**3.4 Future implications for job security**

AI has the potential to construct intelligent applications that can mimic the cognitive capabilities of humans, potentially revolutionizing the workforce in myriad ways. Some experts believe that AI could eventually replace certain tasks currently performed by radiologists and technologists [17] such as the interpretation of medical images. However, it is also possible that AI could augment radiologists' abilities and productivity, allowing them to spend more time on higher-level tasks such as consulting with other physicians, analyzing more complex cases, and providing follow-up to patients [19]. Though there are more than 80 approved algorithms in the US and Europe, only 40 of these have been approved by the FDA and only 34% of those were used for interpretation. The number of radiologists working in the US has risen by 7% in five years from 2015 to 2019 [20, 21]**.**

Regarding job security, it is likely that radiologists and technologists who are trained and experienced in using AI systems will be in high demand. However, those who are not able or refuse to adapt to this new technology may face challenges in their job market [7, 10, 17]. Administrative staff may also be impacted as AI can automate some of the tasks they do, but this may also be a positive change as it can lead to more time for the staff to focus on patient care and other important tasks.

Overall, the effects of AI on the radiology workforce will depend on how the technology is adopted and implemented. Radiologists, standing at the leading edge of digital medicine, can provide support in the incorporation of AI into healthcare, their role in diagnostics communication, the incorporation of patient values and preferences, medical judgment, quality assurance, education, policy-making, and interventional procedures and ensures that they cannot be replaced by AI [22]. However, it is important for all radiologists, technologists, and administrative staff to stay informed about the latest developments in AI and work to develop the necessary skills to remain competitive in the field [23]**.**

### **3.5 Future resident education**

The increasing presence of AI applications in radiology necessitates educators to prepare trainees and radiologists as proficient users and stewards of AI technology. Yet, despite the controversy around if and to what extent AI should be incorporated into radiology residency programs, organized AI education and AI-ML curricula are still limited to a few institutions, with formal training opportunities lacking across the board.

AI has the potential to revolutionize radiology resident education by providing new tools and resources for teaching and learning and is likely to include a greater emphasis on AI-ML curricula and precision medical education [24, 25]. By incorporating AI and machine learning into radiology resident education, they can stay up to date with the latest techniques and technologies used to diagnose and treat patients and gain valuable experience with AI as it becomes increasingly important in healthcare.

One potential application of AI in radiology resident education is the use of AI-assisted image interpretation, which could help residents to develop their diagnostic skills and improve their understanding of complex medical images [25]. For example, AI systems can be used to identify and highlight certain features on an image such as tumors or blood vessels, which can help residents to identify these structures and improve their diagnostic accuracy more easily.

Another potential application of AI in radiology resident education is the use of virtual reality and simulation to provide hands-on training experiences [26, 27]. This technology can be used to create realistic simulations of medical scenarios such as a surgical procedure or an interventional radiology procedure, which can provide residents with an immersive and interactive learning experience.

AI can also be used to provide personalized and adaptive learning experiences to analyze each resident's progress and create personalized learning plans [24, 25]. For example, AI-based systems can be used to track residents' progress, provide feedback, and adjust the learning experience based on their strengths and weaknesses. This can include providing tailored feedback and resources to help them improve their diagnostic accuracy, as well as providing opportunities for hands-on training and simulation. By incorporating AI-ML curricula for radiology residents, the residency program can be at the forefront and focus on teaching residents the fundamental concepts and techniques of AI and machine learning such as data pre-processing, coding, model training, theory, and evaluation [28]. This would enable residents to understand how AI systems work and how to use them effectively in their practice. Precision medical education, which is an approach that aims to provide tailored education based on individual needs and characteristics, will also play an important role in and would involve using AI systems to personalize the learning experience for each resident, considering their strengths, weaknesses, and learning style [24, 28].
