Public articles linked to the same research event.
medRxiv This study conducted a systematic mapping and bibliometric analysis of 4,143 publications from 2015 to 2025 on AI-generated synthetic data in biomedicine, combining expert annotation with LLM-assisted classification across data modality, medical domain, paper type, deployment status, and research stance, finding continuous growth in publication volume, 77.8% of papers strongly supportive with critical work below 1%, medical imaging dominating the corpus, highly cited primary research concentrated in molecular and pharmaceutical applications, and only 27 publications reporting operational use, thereby revealing a gap between methodological growth and deployment.
This study conducted a systematic mapping and bibliometric analysis of 4,143 publications from 2015 to 2025 on AI-generated synthetic data in biomedicine, combining expert annotation with LLM-assisted classification across data modality, medical domain, paper type, deployment status, and research stance, finding continuous growth in publication volume, 77.8% of papers strongly supportive with critical work below 1%, medical imaging dominating the corpus, highly cited primary research concentrated in molecular and pharmaceutical applications, and only 27 publications reporting operational use, thereby revealing a gap between methodological growth and deployment.
This study conducted a systematic mapping and bibliometric analysis of 4,143 publications from 2015 to 2025 on AI-generated synthetic data in biomedicine, combining expert annotation with LLM-assisted classification across data modality, medical domain, paper type, deployment status, and research stance, finding continuous growth in publication volume, 77.8% of papers strongly supportive with critical work below 1%, medical imaging dominating the corpus, highly cited primary research concentrated in molecular and pharmaceutical applications, and only 27 publications reporting operational use, thereby revealing a gap between methodological growth and deployment.
This study conducted a systematic mapping and bibliometric analysis of 4,143 publications from 2015 to 2025 on AI-generated synthetic data in biomedicine, combining expert annotation with LLM-assisted classification across data modality, medical domain, paper type, deployment status, and research stance, finding continuous growth in publication volume, 77.8% of papers strongly supportive with critical work below 1%, medical imaging dominating the corpus, highly cited primary research concentrated in molecular and pharmaceutical applications, and only 27 publications reporting operational use, thereby revealing a gap between methodological growth and deployment.