ReviewForensic science, medicine, and pathology2025
Digital pathology in forensic science: a systematic review of the literature.
Review in Forensic science, medicine, and pathology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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
5 citing papers in PubMed.
- Autopsy Pathology's Paradigm Shift: Artificial Intelligence and Emerging Technologies in the Era of Digitally Integrated Death Investigation.Diagnostics (Basel, Switzerland) · 2026Review
- Comment on: Digital pathology for postmortem investigation: technical maturity and evidentiary implementation.Forensic science, medicine, and pathology · 2026Article
- Development of a deep learning-based tool for coronary artery stenosis evaluation in forensic autopsies using whole slide imaging.International journal of legal medicine · 2026Article
- From forensic medical databases to court-defensible artificial intelligence: the next translational step is standards, not scale.Forensic science, medicine, and pathology · 2026Article
- From forensic 3D avatars to courtroom-grade digital twins: the next translational step for AI-assisted injury documentation.Forensic science, medicine, and pathology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDigital pathology (DP) and whole-slide imaging (WSI) are increasingly utilized in clinical pathology; however, their role in forensic medicine remains less defined, as evidentiary standards demand robust validation, auditability, and a chain of custody.
methodsWe conducted a systematic review of PubMed, Scopus, and Web of Science for studies that applied DP and WSI to forensic, autopsy, or postmortem contexts, with eligibility requiring peer-reviewed human studies that reported methods and outcomes. Data were charted for study design, tissue, devices/software, and outcomes (diagnostic agreement, quantitative metrics, validation/quality assurance (QA)).
resultsThe search retrieved 361 records; after screening and full-text assessment, 21 studies were selected for inclusion. Fifteen studies primarily advanced diagnostic knowledge using postmortem material (e.g., quantitative neuropathology and organ-specific morphometry), while five had direct forensic aims (casework validation or core forensic tests).
conclusionsThe review highlights that DP is technically ready for medico-legal workflows; however, its use remains low compared to other clinical settings. Adoption in forensics should centre on CAP-style, use-case-specific validation, traceable/auditable pipelines (including hashing, logs, and tile-linked overlays), stain/colour governance, and external robustness testing. Under these conditions, DP can deliver reproducible, transparent, and court-defensible evidence across forensic practice.
Indexed as
Identifiers
41389319What 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.