ReviewFrontiers in medicine2026
AI-driven transformation in forensic medicine education: applications, pedagogical shifts, and future challenges.
Review in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 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
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Use of artificial intelligence in education and training of radiology.Frontiers in radiology · 2026Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Forensic medicine, as an interdisciplinary field featuring a high degree of practicality and technological integration, is currently faced with several challenges, including limited teaching resources, few practical training opportunities, and slow adoption of emerging technologies. With the rapid advancement of artificial intelligence (AI), its growing application in medical education has significantly transformed the landscape of traditional teaching approaches in forensic medicine, opening up new possibilities for innovations in pedagogical models. Against this backdrop, this study examines the potential of AI technology in forensic medicine from the perspectives of pedagogical model innovation, content delivery, and evaluation system reform, with specific emphasis on virtual simulation instruction, intelligent case analysis, and personalized learning pathways. Furthermore, it discusses existing ethical, technical, and resource-related limitations in current research. Based on this study, the paper proposes strategies and practical pathways to promote the application of AI in forensic medicine education, aiming to provide theoretical insights for cultivating high-quality forensic medicine professionals.
Indexed as
Identifiers
What Socratic 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 Socratic graph.