ArticleBMC medical education2025
AI-powered platform revolutionizing blood cell morphology education for medical students.
Article in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The effectiveness of GAI-assisted teaching methods in medical education: a systematic review and meta-analysis.Frontiers in public health · 2026Pooled it
- The benchmark illusion: on the curious underestimation of AI and the uncertainty of its creators' gold standards.EClinicalMedicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundThis study aims to preliminarily explore the advantages and potential issues of artificial intelligence in the teaching of blood cell morphology to undergraduate medical students, so as to provide theoretical support and practical experience for promoting the intelligent transformation of medical education.
methodsUndergraduate students from the 2021 cohort of the Aerospace School of Clinical Medicine at Peking University were assigned as the experimental group, while students from the 2020 cohort served as the control group. The experimental group utilized the AI platform to study blood cell morphology, whereas the control group relied on conventional teaching methods. We compared the accuracy rates of cell identification between two groups of students. Additionally, we conducted supplementary research through questionnaires, post-class interviews, and classroom observations.
resultsThe experimental group achieved a significantly higher average score in cell identification (87.82 ± 9.63) compared to the control group (74.83 ± 12.41) (P<0.0001). The correct identification rates of metamyelocytes, eosinophils, and monocytes in the experimental group were significantly increased by over 30%. DISCUSSION: AI holds considerable promise in medical education, particularly in the instruction of hematology cell morphology. Nevertheless, further research is required. Traditional microscope-based teaching should not be completely dismissed, as current digital platforms for blood cells do not yet capture all cellular nuances.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.