AUTHOR=Fahad Nafiz , Rabbi Riadul Islam , Benta Hasan Sumayea , Sultana Prity Fariya , Ahmed Rasel , Ahmed Farhana , Hossen Md. Jakir , Liew Tze Hui , Sayeed Md Shohel , Ong Michael Goh Kah 

TITLE=Generative AI in clinical (2020–2025): a mini-review of applications, emerging trends, and clinical challenges

JOURNAL=Frontiers in Digital Health

VOLUME=Volume 7 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2025.1653369

DOI=10.3389/fdgth.2025.1653369

ISSN=2673-253X

ABSTRACT=Generative artificial intelligence (G-AI) has moved from proof-of-concept demonstrations to practical tools that augment radiology, dermatology, genetics, drug discovery, and electronic-health-record analysis. This mini-review synthesizes fifteen studies published between 2020 and 2025 that collectively illustrate three dominant trends: data augmentation for imbalanced or privacy-restricted datasets, automation of expert-intensive tasks such as radiology reporting, and generation of new biomedical knowledge ranging from molecular scaffolds to fairness insights. Image-centric work still dominates, with GANs, diffusion models, and Vision-Language Models expanding limited datasets and accelerating diagnosis. Yet narrative (EHR) and molecular design domains are rapidly catching up. Despite demonstrated accuracy gains, recurring challenges persist: synthetic samples may overlook rare pathologies, large multimodal systems can hallucinate clinical facts, and demographic biases can be amplified. Robust validation, interpretability techniques, and governance frameworks therefore, remain essential before G-AI can be safely embedded in routine care.