ArticleDiscover oncology2025
A preliminary investigation into the potential, pitfalls, and limitations of large language models for mammography interpretation.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
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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, 1 synthesis or guideline pooled it.
- Effectiveness, Safety, and Workflow Burden of Large Language Model-Based Medical Report Generation: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Benchmarking GPT-5, Gemini 2.5 Pro, Grok 4, and other LLMs on pediatric dentistry questions from a dental specialization exam.Scientific reports · 2026Article
- Performance of Vision-Enabled Large Language Models in Image-Based Electrocardiogram Interpretation: Exploratory Evaluation.Journal of medical Internet research · 2026Article
- Performance of ChatGPT-4o, Claude 3 Opus, and DeepSeek-R1 in BI-RADS Category 4 Classification and Malignancy Prediction From Mammography Reports: Retrospective Diagnostic Study.JMIR medical informatics · 2025Article
- Keeping AI in medicine and radiology within the framework of scientific method: measuring to close the epistemic gap.Insights into imaging · 2025Article
- Limitations of broadly trained LLMs in interpreting orthopedic Walch glenoid classifications.Frontiers in artificial intelligence · 2025Article
- A systematic review and meta-analysis of GPT-based differential diagnostic accuracy in radiological cases: 2023-2025.Frontiers in radiology · 2025Article
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
This study evaluates the capabilities of large language models, specifically GPT-4, in interpreting mammographic images. The analysis involved 120 mammographic images equally divided between cases with and without mammography's findings. Without additional context, the LLM was tasked to generate reports based solely on these images. GPT-4 correctly identified mammographic projections in 53.3% of cases and showed varying degrees of accuracy in identifying microcalcifications and masses. The study highlighted GPT-4's embryonic interpretative abilities with a sensitivity of 50.0% and specificity of 37.5%. However, a significant rate of false positives and false negatives, along with hallucinations, underscored the model's limitations. This exploratory test offers insights into the potential and risks of using LLMs in mammography interpretation, also underscoring the need for dedicated training, validation, and regulation of AI tools in healthcare to ensure their reliability and safety in clinical practice.
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.