ReviewAcademic pathology
Establishing priorities for implementation of large language models in pathology and laboratory medicine.
Review in Academic pathology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Agentic AI workflows in anatomic pathology: Opportunities and challenges.Journal of pathology informatics · 2026Review
- Benchmarking large language models for medical education: performance on the clinical laboratory technician qualification examination.Frontiers in medicine · 2026Article
- The AI-powered pathologist: A global survey mapping initial trends in AI adoption and outlook.Journal of pathology informatics · 2025Article
- Comparative analysis of the performance of the large language models ChatGPT-3.5, ChatGPT-4 and Open AI-o1 in the field of Programmed Cell Death in myeloma.Discover oncology · 2025Article
- REASONING BEYOND ACCURACY: EXPERT EVALUATION OF LARGE LANGUAGE MODELS IN DIAGNOSTIC PATHOLOGY.medRxiv : the preprint server for health sciences · 2025Article
- Leveraging large language models to improve patient education on dry eye disease.Eye (London, England) · 2025Article
- Based on Medicine, The Now and Future of Large Language Models.Cellular and molecular bioengineering · 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence and machine learning have numerous applications in pathology and laboratory medicine. The release of ChatGPT prompted speculation regarding the potentially transformative role of large-language models (LLMs) in academic pathology, laboratory medicine, and pathology education. Because of the potential to improve LLMs over the upcoming years, pathology and laboratory medicine clinicians are encouraged to embrace this technology, identify pathways by which LLMs may support our missions in education, clinical practice, and research, participate in the refinement of AI modalities, and design user-friendly interfaces that integrate these tools into our most important workflows. Challenges regarding the use of LLMs, which have already received considerable attention in a general sense, are also reviewed herein within the context of the pathology field and are important to consider as LLM applications are identified and operationalized.
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.