351-1363. https://doi.org/10.1038/s41591-020-1037-7
  15. Dean, J. C., Gichoya, J. W., Wiggins, W. F., & Safdar, N. M. (2025). Real-world monitoring of artificial intelligence in radiology. Korean Journal of Radiology, 26(1), 1-12. https://doi.org/10.3348/kjr.2025.0962
  16. DeGrave, A. J., Janizek, J. D., & Lee, S. I. (2021). AI for radiographic COVID-19 detection selects shortcuts over signal. Nature Machine Intelligence, 3(7), 610-619. https://doi.org/10.1038/s42256-021-00338-7
  17. Dikici, E., Bigelow, M., Prevedello, L. M., White, R. D., & Erdal, B. S. (2020). Integrating AI into radiology workflow: Levels of research, production, and feedback maturity. Journal of Medical Imaging, 7(1), 016502. https://doi.org/10.1117/1.JMI.7.1.016502
  18. Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24-29. https://doi.org/10.1038/s41591-018-0316-z
  19. Geis, J. R., Brady, A. P., Wu, C. C., Spencer, J., Ranschaert, E., Jaremko, J. L., Langer, S. G., Borondy Kitts, A., Birch, J., Shields, W. F., van den Hoven van Genderen, R., Kotter, E., Wawira Gichoya, J., Cook, T. S., Morgan, M. B., Tang, A., Safdar, N. M., & Kohli, M. (2019). Ethics of artificial intelligence in radiology: Summary of the joint European and North American multisociety statement. Radiology, 293(2), 436-440. https://doi.org/10.1148/radiol.2019191586
  20. Gichoya, J. W., Banerjee, I., Bhimireddy, A. R., Burns, J. L., Celi, L. A., Chen, L. C., Correa, R., Dullerud, N., Ghassemi, M., Huang, S. C., Kuo, P. C., Lungren, M. P., Palmer, L. J., Price, B. J., Purkayastha, S., Pyrros, A. T., Oakden-Rayner, L., Okechukwu, C., Seyyed-Kalantari, L., & Trivedi, H. (2022). AI recognition of patient race in medical imaging: A modelling study. The Lancet Digital Health, 4(6), e406-e414. https://doi.org/10.1016/S2589-7500(22)00063-2
  21. Hardy, M., & Harvey, H. (2020). Artificial intelligence in diagnostic imaging: Impact on the radiography profession. The British Journal of Radiology, 93(1108), 20190840. https://doi.org/10.1259/bjr.20190840
  22. Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. W. L. (2018). Artificial intelligence in radiology. Nature Reviews Cancer, 18(8), 500-510. https://doi.org/10.1038/s41568-018-0016-5
  23. Hwang, E. J., Park, S., Jin, K. N., Kim, J. I., Choi, S. Y., Lee, J. H., Goo, J. M., Aum, J., Yim, J. J., Park, C. M., & others (2019). Development and validation of a deep learning-based automated detection algorithm for major thoracic diseases on chest radiographs. JAMA Network Open, 2(3), e191095. https://doi.org/10.1001/jamanetworkopen.2019.1095
  24. Ibrahim, H., Liu, X., Zariffa, N., Morris, A. D., & Denniston, A. K. (2021). Health data poverty: An assailable barrier to equitable digital health care. The Lancet Digital Health, 3(4), e260-e265. https://doi.org/10.1016/S2589-7500(20)30317-4
  25. Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine, 17, 195. https://doi.org/10.1186/s12916-019-1426-2
  26. Kore, A., Fleming, S. L., Moore, J. H., & others (2024). Data drift in medical machine learning: Implications and mitigation strategies. Nature Communications, 15, 1887. https://doi.org/10.1038/s41467-024-46142-w
  27. Lång, K., Josefsson, V., Larsson, A. M., Larsson, S., Högberg, C., Sartor, H., Hofvind, S., Andersson, I., & Dustler, M. (2023). Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial. The Lancet Oncology, 24(8), 936-944. https://doi.org/10.1016/S1470-2045(23)00298-X
