Radiologist Training and the Evolution of AI: Where are we now

There are several trends influencing the radiology profession at the moment, some brought on by radical change to the industry over the past year, others evolving in response to emergent technologies and solutions. These trends are shifting radiologist training requirements and how radiologists engage with their careers and the industry as a whole.

In a study undertaken by the Journal of American College of Radiology, trends around radiologist-practice separation were unpacked and examined. The workforce has become more mobile, more agile, with more and more radiologists opting into new practices, opportunities and career modalities. The study found that 47% of radiologists were from multi-specialty groups, and the most common subspecialists were breast images (43%) and cardiothoracic images (34%). What does this mean? While the data cannot create a full picture of the reasons why this is happening, the reality is that an increasingly mobile radiology workforce is likely to be more inclined to move towards practices and opportunities that inspire them, and keep them engaged.

Technology is playing no small role in the changing approaches of radiologists, and in radiologists training. From the innovations that are capable of leveraging artificial intelligence (AI) and 3D ultrasound images to deliver extraordinary images, to the solutions that transform the mundane into the useful – technology is changing how radiologists work, influencing where they choose to work, and affecting what skills development programs they engage in.

AI as an integral part of radiology training

At a recent conference hosted by the Radiological Society of North America (RSNA), medical professionals looked towards the future and how technology would potentially shape the industry and the people that worked within it. Osmanuddin Ahmed, M.D, explored the use and potential of hybrid CT-flouroscopy in interventional radiology; and Narayan Viswanadhan, M.D., looked at the rapid evolution of AI solutions within the sector. For Viswanandhan, AI was increasingly valuable as a tool to detect issues and bring them to a busy radiologists attention, and it was showing immense promise across multiple fields such as brain hemorrhage and stroke detection.

AI has rapidly risen in both prominence and popularity over the past few years and has moved from a technology eyed with suspicion to a handy support structure for the incredibly busy professional. AI has the ability to step in as a fresh, never-tired, pair of eyes and can help radiologists juggle and manage their workloads far more effectively than in the past. With AI at the helm, radiologists can prioritize cases at a pace that’s to the patient’s benefit, and translate workloads into manageable efficiencies.

However, while leading AI systems are designed to be integrated as seamlessly as possible into existing PACS and RIS platforms with a minimal barrier to entry, there is a need for more radiologists training opportunities in AI. According to this piece in Springer published this year, there are numerous AI radiologists training programs but the majority are short and focused and don’t form part of a long-term learning process. The analysis concluded that there should be more AI-focused radiologists training programs that go in-depth into the technology, how to leverage the technology more effectively, and how to fully realize the potential and value of an AI system.

radiologists looking at scans

Already there are numerous examples of how AI is changing medical and radiology training. In a recent interview with Aidoc, Dr. Yosef Chodakiewitz, a resident at Cedars-Sinai in LA spoke of how he has leveraged AI within his career, and how it has changed his approaches and practice. He believes that AI provides a tangible benefit to radiology residents to provide immediate and high-quality patient care. It offers a safety net that can potentially support trainees in their development and growth. However, he does clarify that this safety net could minimize growth and experience if residents rely on AI too much. This would likely be easily managed, especially if radiology training emphasized the risks and outlined the benefits of using AI correctly, for maximum value.

Dr. Komal Chughtai also discussed the evolution of radiologist training with the added benefit of AI. “Radiology is one profession where AI will be implemented in our workflows, whether head CTs or PE studies or nodule research. To some extent, AI and computer-assisted diagnosis are the future of radiology so it is important for people to come into contact with AI and understand how it works.” She also shared the importance of not being afraid to work with AI, and how exposure to AI as a radiology resident can be beneficial for the future.

Looking towards the future

Regardless of what may lie ahead for the industry in terms of technology evolution and revolution, AI is destined to remain a very large part of that future. However, it is equally important to ensure that AI systems can be fully leveraged by radiologists and medical institutions in order to ensure that they are worth the time and expense. All AI platforms are not created equal which makes implementation a careful and considered strategy that should take existing skillsets and radiology training programs into account.

AI needs to be capable of helping the radiologist make faster decisions and pull out critical information at speed. You want an AI that offers a significant breadth and depth of pathologies and modalities, that has its own clearly defined roadmap, and that has a future-proof strategy. This will directly influence its relevance. Radiologists have to be given the opportunity to achieve the same benchmarks. They require radiologists training programs centered around AI so they have the tools they need to thrive in a busy, challenging and complex landscape.

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