Training Diagnostic Radiology specialists: The disconnect between the Spanish MIR curriculum and the demands of daily practice

Diagnostic radiology is a constantly evolving speciality. Since being recognised in 1984 as a distinct specialty in MIR [the acronym for specialised medical training in Spain],¹ radiologists have redefined their role, adapting to technological advances and modifying their daily responsibilities. In general terms, radiologists are expected to: be experts in imaging diagnostics, keep abreast of the latest technologies, perform a consultative role in multidisciplinary committees, be competent in the interventions within their specific area, and, ideally, be a leader in research. Trainees should acquire all these skills during their specialty training, but the path is strewn with obstacles.

Some of the challenges are structural ones, often beyond the direct control of ordinary radiologists, such as the lack of workstations, insufficient reporting rooms or antiquated Picture Archiving and Communication Systems (PACS). These shortcomings are the first barrier to effective teaching.

Remote working may improve quality of life, but it poses a new question: how to ensure proper supervision and comprehensive training when part of the job is performed from another location? Work performed remotely is work that the specialty trainee does not see. A balanced combination of in-person and remote work allows for proper training, but if it becomes common practice without a clear training framework, it poses a potential risk in a specialty that relies on real-time feedback and error correction.

Another growing problem is excessive caseload. The volume and complexity of studies have increased by up to 80% in recent years, with no proportional increase in staff.² This pressure drastically reduces the time available for teaching, seriously compromising the quality of training in MIR. Overload not only encroaches on training, but also increases the risk of trainer burnout, diagnostic errors and professional isolation, leading them to consider teaching the least of their priorities.³

Despite these difficulties, we train specialty trainees with the utmost effort, following an official training programme⁴ that includes sessions, conferences, continuous professional development, research activity, rotations across all areas and on-call duties. However, given the breadth of the specialty, and the divergence between “pure diagnostics” and interventional practice, many specialty trainees can feel overwhelmed. This is also a challenge for teaching, where time is against us.

Faced with this sense of being swamped, some specialty trainees prefer to subspecialise early.⁵ Others, in turn, prefer to put the decision off as long as possible, generally for fear of choosing too soon. What is the best training model? Should we facilitate early subspecialisation or should generalist training be prioritised throughout their specialty programme? Would it be beneficial to define core competences and establish specific elective paths from the third year, in the middle of the specialty training? Could one more year of specialty training be the solution? Would it help to establish a fellowship programme at the end of the specialty training to consolidate knowledge and skills?

Interest in research is still a weak point in radiology training. A survey by the European Society for Radiology in 2021⁶ revealed that nearly half (47.8%) of trainee radiologists had never published an indexed manuscript. Of those who had, the majority (81.13%) did so in their own time and with no statistical training or guidance from anyone from the educational unit about how to write a research paper. This lack of interest should come as no surprise then, if research must be conducted outside working hours, for free, and often with no one to turn to for guidance. This means that in most cases research depends on individual desires and motivations, as if it were a hobby. But if this is how we see it, what sense does it make to include it in the specialty training curriculum?

Artificial intelligence (AI), loved and feared in equal measure, emerges as a possible means to free up time. Once implemented, it could perform routine tasks such as protocoling and drafting preliminary reports, segmenting or measuring lesions, which would theoretically leave us with more time for teaching and research. Furthermore, AI can itself be a training tool, adapting to the individual needs of the specialty trainee for “personalised precision education”.⁷ However, this transformation will require a significant investment of money and time. And if we should be offering progressive training to acquire these emerging competences to anyone, it is precisely to trainees, as it is a tool that they will certainly have to use, whether they like it or not.

This complex reality—characterised by high caseloads, training that is sometimes perceived as lacking/insufficient due to the heavy demands on qualified radiologists, the challenge posed by the breadth of the discipline, and the uncertainty generated by the rise of AI—has a direct impact on the appeal of diagnostic radiology to the next generation. The future of our speciality will fall to the professionals we are training now; and, in a context where there is practically no unemployment in medicine,⁸ it will depend on our ability to attract talent from universities to our speciality training programmes. Our capacity to do this will depend greatly on how we structure our training, the quality of teaching we offer and our viewing it as a priority (in terms of time and resources), rather than just another task on our list.

CRediT authorship contribution statement

Both authors have contributed intellectually to the work. David Castanedo Vázquez wrote the draft outline; we both revised the draft, expanded the bibliography and approved the final version.

Ethical responsibilities

The authors believe there is no ethical responsibility to declare for this paper.