ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2024
Comparative Analysis of Data Generation Techniques for Breast Cancer Research Using Artificial Intelligence.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. 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
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study investigates the use of ChatGPT to support clinical teams with limited expertise in generating synthetic data for breast cancer research. It assesses ChatGPT's application, focusing on effective prompting and best practices for creating high-fidelity synthetic data. The research compares the generated synthetic data to the Wisconsin Breast Cancer Dataset through statistical analysis, structural similarity metrics, and machine learning performance. Results indicate that the quality of prompts and generation techniques significantly affects the data's fidelity. The study highlights the critical role of prompt engineering and data synthesis techniques in producing accurate synthetic data for healthcare research, underscoring the need for precise prompts and generation methods to maintain data integrity in sensitive areas like cancer research.
Indexed as
Identifiers
40417503PMC12099320What 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.