ReviewJournal of pathology informatics2023
Organizational preparedness for the use of large language models in pathology informatics.
Review in Journal of pathology informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- Agentic AI workflows in anatomic pathology: Opportunities and challenges.Journal of pathology informatics · 2026Review
- Symptoms and causes of obstacles in AI-based telemonitoring: strategies for system development through a CRISP-DM lens.Frontiers in artificial intelligence · 2026Article
- The role of artificial intelligence-based foundation models and "copilots" in cancer pathology: potential and challenges.Journal of experimental & clinical cancer research : CR · 2025Review
- Applications of Artificial Intelligence in the Control of Infectious Diseases in the Post-COVID Era: Scoping Review.JMIR nursing · 2025Article
- The AI-powered pathologist: A global survey mapping initial trends in AI adoption and outlook.Journal of pathology informatics · 2025Article
- PIRO: A web-based search platform for pathology reports, leveraging large language models to generate discrete searchable insights.Journal of pathology informatics · 2025Article
- Unveiling the risks of ChatGPT in diagnostic surgical pathology.Virchows Archiv : an international journal of pathology · 2025Article
- Comprehensive testing of large language models for extraction of structured data in pathology.Communications medicine · 2025Article
- Performance Evaluation of Large Language Models in Cervical Cancer Management Based on a Standardized Questionnaire: Comparative Study.Journal of medical Internet research · 2025Article
- Advanced hybrid deep learning model for enhanced evaluation of osteosarcoma histopathology images.Frontiers in medicine · 2025Article
- Validation of large language models for detecting pathologic complete response in breast cancer using population-based pathology reports.BMC medical informatics and decision making · 2024Article
- Applications of Large Language Models in Pathology.Bioengineering (Basel, Switzerland) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, we consider the current and potential role of the latest generation of Large Language Models (LLMs) in medical informatics, particularly within the realms of clinical and anatomic pathology. We aim to provide a thorough understanding of the considerations that arise when employing LLMs in healthcare settings, such as determining appropriate use cases and evaluating the advantages and limitations of these models. Furthermore, this paper will consider the infrastructural and organizational requirements necessary for the successful implementation and utilization of LLMs in healthcare environments. We will discuss the importance of addressing education, security, bias, and privacy concerns associated with LLMs in clinical informatics, as well as the need for a robust framework to overcome regulatory, compliance, and legal challenges.
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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.