ReviewCureus2026
Artificial Intelligence Language Models in Oncology: A Cross-Sectional Analysis of Published Studies.
Review in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Since its public release in November 2022, ChatGPT has been rapidly evaluated across medical disciplines, including oncology. However, the scope, methodological characteristics, and clinical focus of oncology-specific evaluations remain poorly characterized. We conducted a descriptive, cross-sectional meta-research analysis of oncology-related studies that explicitly evaluated ChatGPT, identified through Ovid Medline and Embase from database inception through December 25, 2025. Included studies were categorized by study design, oncology discipline, and artificial intelligence (AI) task, and results were summarized descriptively. A total of 1,325 oncology-related studies evaluating ChatGPT were identified, with publication volume increasing over time, including 128 (10%) in 2023, 540 (41%) in 2024, and 657 (50%) in 2025. Most studies were clinical studies (949, 72%), predominantly methodological or performance evaluation studies (658, 69% of clinical studies). Research activity was concentrated in general or multidisciplinary oncology (702, 53%) and radiation oncology (575, 43%), with limited representation in medical oncology (26, 2%) and basic science (22, 2%), and no identified studies in surgical oncology (0, 0%). ChatGPT was most frequently evaluated for diagnostic accuracy or classification tasks (760, 57%), followed by mixed AI tasks (371, 28%). Oncology-focused evaluations of ChatGPT are predominantly concentrated in methodological and performance-based study designs, with a strong emphasis on diagnostic applications and a limited range of oncology disciplines. These patterns indicate that current research has primarily focused on establishing model performance within structured evaluations, with comparatively less emphasis on broader clinical contexts. This study defines the current research landscape and provides a structured reference for interpreting how ChatGPT is being studied across oncology.
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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.