Current problems in diagnostic radiology最新文献

The magic number, or number of ranks needed to achieve a greater than 90 % chance of matching, has not been investigated for diagnostic radiology (DR). Somewhat reflective of a field's changing competitiveness, this individual metric can be useful for reassuring applicants or identifying a need to reach out to mentors. The NRMP's Charting Outcomes in the Match was accessed over the previous 10 cycles to assess changes to magic number and other match-related metrics. Over the last 10 cycles, there has been an increase in magic number for prospective radiologists. Based on the most 2022 recent report, the magic number was 14 compared to 5 and 2 in 2014 and 2016 respectively. Compared to the average US MD senior, those applying into DR were significantly more likely to match in 2014, 2016 and 2020 (p < 0.01 for all), and significantly less likely to match in 2018 and 2022 (p = 0.03 and p < 0.01, respectively). This trend has had important consequences for applicants and programs as the incentive to apply more widely grows. The increasing magic number demonstrates increasing competitiveness in the field, which might be due to a positive job market, changing medical student preferences, or increased access to radiology electives and mentors. The 2024 Charting Outcomes document will be the first to include data from a class almost entirely affected by the change to a pass/fail Step1 and the new preference signaling supplement. It is currently unclear how either change will affect the overall competitiveness of the field and the magic number.

Problem description: Musculoskeletal (MSK) anatomy and pathology from a radiology perspective can be difficult to conceptualize and understand due to the challenge of visualizing 3D structures in stacks of 2D imaging. Consequently, trainees may benefit from inexpensive methods that can help trainees better visualize MSK anatomy and pathology. The purpose of this study is to provide proof of concept for inexpensive methodology to help learners such as radiology residents quickly and inexpensively understand musculoskeletal anatomy and pathology. This can help trainees become better at applying musculoskeletal knowledge to clinical practice.

Institutional methodology: Soft-modeling compounds such as Play-Doh® was utilized in a variety of colors with pottery tools to recreate 3D models of challenging MSK anatomy and pathology for trainees. Qualitative feedback from the residents was collected.

Results: Eighteen different pathological conditions across six major bone structures were modeled with a soft modeling compound. Residents qualitatively identified the experience as educational in terms of helping them better understand MSK pathology and positive in terms of making learning fun, less stressful, and memorable due to uniqueness of the learning modality. Residents report challenges modeling complex anatomical features and pathology via this methodology.

Conclusion: Radiology residents and other learners can enhance their knowledge of musculoskeletal anatomy and pathology via utilization of inexpensive soft modeling compounds. This may offer a cheaper and more time sensitive alternative to current 3-dimensional hardware and software technologies being developed for educational purposes. Additional work needs to be done to examine the utility of this methodology across larger and diverse groups of learners.

Radiology has usually been the field of medicine that has been at the forefront of technological advances, often being the first to wholeheartedly embrace them. Whether it's from digitization to cloud side architecture, radiology has led the way for adopting the latest advances. With the advent of large language models (LLMs), especially with the unprecedented explosion of freely available ChatGPT, time is ripe for radiology and radiologists to find novel ways to use the technology to improve their workflow. Towards this, we believe these LLMs have a key role in the radiology reading room not only to expedite processes, simplify mundane and archaic tasks, but also to increase the radiologist's and radiologist trainee's knowledge base at a far faster pace. In this article, we discuss some of the ways we believe ChatGPT, and the likes can be harnessed in the reading room.