  28. Lekadir, K., Frangi, A. F., Porras, A. R., Glocker, B., Cintas, C., Langlotz, C. P., & FUTURE-AI Consortium (2025). FUTURE-AI: Guiding principles and consensus recommendations for trustworthy artificial intelligence in health care. BMJ, 388, e081554. https://doi.org/10.1136/bmj2024-081554
  29. Lindqwister, A. L., Hassanpour, S., Lewis, P. J., & others (2023). AI-RADS: An artificial intelligence curriculum for residents. Frontiers in Medical Technology, 4, 1007708. https://doi.org/10.3389/fmedt.2022.1007708
  30. Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sanchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60-88. https://doi.org/10.1016/j.media.2017.07.005
  31. Liu, X., Faes, L., Kale, A. U., Wagner, S. K., Fu, D. J., Bruynseels, A., Mahendiran, T., Moraes, G., Shamdas, M., Kern, C., Ledsam, J. R., Schmid, M. K., Balaskas, K., Topol, E. J., Bachmann, L. M., Keane, P. A., & Denniston, A. K. (2019). A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: A systematic review and meta-analysis. The Lancet Digital Health, 1(6), e271-e297. https://doi.org/10.1016/S2589-7500(19)30123-2
  32. Liu, X., Cruz Rivera, S., Moher, D., Calvert, M. J., Denniston, A. K., & SPIRIT-AI and CONSORT-AI Working Group (2020). Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: The CONSORT-AI extension. Nature Medicine, 26(9), 1364-1374. https://doi.org/10.1038/s41591-020-1034-x
  33. Lundervold, A. S., & Lundervold, A. (2019). An overview of deep learning in medical imaging focusing on MRI. Zeitschrift fur Medizinische Physik, 29(2), 102-127. https://doi.org/10.1016/j.zemedi.2018.11.002
  34. Malamateniou, C., O’Regan, T., McFadden, S., & Jackson, S. (2024). Artificial intelligence in radiography: Practice, research and education. Radiography, 30(Suppl. 2), S56-S59. https://doi.org/10.1016/j.radi.2024.09.062
  35. McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G. S., Darzi, A., Etemadi, M., Garcia-Vicente, F., Gilbert, F. J., Halling-Brown, M., Hassabis, D., Jansen, S., Karthikesalingam, A., Kelly, C. J., King, D., & Shetty, S. (2020). International evaluation of an AI system for breast cancer screening. Nature, 577(7788), 89-94. https://doi.org/10.1038/s41586-019-1799-6
  36. Moons, K. G. M., Wolff, R. F., Riley, R. D., Whiting, P. F., Westwood, M., Collins, G. S., Reitsma, J. B., Kleijnen, J., & Mallett, S. (2019). PROBAST: A tool to assess risk of bias and applicability of prediction model studies. Annals of Internal Medicine, 170(1), 51-58. https://doi.org/10.7326/M18-1376
  37. Mongan, J., Moy, L., & Kahn, C. E. (2020). Checklist for artificial intelligence in medical imaging (CLAIM): A guide for authors and reviewers. Radiology: Artificial Intelligence, 2(2), e200029. https://doi.org/10.1148/ryai.2020200029
  38. Muehlematter, U. J., Daniore, P., & Vokinger, K. N. (2021). Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe. The Lancet Digital Health, 3(3), e195-e203. https://doi.org/10.1016/S2589-7500(20)30292-2
  39. Nagendran, M., Chen, Y., Lovejoy, C. A., Gordon, A. C., Komorowski, M., Harvey, H., Topol, E. J., Ioannidis, J. P. A., Collins, G. S., & Maruthappu, M. (2020). Artificial intelligence versus clinicians: Systematic review of design, reporting standards, and claims of deep learning studies. BMJ, 368, m689. https://doi.org/10.1136/bmj.m689
  40. Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. https://doi.org/10.1126/science.aax2342
