ReviewJournal of pharmacy & bioallied sciences2024
Artificial Intelligence in Histopathology.
Review in Journal of pharmacy & bioallied sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Retrospective Validation of a Resource-Aware Assistive AI Tool for Screening Prostate Needle Biopsies and Classifying Prostate Adenocarcinoma into ISUP Grade Groups.Life (Basel, Switzerland) · 2026Article
- The expanding role of artificial intelligence in personalised medicine: from innovation to individualized care.Frontiers in medicine · 2026Review
- Autoimmune Diseases of the Eyelid Skin: Molecular Pathways, Clinical Manifestations, and Therapeutic Insights.International journal of molecular sciences · 2025Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence will be revolutionizing the healthcare in near future and is already being integrated in several areas. The utilization of artificial intelligence algorithms to extract quantitative information from full-slide histopathology images has been demonstrated in digital pathology. Artificial intelligence (AI) is anticipated to reduce the workload of pathologists, enhance the impartiality and uniformity of pathology reports, and impact treatment choices through the identification of concealed information within easily accessible data. This review sheds light on how deep learning and machine learning can enhance the imaging of the slides in digital pathology and help physicians make the diagnosis faster.
Indexed as
Identifiers
What 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.