Comparative Impact of ChatGPT and Conventional Search Tools on Clinical Reasoning Performance: A Randomized Crossover Study in Preclinical Medical Students [Letter]

A Randomized Crossover Study in Preclinical Medical Students". 1 The study provides timely insights into the integration of Artificial Intelligence (AI) tools within medical education and highlights the complementary role of large language models and traditional search strategies in enhancing clinical reasoning.While the findings are promising, several methodological aspects warrant further clarification and critical appraisal.First, although the randomized crossover design strengthens internal validity by allowing participants to serve as their own controls, the potential for carryover and period effects remains a concern.The authors implemented a 60-minute washout period; however, given the cognitive nature of clinical reasoning and learning retention, this duration may be insufficient to eliminate residual learning effects between interventions.The progressive improvement in scores across phases raises the possibility that learning or practice effects, rather than the intervention itself, contributed substantially to the observed outcomes. 2 Second, the sampling framework is limited to a single institution with a relatively small cohort (n = 46), which restricts the generalizability of the findings.Although the authors justified the sample size statistically, the inclusion of a homogeneous group of second-year medical students may not adequately represent variability in baseline clinical reasoning abilities across different educational settings. 3 Third, the assessment of clinical reasoning using an eight-point rubric, although structured, raises questions regarding its psychometric robustness.While content validity was reviewed by faculty members, the absence of reported inter-rater reliability, construct validity, and sensitivity to change limits confidence in the measurement of such a complex construct.Clinical reasoning is inherently multidimensional, and reliance on a single rubric-based tool may oversimplify its evaluation. 4 Furthermore, the study utilized paired t-tests for statistical analysis, which are appropriate for within-subject comparisons; however, the absence of adjustments for multiple comparisons may increase the risk of Type I error.Additionally, while effect sizes were reported, the interpretation of cumulative improvement across phases should be approached cautiously due to the potential confounding influence of repeated testing.Another important consideration is the lack of standardization in ChatGPT prompting strategies.Participants were allowed self-directed prompting, which introduces variability and limits reproducibility.Given that prompt engineering significantly influences AI-generated outputs, this factor represents a critical source of bias that should be controlled or at least systematically documented.