  41. Panch, T., Mattie, H., & Celi, L. A. (2019). The “inconvenient truth” about AI in healthcare. npj Digital Medicine, 2, 77. https://doi.org/10.1038/s41746-019-0155-4
  42. Park, S. H. (2025). Human-AI interaction in radiology: Balancing automation, trust, and clinical judgment. Korean Journal of Radiology, 26(2), 120-126. https://doi.org/10.3348/kjr.2025.0071
  43. Park, S. H., & Han, K. (2018). Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction. Radiology, 286(3), 800-809. https://doi.org/10.1148/radiol.2017171920
  44. Pesapane, F., Codari, M., & Sardanelli, F. (2018). Artificial intelligence in medical imaging: Threat or opportunity? Radiologists again at the forefront of innovation in medicine. Insights into Imaging, 9(5), 745-753. https://doi.org/10.1007/s13244-018-0647-0
  45. Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022). AI in health and medicine. Nature Medicine, 28(1), 31-38. https://doi.org/10.1038/s41591-021-01614-0
  46. Ricci Lara, M. A., Echeveste, R., & Ferrante, E. (2022). Addressing fairness in artificial intelligence for medical imaging. Nature Communications, 13, 4581. https://doi.org/10.1038/s41467-022-32186-3
  47. Sahiner, B., Chen, W., Pezeshk, A., & Petrick, N. (2023). Data drift in medical machine learning: Implications and potential remedies. The British Journal of Radiology, 96(1150), 20220878. https://doi.org/10.1259/bjr.20220878
  48. Seyyed-Kalantari, L., Zhang, H., McDermott, M. B. A., Chen, I. Y., & Ghassemi, M. (2021). Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in underserved patient populations. Nature Medicine, 27(12), 2176-2182. https://doi.org/10.1038/s41591-021-01595-0
  49. Shen, D., Wu, G., & Suk, H. I. (2017). Deep learning in medical image analysis. Annual Review of Biomedical Engineering, 19, 221-248. https://doi.org/10.1146/annurev-bioeng-071516-044442
  50. Shiang, T., Sarwar, A., Halabi, S. S., & others (2022). Implementation of an artificial intelligence decision support system in radiology residency training. Clinical Imaging, 92, 32-37. https://doi.org/10.1016/j.clinimag.2022.09.003
  51. Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6, 60. https://doi.org/10.1186/s40537-019-0197-0
  52. Sounderajah, V., Ashrafian, H., Rose, S., Golub, R. M., Shetty, S., De Fauw, J., Karthikesalingam, A., Liu, X., Denniston, A. K., & others (2025). STARD-AI: An extension of the STARD 2015 reporting guideline for diagnostic accuracy studies involving artificial intelligence. Nature Medicine, 31, 1-8. https://doi.org/10.1038/s41591-025-03953-8
  53. Steyerberg, E. W., & Harrell, F. E. (2016). Prediction models need appropriate internal, internal-external, and external validation. Journal of Clinical Epidemiology, 69, 245-247. https://doi.org/10.1016/j.jclinepi.2015.04.005
  54. Tejani, A. S., Moy, L., Kahn, C. E., & others (2024). Checklist for artificial intelligence in medical imaging (CLAIM): 2024 update. Radiology: Artificial Intelligence, 6(4), e240300. https://doi.org/10.1148/ryai.240300
  55. Tejani, A. S., Yi, P. H., Marin, D., & others (2024). Integrating and adopting artificial intelligence in the radiology workflow: A primer for standards and Integrating the Healthcare Enterprise profiles. Radiology, 311(3), e232653. https://doi.org/10.1148/radiol.232653
