A Study on Acceptance of the Digital Transformation of the Radiology Diagnosis Process Using AI-Enriched Tools: Scientific, Marketing and Management Implications

Dimitrios S. Stamoulis; Chrysanthi Papachristopoulou; Stéphane Bourliataux-Lajoinie

المؤلفون

Dimitrios S. Stamoulis Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Greece
Chrysanthi Papachristopoulou E-medical Products and Services Specialist, Greece
Stéphane Bourliataux-Lajoinie Department Professeur des Universités - Marketing Digital Laboratoire Lirsa / Irgo 75003 Paris, France

الكلمات المفتاحية:

AI-enriched healthcare، Medical technology، Healthcare IT، AI-based health services، AI-augmented radiology

الملخص

As it happens in every major technological breakthrough, acceptance is an issue. Especially when this new technology causes transformation of existing business process, to which all stakeholders have to adapt. This is particularly the case with AI-enriched radiology tools that have started finding their way into the medical practice of several parts of the world. This mixed, qualitative and quantitative, study shows that TAM2 is still relevant to be used a driver of such studies for newer digital technologies and also that it has the power to produce meaning and actionable results. Moreover, this results can be perfectly exploitable from a marketing management perspective, since they provide the basis for a behavioural segmentation of the respondents along the well-known innovation adoption curve, enabling significant positioning and product launching decisions.

التنزيلات

تنزيل البيانات ليس متاحًا بعد.

المراجع

Creswell, J. W., & Clark, V. L. P. (2017). Designing and conducting mixed methods research. Sage publications.

Davis FD. (1989a) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly. 1989;13:319–340.

Davis FD. (1989b) Bagozzi RP, Warshaw PR. User acceptance of computer technology: A comparison of 2 theoretical models. Manage Sci. 1989;35:982–1003.

Driver CN, Bowles BS, Bartholmai BJ, Greenberg-Worisek AJ. Artificial Intelligence in Radiology: A Call for Thoughtful Application. Clin Transl Sci. 2020 Mar;13(2):216-218. doi: 10.1111/cts.12704. Epub 2019 Oct 30.

ESR (2018) Beyond Imaging: the paradox of Artificial Intelligence www.myesr.org/article/1934

Jiang, F., Jiang, Y., Zhi, H., et al. (2017). Artificial intelligence in healthcare: past, present, and future. Stroke and Vascular Neurology, 2(4).

Jungmann, F., Jorg, T., Hahn, F., Dos Santos, D. P., Jungmann, S. M., Düber, C., ... & Kloeckner, R. (2021). Attitudes toward artificial intelligence among radiologists, IT specialists, and industry. Academic radiology, 28(6), 834-840.

Kulkarni S, Seneviratne N, Baig MS, Khan AHA. (2020). Artificial Intelligence in Medicine: Where Are We Now? Acad Radiol. 2020 Jan;27(1):62-70. doi: 10.1016/j.acra.2019.10.001.

Lakhani P, Prater AB, Hutson RK, et al. (2017) Machine learning in radiology: applications beyond image interpretation. J Am Coll Radiol. 2017:1–10

Langlotz, C. P., et al. (2019). A roadmap for foundational research on artificial intelligence in medical imaging. Radiology, 291(3), 781-791

Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future—big data, machine learning, and clinical medicine. The New England journal of medicine, 375(13), 1216.

Pakdemirli E. Artificial intelligence in radiology: friend or foe? Where we now and where are are we heading? Acta Radiol Open. 2019 Feb 21;8(2):2058460119830222. doi: 10.1177/2058460119830222.

Parikh, R. B., Obermeyer, Z., & Navathe, A. S. (2019). Regulation of predictive analytics in medicine. Science, 363(6429), 810-812.

Recht, M., & Bryan, R. N. (2017). Artificial intelligence: threat or boon to radiologists?. Journal of the American College of Radiology, 14(11), 1476-1480.

Reddy, S., Fox, J., & Purohit, M. P. (2019). Artificial intelligence-enabled healthcare delivery. Journal of the Royal Society of Medicine, 112(1), 22-28.

Rogers, Everett M. (1983). Diffusion of innovations (3rd ed.). New York: Free Press of Glencoe. ISBN 9780029266502.

Shen J, Zhang CJP, Jiang B, Chen J, Song J, Liu Z, He Z, Wong SY, Fang PH, Ming WK, "Artificial Intelligence Versus Clinicians in Disease Diagnosis: Systematic Review", JMIR Med Inform 2019;7(3).doi: 10.2196/10010

Thrall JH, 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. J Am Coll Radiol. 2018 Mar;15(3 Pt B):504-508. doi: 10.1016/j.jacr.2017.12.026. Epub 2018 Feb 4. PMID: 29402533.

Tonekaboni, S., et al. (2019). What clinicians want: contextualizing explainable machine learning for clinical end use. Proceedings of the Machine Learning for Healthcare Conference, 359-380.

Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56. Available: doi:10.1038/s41591-018-0300-7.

Van Raaij E. M., Schepers J. J. (2008). The acceptance and use of a virtual learning environment in china. Computers & Education, 50(3), 838–852.

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. doi:10.1287/mnsc.46.2.186.11926

Venkatesh, V., Brown, S. A., & Bala, H. (2013). Bridging the qualitative-quantitative divide: Guidelines for conducting mixed methods research in information systems. MIS quarterly, 21-54.

Vyborny C.J. (1997) “Image quality and the clinical radiographic examination,” Radiographics, vol. 17, no. 2, pp. 479–498, 1997.

Wang X, Peng Y, Lu L, et al. ChestX-ray. (2017). hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. Available from: http://arxiv.org/abs/1705.02315

Yamada, Y., & Kobayashi, M. (2018). Detecting mental fatigue from eye-tracking data gathered while watching video: Evaluation in younger and older adults. Artificial intelligence in medicine, 91, 39-48.