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Introduction
New technological advancements in any field are always met with some form of resistance due to precautions and concerns regarding the safety of use. It is especially true in the field of radiology and imaging, which is primarily reliant on technology for its proper functioning. Imaging developments made a massive improvement in the medical sphere because it allows healthcare professionals to provide better care for patients. In addition, such technology enables doctors to conduct more in-depth assessments without severely affecting people. However, technophobia and cyberphobia are still persistent as the main hindrance factors. Therefore, the major problems in advancing new technology for radiology and imaging are artificial intelligence (AI), whole slide imaging, and radiologist workload, where the solutions are AI ethics, implementation in phases, and managerial adjustments, respectively.
Artificial Intelligence
The process of integration of artificial intelligence in radiology is not a new concept, and thus, there are major concerns regarding the overall risks associated with implementation. To properly address the issue, it is critical to define and understand the given industry. Medical radiology is a field of medicine that develops the theory and practice of using radiation for medical purposes. Medical radiology comprises two main scientific disciplines, diagnostic radiology, and therapeutic radiology. Radiation diagnostics is the science of using radiation to study the structure and function of normal and pathologically altered organs and systems of a person to prevent and recognize diseases. Radiation therapy is the science of using ionizing radiation to treat disease. Radiology diagnostics include X-ray diagnostics, radionuclide diagnostics, ultrasound diagnostics, magnetic resonance diagnostics, and medical thermography. In addition, the so-called interventional radiology, which consists of the performance of medical interventions based on radiation diagnostic procedures, is adjacent to it.
However, despite the given uses of radiology technology in medicine, there are certain elements of technophobia. It is especially true about the implementation of artificial intelligence in radiology. It is stated that the main disadvantages of such an approach are substantial costs and major risks due to learning curves (Pandya, 2019). In other words, the danger lies in the fact that AI requires a specific period to be better at diagnostics and therapy than experts. The learning process must be accompanied by trials and repetitions, which will inevitably involve real patients. The only plausible solution for this valid technophobia is to implement strict ethical protocols on AI. All medical experts involved in the process must be able to minimize potential harm and adhere to human values.
Whole Slide Imaging
Another major component of cyberphobia in the sphere of radiology and imaging is whole slide imaging (WSI). It is important to note that there can be significant ramifications if technology is used incorrectly. It is stated that the best course of action in regards to the integration of WSI should revolve around a phase-based approach (Evans et al., 2017). It means that the given technology must be utilized in small incremental steps to ensure that there will be no harm to patients. One should understand that the technologys overall potential in the field of digital clinical pathology is immense. However, the risks can range from minor errors to large misdiagnoses. The domain itself is highly sensitive to mistakes, which makes the issue more critical due to the high stakes involved.
Radiologist Workload
The current trend in technological advancement in cross-sectional imaging shows that there are substantial improvements in both speed and accuracy of the instruments. However, one should note that this massive growth in imaging volumes has not been proportionately increasing with workload adjustments for radiologists. It is stated that average radiologists have to review and interpret a single image every 3-4 seconds under the standard work-day condition of eight hours (McDonald et al., 2015). In other words, both the need and utilization of cross-sectional imaging technology have grown significantly, but there were no improvements in the workload process management for relevant experts. The suggested course of action is to realize that the overall demand for radiology instruments has increased substantially and reduced the workload on radiologists. The main ramification for dismissing the recommended approach is the fact that such pressure can severely elevate the misinterpretation rate, which means that there will be more errors.
Conclusion
In conclusion, radiology and imaging are essential in the medical field because they provide invaluable measures for diagnostics and therapy. However, there is reasonable technophobia in regards to new technology, which possesses valid solutions. Novel advancements in the field cannot be ignored, and their integration is inevitable, and thus, it is critical to carefully assess the process of implementation. Artificial intelligence will be a major element in imaging and radiology, and the only plausible solution is to design strict and strong AI ethics and related protocols. These documents must be specific and protective of patients well-being, and the execution will primarily depend on experts themselves. WSI is also on its path to clinical pathology, which means that only phase-based integration can be applied. The last component of technophobia is manifested in improper managerial approaches regarding workload among radiologists. Cyberphobia will persist if the problem is not eliminated effectively.
References
Evans, A. J., Salama, M. E., Henricks, W. H., & Pantanowitz, L. (2017). Implementation of whole slide imaging for clinical purposes: Issues to consider from the perspective of early adopters. Archives of Pathology & Laboratory Medicine, 141(7), 944-959. Web.
McDonald, R. J., Schwartz, K. M., Eckel, L. J., Diehn, F. E., Hunt, C. H., Bartholmai, B. J., Erickson, B. J., & Kallmes, D. F. (2015). The effects of changes in utilization and technological advancements of cross-sectional imaging on radiologist workload. Academic Radiology, 22(9), 1191-1198. Web.
Pandya, S. K. (2019). Artificial intelligence and up-to-date technology in the clinical neurosciences. Neurology India, 67(3), 949-951. Web.
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