To prepare medical students for their future role addressing complex health problems, medical education should pay attention to students’ Personal and Professional Development (PPD). Meaningful reflection plays an essential role in such education. We aimed to explore how to facilitate PPD-related reflections, periodic retrospection and collation by medical students. Methods We performed an intervention study with fourth-year medical students at the University Medical Centre Utrecht in 2024. The interventions consisted of workshops and individual assignments addressing PPD with three different formats for reflection (note to self, core value or representative item), retrospection (compilation, value mapping or self-scoring), and collection (online or analogue). These were analyzed using a convergent mixed methods design with data from Likert scales and open questions in a survey, focus groups and analysis of the submitted reflection materials. Results Thirty-four students completed the intervention (participation rate 100%), 33 students completed the survey (response rate 97%). The format of a making a note to oneself using text/video/audio/image was experienced as the most suitable form of reflection. Students experienced the retrospection systems as useful, fun, and/or actionable and most appreciated the opportunity to trace their personal development. An online medium was preferred for the collection of reflections, but ease-of-use and an overview display option of the collected materials were deemed crucial requirements.
Conclusions
Students found the reflection, retrospection and collation methods useful and desirable. Most important for the future design is the freedom to choose and adapt, as well as a balance in time investment and perceived added value. Further research should focus on development of a suitable online medium and test this in a longitudinal setting to address retrospection suitability.

In recent years, there has been an increasing interest in developing and using artificial intelligence (AI) models in pathology. Although pathologists generally have a positive attitude toward AI, they report a lack of knowledge and skills regarding how to use it in practice. Furthermore, it remains unclear what skills pathologists would require to use AI adequately and responsibly. However, adequate training of (future) pathologists is essential for successful AI use in pathology. In this paper, we assess which entrustable professional activities (EPAs) and associated competencies pathologists should acquire in order to use AI in their daily practice. We make use of the available academic literature, including literature in radiology, another image-based discipline, which is currently more advanced in terms of AI development and implementation. Although microscopy evaluation and reporting could be transferrable to AI in the future, most of the current pathologist EPAs and competencies will likely remain relevant when using AI techniques and interpreting and communicating results for individual patient cases. In addition, new competencies related to technology evaluation and implementation will likely be necessary, along with knowing one’s own strengths and limitations in human-AI interactions. Because current EPAs do not sufficiently address the need to train pathologists in developing expertise related to technology evaluation and implementation, we propose a new EPA to enable pathology training programs to make pathologists fit for the new AI era “using AI in diagnostic pathology practice” and outline its associated competencies.

In the medical literature, promising results regarding accuracy of medical AI are presented as claims for its potential to increase efficiency. This elision of concepts is misleading and incorrect. First, the promise that AI will reduce human workload rests on a too narrow assessment of what constitutes workload in the first place. Human operators need new skills and deal with new responsibilities, these systems need an elaborate infrastructure and support system that all contribute to an increased amount of human work and short-term efficiency wins may become sources of long-term inefficiency. Second, for the realization of increased efficiency, the human-side of technology implementation is determinate. Human knowledge, competencies and trust can foster or undermine efficiency. We conclude that is important to remain conscious and critical about how we talk about expected benefits of AI, especially when referring to systemic changes based on single studies.