The culture of doubt: Do medical students really experience clinical uncertainty when they should?

Uncertainty is a fundamental aspect of medical practice, necessitating incorporation into undergraduate medical training. The integrative model of uncertainty tolerance (UT) developed by Hillen and Han serves as a comprehensive framework for exploring clinical uncertainty. While studies have extensively examined UT dimensions, including sources, responses, and moderators, the factors influencing the perception of uncertainty stimuli remain underexplored. However, students' ability to perceive uncertainty and their approach to uncertain stimuli play a crucial role in enabling them to develop adaptive responses to uncertainty, necessary for their comfort in these situations. Defining uncertainty as a metacognitive state suggests significant variability in its perception among individuals and within an individual over time. Moreover, several studies have demonstrated the substantial influence of various individual and contextual factors on how individuals perceive and respond to uncertainty. In this paper, the authors present multiple hypotheses to address the question of whether students genuinely perceive uncertainty stimuli when they should. The authors argue that students' personal relationship with their knowledge is essential in their ability to identify clinical uncertainty, particularly concerning the limits of medical knowledge. Therefore, they propose that an academic culture fostering doubt, through exposing students to a variety of perspectives, would enhance their ability to identify uncertainty zones in a clinical situation at an early stage. Drawing on Dewey's situational theory, the authors emphasize the importance of better understanding, in a work setting, the influence of contextual and situational characteristics on individual perceptions of uncertainty. In line with this idea, ethnographic studies would offer valuable insights into identifying the relationship between the students, their work environment, and their perception of clinical uncertainty.

[Do artificial intelligence systems reason in the same way as clinicians when making diagnoses?] / Une intelligence artificielle raisonne-t-elle de la même façon que les cliniciens pour poser des diagnostics ?

Clinical reasoning is at the heart of physicians' competence, as it allows them to make diagnoses. However, diagnostic errors are common, due to the existence of reasoning biases. Artificial intelligence is undergoing unprecedented development in this context. It is increasingly seen as a solution to improve the diagnostic performance of physicians, or even to perform this task for them, in a totally autonomous and more efficient way. In order to understand the challenges associated with the development of artificial intelligence, it is important to understand how the machine works to make diagnoses, what are the similarities and differences with the physician's diagnostic reasoning, and what are the consequences for medical training and practice.

Can teaching methods based on pattern recognition skill development optimise triage in mass-casualty incidents?

BACKGROUND: Patient's triage is a key element of mass-casualty incidents (MCIs) response, although performance of healthcare professionals in triage proved to be poor. It was assessed if specific teaching methods based on pattern recognition skill development can help healthcare students to improve their performance in triage. METHODS: 128 medical and nursing students have been assessed while performing triage during disaster medicine exercises. Half of them (group 1) had previously been involved in a standard curriculum. The remaining half (group 2) benefited from specific teaching methods based on pattern recognition skill development. RESULTS: Performance of group 2 was significantly higher compared with group 1 (p<0.01). Group 2 had a lower overtriage rate. CONCLUSIONS: Disaster medicine courses based on pattern recognition skill development improve triage performance of healthcare students during simulated MCI. This study may have a high impact on healthcare students and professionals' education in the perspective of MCIs preparedness and response.