AI Will Change Radiology, but It Won’t Replace Radiologists

Artificial Intelligence (AI) is a simulation of human intelligence carried out by machines or computer systems. As technology continues to advance, AI is becoming increasingly incorporated into different fields, one of the most notable being medicine- specifically radiology (the process of using radiation, such as x-rays, and imaging technologies in order to diagnose a patient). However, the practicality and value these AI systems offer in a field like radiology further call into question the permanence of human radiologists and whether their jobs will someday be taken over by AI.

Firstly, it is important to note that there would be significant benefits from introducing AI systems into the field of radiology, as deep learning (a method of AI that teaches computers to process data in a way that is inspired by human brains) networks can be programmed to specifically identify pathologies (diseases) in radiological images such as bone fractures and lesions (potentially cancerous growths). Furthermore, these systems read and interpret data at a high rate, which improves the overall efficiency of the process and is a necessary tool when considering the volume of images that must be analysed and processed per patient. Moreover, the inflated costs of conducting the imaging and radiology processes could be somewhat averted through the use of AI systems, as the labour fees would decrease significantly. However, while this could be more reliable than the average radiologist, the best systems are currently only on par with human performance and are only used in research settings, making it unlikely that these models will replace human radiologists at any time in the near future.

Currently, AI systems are trained to handle the image recognition and research aspects of the radiology process. However, while these algorithms are well programmed to read and interpret data from the images, they are currently capable of little else and cannot be trusted with more important or challenging tasks. Furthermore, given the varied aspects of a radiologist’s job, and the limited capabilities of machine learning systems, the role of a human professional is vital and something that cannot be replaced by AI at this stage. Even so, if a computer system were to be used instead, there are additional elements of a radiologist’s job, such as consultations with other physicians on diagnoses and treatment plans, and defining the technical parameters of imaging examinations, that cannot be carried fulfilled by AI, and would be maintained as the roles of a human expert. This means that the AI systems would, at best, only be capable of better enabling or facilitating tasks performed by radiologists.

Moreover, deep learning algorithms (models of AI with greater complexity and depth) for image recognition must be trained on labelled data (existing images from patients that have already received a definitive diagnosis, for eg. a cancerous tumour confirmed or a definitive fracture identified). This allows the system to correlate commonalities between the images, and essentially “learn” how to identify the presence of a disease or abnormality. However, for this process to generate accurate results, the machine must be trained on millions of labelled images, all of which are owned and stored securely by physicians, hospitals and imaging facilities. This makes it a challenging and time-consuming process to attain a sufficient amount of pictures, hence compromising the reliability and accuracy of the results. Hence, AI systems cannot entirely replace current radiologists, as the requirements for the algorithm to even function are hard to meet.

Additionally, if AI systems were to be used, changes in medical regulations and health insurance for automated image analysis would become vital. Moreover, if a machine were to misdiagnose a patient, it would make it exceedingly hard to determine the responsible party- would it be the physician, the hospital, the imaging technology vendor, or the data scientist who created the algorithm? Thus, AI radiology machines will not suffice if they are merely at par with human performance, but will instead need to become substantially better in order to be safely implemented and drive the level of change required.

Hence, while it would be a lie to say that AI systems succeeding radiologists is outside the realm of possibility, the chances of human doctors being completely replaced are very low. Thus, in conclusion, when AI becomes more widespread in its integration into radiology, most radiologists will find change to, rather than replacement of, their current jobs.

Works Cited

Davenport, Thomas, and Keith Dreyer. “AI Will Change Radiology, but It Won’t Replace Radiologists.” Harvard Business Review, 27 Mar. 2018, hbr.org/2018/03/ai-will-change-radiology-but-it-wont-replace-radiologists.

dreaguero. “Will AI Replace Radiologists?” Intelerad, 13 May 2022, www.intelerad.com/en/2022/05/13/will-ai-replace-radiologists/#:~:text=AI%20cannot%20replace%20radiologists.

“Three Reasons AI Is Not Ready to Replace Radiologists.” Omnia Health Insights, 12 Apr. 2023, insights.omnia-health.com/clinical/three-reasons-ai-not-ready-replace-radiologists. Accessed 31 May 2023.