Evidence map›Paper›PMID 39469392›Full record

ReviewCureus2024

Artificial Intelligence in Forensic Sciences: A Systematic Review of Past and Current Applications and Future Perspectives.

Ioannis Ketsekioulafis, Giorgos Filandrianos, Konstantinos Katsos, Konstantinos Thomas, Chara Spiliopoulou, Giorgos Stamou, Emmanouil I Sakelliadis

Abstract readReview
In one paragraph

Review in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Review
  6. Review
  7. Article
  8. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Ioannis KetsekioulafisDepartment of Forensic Medicine and Toxicology, National and Kapodistrian University of Athens School of Medicine, Athens, GRC.
Giorgos FilandrianosArtificial Intelligence and Learning Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, GRC.
Konstantinos KatsosDepartment of Forensic Medicine and Toxicology, National and Kapodistrian University of Athens School of Medicine, Athens, GRC.
Konstantinos ThomasArtificial Intelligence and Learning Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, GRC.
Chara SpiliopoulouDepartment of Forensic Medicine and Toxicology, National and Kapodistrian University of Athens School of Medicine, Athens, GRC.
Giorgos StamouArtificial Intelligence and Learning Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, GRC.
Emmanouil I SakelliadisDepartment of Forensic Medicine and Toxicology, National and Kapodistrian University of Athens School of Medicine, Athens, GRC.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The aim of this study is to review the available knowledge concerning the use of artificial Intelligence (AI) in general in different areas of Forensic Sciences from human identification to postmortem interval estimation and the estimation of different causes of death. This paper aims to emphasize the different uses of AI, especially in Forensic Medicine, and elucidate its technical part. This will be achieved through an explanation of different technologies that have been so far employed and through new ideas that may contribute as a first step to the adoption of new practices and to the development of new technologies. A systematic literature search was performed in accordance with the Preferred Reported Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines in the PubMed Database and Cochrane Central Library. Neither time nor regional constrictions were adopted, and all the included papers were written in English. Terms used were MACHINE AND LEARNING AND FORENSIC AND PATHOLOGY and ARTIFICIAL AND INTELIGENCE AND FORENSIC AND PATHOLOGY. Quality control was performed using the Joanna Briggs Institute critical appraisal tools. A search of 224 articles was performed. Seven more articles were extracted from the references of the initial selection. After excluding all non-relevant articles, the remaining 45 articles were thoroughly reviewed through the whole text. A final number of 33 papers were identified as relevant to the subject, in accordance with the criteria previously established. It must be clear that AI is not meant to replace forensic experts but to assist them in their everyday work life.

Indexed as

artificial intelligencedeep learningforensic medicineforensic pathologyforensic sciencesmachine learning

Identifiers

PMID39469392
PMCPMC11513614

What OpenQuestion holds

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Registered trials

None linked

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.