Canonical text (the exact bytes fingerprinted)

TRUVACE RECORD VERSION
record: TRV-2026-2157
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-22T10:42:02.611247Z
status: published
lens: p_space
sector: health
headline: Associations of patient safety-related perceptions and lifelong learning-related attitudes with critical trust in AI/digital clinical tools among medical students: a cross-sectional study
dek: Patient safety, lifelong learning, and the safe use of artificial intelligence (AI) are increasingly relevant to undergraduate medical education, but their interrelationships remain poorly understood. This study examined medical students’ patient safety-related perceptions, lifelong learning-related attitudes, and critical trust in AI/digital clinical tools. This single-center cross-sectional survey was conducted at Gaziantep University Faculty of Medicine, Türkiye, between April and May 2026. Of 366 submitted r…
gain_title: (none)
problem_title: Few medical students had recent patient safety or AI/digital clinical tool training, and reported AI training was inversely associated with critical trust, leaving preparation for safe AI use thin.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Few medical students had recent patient safety or AI/digital clinical tool training, and reported AI training was inversely associated with critical trust, leaving preparation for safe AI use thin.
problem_evidence: AI/digital health or clinical decision support system training by 50 (13.9%) | reported AI/digital health training was inversely associated (B = − 0.177, p = 0.018)
quick_read: A single-center survey at Gaziantep University Faculty of Medicine in Türkiye collected responses from 359 students in April and May 2026. It measured patient safety perceptions, lifelong learning attitudes, and critical trust in AI and digital clinical tools using study-developed Likert-type items.

The results suggest that students who hold stronger patient-safety perceptions also tend to evaluate AI-supported recommendations more critically, which may matter for how medical schools teach safe AI use. Because the design is cross-sectional and exploratory, it cannot show that safety teaching causes better judgment about AI, and the size of the link depended on how scores were built.
limitation: The study is a single-center, exploratory cross-sectional survey using study-developed items, so it cannot establish causal or curricular effects, and the estimated association size varied with how scores were constructed.
tag: Evidence-backed problem
key_points: Of the 359 analyzed students, 72 (20.1%) reported patient safety training in the previous 12 months. | Only 50 students (13.9%) reported AI/digital health or clinical decision support training. | Among 79 clinical-phase students, 10 (12.7%) had encountered an adverse-event or near-miss reporting system. | The patient-safety index correlated positively with the critical-trust index (Spearman's ρ = 0.453). | Reported AI/digital health training was inversely associated with critical trust, while clinical-phase status was positively associated.
rundown: The survey received 366 responses; seven from students younger than 18 were excluded. Analysis used exploratory factor analysis informed by parallel analysis, nonparametric tests, and multivariable linear regression.

In the regression, the patient-safety index had the largest standardized association with critical trust (B = 0.473, β = 0.507, p < 0.001). Clinical-phase status was also positively associated (B = 0.167, p = 0.010).

Simply having used digital clinical or AI-based tools was not independently associated with critical trust. In the complete-item sensitivity model, the patient-safety association persisted but weakened (β = 0.292), so the effect size was sensitive to score construction.
sources:
- peer_reviewed | BMC Medical Education | https://doi.org/10.1186/s12909-026-10677-x | 2026-10-21
prev: 0000000000000000000000000000000000000000000000000000000000000000

sha256

3d5081d9e02aad8b89398e33cb2318688d5e2af8fe1191b58c9ef105290b85ef

previous

0000000000000000000000000000000000000000000000000000000000000000