International Medical Education

Background: Artificial intelligence (AI) may support clinical decision-making but introduce automation bias when incorrect recommendations influence users. This study aimed to determine the occurrence of harmful automation bias among undergraduate radiological sciences students during AI-assisted radiotherapy decision-making and to examine the influence of time pressure. Methods: In a controlled experiment, 54 final-year undergraduate radiological sciences students with no prior clinical radiotherapy placement experience completed 20 simulated radiotherapy decision-making scenarios with 13 correct and seven deliberately incorrect AI recommendations (1080 trials). Initial and final responses, AI advice acceptance, confidence, perceived workload, and attitudes toward AI verification were assessed. Students were assigned to time-pressure (n = 26) or no-time-pressure (n = 28) conditions. Harmful automation bias was operationally defined as switching from an initially correct response to an incorrect response matching AI advice. Results: Harmful automation bias occurred in 20/220 eligible trials (9.1%), affecting 15/54 students (27.8%). Incorrect AI advice was followed in 137/378 trials (36.2%). Overall accuracy remained similar before and after AI exposure (75.65% vs. 74.91%; p = 0.289). Time pressure increased workload, but no statistically significant association with harmful automation bias was observed (OR = 0.78; p = 0.654). Lower post-decision confidence was associated with harmful automation bias (OR = 0.599; p = 0.028). No statistically significant difference in harmful automation bias was observed between participants who supported AI verification and those who did not. Conclusions: Harmful automation bias can occur despite stable overall accuracy, suggesting that greater emphasis on critical appraisal, active verification, and independent judgement may be valuable in radiotherapy education.

Healthcare educators face the challenge of adapting to new and innovative teaching practices to engage learners and support them to achieve learning outcomes. Educational technologies can assist with this, and extended reality, including virtual, mixed, and augmented realities, can be a potential solution. The aim of this study was to document healthcare professional educators’ perspectives on the use of extended reality (XR) to teach child health, with a view to using it for child development teaching. Using a qualitative exploratory design, focus-group discussions with health professional educators were held. Data from the focus group was analyzed using indicative content analysis. Fifteen participants engaged in the focus-group discussions. Analysis of this data resulted in the development of three categories: real children are the best for learning, but they come with challenges; there is an appeal to using XR; and there are challenges and enablers to using extended realities in child health teaching. Whilst the participants initially did not think that using a form of extended reality to simulate and educate healthcare learners about a well child would be useful, they did see that it does have potential in developmental screening. These results are useful for healthcare educators when considering educational technologies in their practice.

Background/Objectives: Cancer genomic testing presents unique communication and educational challenges for healthcare providers, requiring specialized training approaches. We developed and evaluated a communication skills training program specifically designed for cancer genomic testing based on the SHARE protocol to reduce psychological distress in both medical staff and patients. Methods: A mixed-methods evaluation was conducted of a structured training program implemented across 10 institutions in Yamagata Prefecture. The program incorporated SHARE protocol-based training, role-playing sessions, and specialized modules addressing cancer genomic testing-specific challenges. Quantitative assessment utilized pre- and post-intervention questionnaires, and qualitative evaluation employed semi-structured interviews analyzed using exploratory inductive analysis. Results: Among 251 participants, 95% initially reported psychological burden when explaining cancer genomic testing results. Post-intervention, 95% reported a perceived reduction in psychological burden, with 84% expressing training satisfaction. Exploratory analyses suggested that the perceived reduction in psychological burden was significantly greater in nurses than other professionals. Qualitative analysis of 19 interviews identified two conceptual categories among healthcare providers (demand for ongoing feedback and educational reinforcement, and anxiety and response needs regarding genetic results) and four conceptual categories among patients and family members (information acquisition needs, trust in and expectations of healthcare providers, psychological burden related to genetic information, and equitable access requirements for testing). Conclusions: This educational initiative was associated with self-reported perceived reductions in psychological burden and anxiety related to cancer genomic testing communication immediately following training. The mixed-methods evaluation provides preliminary, hypothesis-generating input for curriculum development and faculty training in genomic medicine communication.