# An open-source interactive AI framework for assisting automatic literature review in forensic medicine: Focus on brain injury mechanisms

## TL;DR

This paper explores the use of an open-source AI tool called ASReview to improve the efficiency of literature reviews in forensic medicine, particularly for studying brain injury mechanisms.

## Contribution

The study is the first to evaluate ASReview's applicability to forensic medicine literature reviews.

## Key findings

- ASReview efficiently prioritized relevant studies and excluded irrelevant ones in forensic medical literature.
- Model performance remained stable with training data comprising less than 80% of the sample.
- ASReview's predictions aligned closely with human reviewers when training set proportions ranged from 10% to 55%.

## Abstract

Systematic reviews and meta-analyses are critical in forensic medicine; however, these processes are labor-intensive and time-consuming. ASReview, an open-source machine learning framework, has demonstrated potential to improve the efficiency and transparency of systematic reviews in other disciplines. Nevertheless, its applicability to forensic medicine remains unexplored. This study evaluates the utility of ASReview for forensic medical literature review.

A three-stage experimental design was implemented. First, stratified five-fold cross-validation was conducted to assess ASReview’s compatibility with forensic medical literature. Second, incremental learning and sampling methods were employed to analyze the model’s performance on imbalanced datasets and the effect of training set size on predictive accuracy. Third, gold standard were translated into computational languages to evaluate ASReview’s capacity to address real-world systematic review objectives.

ASReview exhibited robust viability for screening forensic medical literature. The tool efficiently prioritized relevant studies while excluding irrelevant records, thereby improving review productivity. Model performance remained stable when labeled training data constituted less than 80% of the total sample size. Notably, when the training set proportion ranged from 10% to 55%, ASReview’s predictions aligned closely with human reviewer decisions.

ASReview represents a promising tool for forensic medical literature review. Its ability to handle imbalanced datasets and gather goal-oriented information enhances the efficiency and transparency of systematic reviews and meta-analyses in forensic medicine. Further research is required to optimize implementation strategies and validate its utility across diverse forensic medical contexts.