  56. Thrall, J. H., Li, X., Li, Q., Cruz, C., Do, S., Dreyer, K., & Brink, J. (2018). Artificial intelligence and machine learning in radiology: Opportunities, challenges, pitfalls, and criteria for success. Journal of the American College of Radiology, 15(3), 504-508. https://doi.org/10.1016/j.jacr.2017.12.026
  57. Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56. https://doi.org/10.1038/s41591-018-0300-7
  58. Tschandl, P., Rinner, C., Apalla, Z., Argenziano, G., Codella, N., Halpern, A., Janda, M., Lallas, A., Longo, C., Malvehy, J., Paoli, J., Puig, S., Rosendahl, C., Soyer, H. P., Sinz, C., & Kittler, H. (2020). Human-computer collaboration for skin cancer recognition. Nature Medicine, 26(8), 1229-1234. https://doi.org/10.1038/s41591-020-0942-0
  59. van Kooten, M. J., de Vos, B. D., van Leeuwen, K. G., van Ginneken, B., & others (2024). A framework to integrate artificial intelligence training into radiology residency programs. Insights into Imaging, 15, 15. https://doi.org/10.1186/s13244-023-01595-3
  60. van Leeuwen, K. G., Schalekamp, S., Rutten, M. J. C. M., van Ginneken, B., & de Rooij, M. (2021). Artificial intelligence in radiology: 100 commercially available products and their scientific evidence. European Radiology, 31(6), 3797-3804. https://doi.org/10.1007/s00330-021-07892-z
  61. Vasey, B., Nagendran, M., Campbell, B., Clifton, D. A., Collins, G. S., Denaxas, S., Denniston, A. K., Faes, L., Geerts, B. F., Ibrahim, M., Liu, X., Mateen, B. A., Mathur, P., McCradden, M. D., Morgan, L., Ordish, J., Rogers, C., Saria, S., Ting, D. S. W., & McCulloch, P. (2022). Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. Nature Medicine, 28(5), 924-933. https://doi.org/10.1038/s41591-022-01772-9
  62. Vayena, E., Blasimme, A., & Cohen, I. G. (2018). Machine learning in medicine: Addressing ethical challenges. PLOS Medicine, 15(11), e1002689. https://doi.org/10.1371/journal.pmed.1002689
  63. Wiens, J., Saria, S., Sendak, M., Ghassemi, M., Liu, V. X., Doshi-Velez, F., Jung, K., Heller, K., Kale, D., Saeed, M., Ossorio, P. N., Thadaney-Israni, S., & Goldenberg, A. (2019). Do no harm: A roadmap for responsible machine learning for health care. Nature Medicine, 25(9), 1337-1340. https://doi.org/10.1038/s41591-019-0548-6
  64. Willemink, M. J., Koszek, W. A., Hardell, C., Wu, J., Fleischmann, D., Harvey, H., Folio, L. R., Summers, R. M., Rubin, D. L., & Lungren, M. P. (2020). Preparing medical imaging data for machine learning. Radiology, 295(1), 4-15. https://doi.org/10.1148/radiol.2020192224
  65. Wynants, L., Van Calster, B., Collins, G. S., Riley, R. D., Heinze, G., Schuit, E., Bonten, M. M. J., Damen, J. A. A., Debray, T. P. A., De Vos, M., Dhiman, P., Haller, M. C., Harhay, M. O., Henckaerts, L., Heus, P., Kreuzberger, N., Lohmann, A., Luijken, K., Ma, J., & van Smeden, M. (2020). Prediction models for diagnosis and prognosis of covid-19: Systematic review and critical appraisal. BMJ, 369, m1328. https://doi.org/10.1136/bmj.m1328
  66. Yu, A. C., Eng, J., & Poon, A. D. (2022). External validation of deep learning algorithms for radiologic diagnosis: A systematic review. Radiology: Artificial Intelligence, 4(3), e210064. https://doi.org/10.1148/ryai.210064
  67. Zech, J. R., Badgeley, M. A., Liu, M., Costa, A. B., Titano, J. J., & Oermann, E. K. (2018). Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study. PLOS Medicine, 15(11), e1002683. https://doi.org/10.1371/journal.pmed.1002683

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