ReviewJournal of education and health promotion2026
A strategic framework for data science integration in undergraduate medical education and clinical training.
Review in Journal of education and health promotion, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Data science is rapidly transforming medical research and clinical practice. There is growing recognition of the need to integrate data science into medical education to equip healthcare professionals with the skills to effectively use data in patient care and innovation. This article presents a conceptual model for integrating data science into medical education, highlighting educational strategies, implementation barriers, and opportunities. The integration of data science in medical education offers benefits such as enhancing diagnostic accuracy, personalizing treatments, and advancing research. However, challenges exist, including the need for specialized knowledge and limited resources. The article highlights these challenges while emphasizing the importance of addressing them to foster data-driven decision-making in healthcare. For example, a machine learning model predicting hospital readmission risk can help clinicians allocate resources more efficiently. Hypothetical examples throughout the article illustrate practical applications of data science, including machine learning for predicting patient outcomes, identifying disease risk factors, discovering new treatments, and uncovering patterns in patient data. In conclusion, the article underscores the importance of embracing data science within medical education. By doing so, medical professionals and students can be empowered to leverage data in making informed decisions, advancing research, and ultimately improving patient care.
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.