Evidence map›Paper›PMID 40236563›Full record

ArticleJournal of pathology informatics2025

PIRO: A web-based search platform for pathology reports, leveraging large language models to generate discrete searchable insights.

Scott Robertson, Venkata Koppireddy, Jeremy Cumbo, Hooman Rashidi, Samer Albahra

Erratum issuedAbstract read
In one paragraph

Article in Journal of pathology informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Scott RobertsonCenter for Diagnostics and Artificial Intelligence, Cleveland Clinic Foundation, Department of Pathology and Laboratory Medicine, 9500 Euclid Ave, Cleveland, OH 44195, USA.
Venkata KoppireddyCenter for Diagnostics and Artificial Intelligence, Cleveland Clinic Foundation, Department of Pathology and Laboratory Medicine, 9500 Euclid Ave, Cleveland, OH 44195, USA.
Jeremy CumboCenter for Diagnostics and Artificial Intelligence, Cleveland Clinic Foundation, Department of Pathology and Laboratory Medicine, 9500 Euclid Ave, Cleveland, OH 44195, USA.
Hooman RashidiComputational Pathology and AI Center of Excellence (CPACE), University of Pittsburgh School of Medicine, Department of Pathology, 200 Lothrop Street, Pittsburgh, PA 15261, USA.
Samer AlbahraCenter for Diagnostics and Artificial Intelligence, Cleveland Clinic Foundation, Department of Pathology and Laboratory Medicine, 9500 Euclid Ave, Cleveland, OH 44195, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pathologists rely on access to historical diagnostic case texts for research, education, and peer learning. However, many laboratory information systems (LIS), including Epic Beaker, lack optimized search tools tailored to pathology-specific text queries. To address this need, we developed PIRO (Pathology Information Retrieval Optimizer), a web-based platform enabling efficient text searches of diagnostic archives. Built using FastAPI, Angular, and Apache Solr, PIRO supports both basic and advanced search functionalities, faceted filtering, and data extraction, while ensuring compliance with institutional privacy protocols. PIRO's capabilities extend to case cohort building, search result export, and secure access control within the institutional network. In an 8-month study, we observed significantly higher PIRO adoption rates (67 %) among pathologists compared to Epic Beaker's SlicerDicer (9 %), underscoring PIRO's usability and relevance. Additionally, we implemented a large language model (LLM) to annotate reports with a "Malignancy Risk" label, enhancing search precision and enabling future expansion of automated annotations. Ongoing work focuses on integrating PIRO with our digital pathology platform, enabling direct access to digital slides from case results. PIRO's adaptable design makes it applicable across institutions, advancing search and retrieval efficiency in pathology archives and enhancing support for pathology research and education.

Indexed as

Full-textLarge language modelPathology reportsSearchUnstructured text

Identifiers

PMID40236563
PMCPMC11999576

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.