Bibliographic record
Abstract
Objective: Forensic science is a superstructure that encompasses many specialized fields that require expertise. In recent years, studies have been conducted on artificial intelligence and machine learning-based applications in almost all areas of forensic science. The aim of our study is to determine the trends related to the research/application areas of artificial intelligence and machine learning-based programs in forensic sciences and to make predictions about the future of the subject, and to contribute to the professionals working in the field. Methods: When the Web of Science database was searched with the keywords “artificial intelligence/machine learning” and “forensic/forensic science” in the title or abstract between 2001-2023, 229 results were obtained. Simple frequency analyses were performed using IBM SPSS 23 software for the study, and R Studio and Vosviewer (version1.16.19) programs were used for bibliometric analysis. Results: It was found that there were 229 publications meeting the criteria, and the most studies on the subject were published in International Journal of Legal Medicine with 9 publications. The most frequently published countries were the United States with 32 (13.9%) publications, China with 30 (13.04%) publications, and India with 23 (10%) publications. The most commonly used keywords in the publications were “artificial intelligence”, “deep learning” and “machine learning”. Conclusion: The results of analysis show that artificial intelligence and machine learning-based systems have become increasingly studied in many areas of forensic science in recent years. As machine learning/artificial intelligence programs are developed, it is likely that these applications will be used in forensic science/medicine practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".