Why Isn't There More High-fidelity Simulation Training in Diagnostic Radiology? Results of a Survey of Academic Radiologists

Training the New Radiologists: Approaches for Education

The field of Radiology is continually changing, requiring corresponding evolution in both medical student and resident training to adequately prepare the next generation of radiologists. With advancements in adult education theory and a deeper understanding of perception in imaging interpretation, expert educators are reshaping the training landscape by introducing innovative teaching methods to align with increased workload demands and emerging technologies. These include the use of peer and interdisciplinary teaching, gamification, case repositories, flipped-classroom models, social media, and drawing and comics. This publication aims to investigate these novel approaches and offer persuasive evidence supporting their incorporation into the updated Radiology curriculum.

Ultrasound Simulation Training for Radiology Residents-Curriculum Design and Implementation

Medical simulation training can be used to improve clinician performance, teach communication and professionalism skills, and enhance team training. Radiology residents can benefit from simulation training in diagnostic ultrasound, procedural ultrasound, and communication skills prior to direct patient care experiences. This paper details a weeklong ultrasound simulation training curriculum for radiology residents during the PGY-1 clinical internship. The organization of established teaching methods into a dedicated course early in radiology residency training with the benefit of a multi-disciplinary approach makes this method unique. This framework can be adapted to fit learners at different skill levels or with specific procedural needs.

Improving the Relationship Between Confidence and Competence: Implications for Diagnostic Radiology Training From the Psychology and Medical Literature

The focus of diagnostic radiology training is on creating competent professionals, whereas confidence and its calibration receive less attention. Appropriate confidence is critical for patient care both during and after training. Overconfidence can adversely affect patient care and underconfidence can create excessive costs. We reviewed the psychology and medical literature pertaining to confidence and competence to collect insights and best practices from the psychology and medical literature on confidence and apply them to radiology training. People are rarely accurate in assessments of their own competence. Among physicians, the correlation between perceived abilities and external assessments of those abilities is weak. Overconfidence is more prevalent than underconfidence, particularly at lower levels of competence. On the individual level, confidence can be calibrated to a more appropriate level through efforts to increase competence, including sub-specialization, and by gaining a better understanding of metacognitive processes. With feedback, high-fidelity simulation has the potential to improve both competence and metacognition. On the system level, systems that facilitate access to follow-up imaging, pathology, and clinical outcomes can help close the gap between perceived and actual performance. Appropriate matching of trainee confidence and competence should be a goal of radiology residency and fellowship training to help mitigate the adverse effects of both overconfidence and underconfidence during training and independent practice.

Artificial intelligence-based education assists medical students' interpretation of hip fracture

Background

With recent transformations in medical education, the integration of technology to improve medical students’ abilities has become feasible. Artificial intelligence (AI) has impacted several aspects of healthcare. However, few studies have focused on medical education. We performed an AI-assisted education study and confirmed that AI can accelerate trainees’ medical image learning.

Materials

We developed an AI-based medical image learning system to highlight hip fracture on a plain pelvic film. Thirty medical students were divided into a conventional (CL) group and an AI-assisted learning (AIL) group. In the CL group, the participants received a prelearning test and a postlearning test. In the AIL group, the participants received another test with AI-assisted education before the postlearning test. Then, we analyzed changes in diagnostic accuracy.

Results

The prelearning performance was comparable in both groups. In the CL group, postlearning accuracy (78.66 ± 14.53) was higher than prelearning accuracy (75.86 ± 11.36) with no significant difference (p = .264). The AIL group showed remarkable improvement. The WithAI score (88.87 ± 5.51) was significantly higher than the prelearning score (75.73 ± 10.58, p < 0.01). Moreover, the postlearning score (84.93 ± 14.53) was better than the prelearning score (p < 0.01). The increase in accuracy was significantly higher in the AIL group than in the CL group.

Conclusion

The study demonstrated the viability of AI for augmenting medical education. Integrating AI into medical education requires dynamic collaboration from research, clinical, and educational perspectives.

Full Resolution Simulation for Evaluation of Critical Care Imaging Interpretation; Part 2: Random Effects Reveal the Interplay Between Case Difficulty, Resident Competence, and the Training Environment

RATIONALE AND OBJECTIVES

To further characterize empirical data from a full-resolution simulation of critical care imaging coupled with post hoc grading of resident's interpretations by senior radiologists. To present results from estimating the random effects terms in a comprehensive mixed (hierarchical) regression model.

MATERIALS AND METHODS

After accounting for 9 fixed effects detailed in Part 1 of this paper, we estimated normally distributed random effects, expressed in terms of score offsets for each case, resident, program, and grader.

RESULTS

The fixed effects alone explained 8.8% of score variation and adding the random effects increased explanatory power of the model to account for 36% of score variation. As quantified by intraclass correlation coefficient (ICC = 28.5%; CI: 25.1-31.6) the majority of score variation is directly attributable to the case at hand. This "case difficulty" measure has reliability of 95%. Individual residents accounted for much of the remaining score variation (ICC = 5.3%; CI: 4.6-5.9) after adjusting for all other effects including level of training. The reliability of this "resident competence" measure is 82%. Residency training program influence on scores was small (ICC = 1.1%; CI: 0.42-1.7). Although a few significantly high and low ones can be identified, reliability of 73% militates for caution. At the same time, low intraprogram variation is very encouraging. Variation attributable to differences between graders was minimal (ICC = 0.58%; CI: 0.0-1.2) which reassures us that the method of scoring is reliable, consistent, and likely extensible.

CONCLUSION

Full resolution simulation based evaluation of critical care radiology interpretation is being conducted remotely and efficiently at large scale. A comprehensive mixed model of the resulting scores reliably quantifies case difficulty and resident competence.

Artificial intelligence for precision education in radiology

In the era of personalized medicine, the emphasis of health care is shifting from populations to individuals. Artificial intelligence (AI) is capable of learning without explicit instruction and has emerging applications in medicine, particularly radiology. Whereas much attention has focused on teaching radiology trainees about AI, here our goal is to instead focus on how AI might be developed to better teach radiology trainees. While the idea of using AI to improve education is not new, the application of AI to medical and radiological education remains very limited. Based on the current educational foundation, we highlight an AI-integrated framework to augment radiology education and provide use case examples informed by our own institution's practice. The coming age of "AI-augmented radiology" may enable not only "precision medicine" but also what we describe as "precision medical education," where instruction is tailored to individual trainees based on their learning styles and needs.

A Review of Resources and Methodologies Available for Teaching and Assessing Patient-Related Communication Skills in Radiology

ACGME expectations for radiology trainees' proficiencies in communication skills pose a challenge to program directors who wish to develop curricula addressing these competencies. Numerous educational resources and pedagogical approaches have emerged to address such competencies specifically for radiology, but have yet to be systematically catalogued. In this paper, we review and compile these resources into a toolkit that will help residencies develop curricula around patient-centered communication. We describe numerous web-based resources and published models that have incorporated innovative, contemporary pedagogical techniques. In undertaking this compilation, our hope is to kindle discussion about the development of formalized or standardized communication curricula or guides for radiology residencies.