Citation

Fahad, Nafiz and Rabbi, Riadul Islam and Benta Hasan, Sumayea and Sultana Prity, Fariya and Ahmed, Rasel and Ahmed, Farhana and Hossen, Md. Jakir and Liew, Tze Hui and Sayeed, Md. Shohel and Goh, Kah Ong Michael (2025) Generative AI in clinical (2020–2025): a mini-review of applications, emerging trends, and clinical challenges. Frontiers in Digital Health, 7. ISSN 2673-253X

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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.

Item Type:	Article
Uncontrolled Keywords:	diffusion models, electronic-health-record, GANs; generative AI, Vision-Language Models
Subjects:	R Medicine > R Medicine (General) > R855-855.5 Medical technology
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Nor Afiqah Mohd Adnan
Date Deposited:	10 Dec 2025 03:07
Last Modified:	13 Dec 2025 03:53
URII:	http://shdl.mmu.edu.my/id/eprint/15017

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