MINI REVIEW article
Front. Digit. Health
Sec. Health Informatics
Volume 7 - 2025 | doi: 10.3389/fdgth.2025.1653369
This article is part of the Research TopicAI in Healthcare: Transforming Clinical Risk Prediction, Medical Large Language Models, and BeyondView all 10 articles
Generative Artificial Intelligence in Clinical Practice: A Mini-Review of Applications, Benefits, and Limitations (2020 – 2025)
Provisionally accepted
Nafiz Fahad
Sumayea Benta Hasan
Rasel Ahmed
Riyadul Islam Rabbi
Farhana AhmedMd Jakir Hossen*
Tze Hui Liew- Multimedia University, Cyberjaya, Malaysia
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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.
Keywords: Generative artificial intelligence, Generative AI, Synthetic samples, rare pathologies, large multimodal systems, hallucinate clinical facts
Received: 24 Jun 2025; Accepted: 30 Sep 2025.
Copyright: © 2025 Fahad, Benta Hasan, Ahmed, Islam Rabbi, Ahmed, Hossen and Liew. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Md Jakir Hossen, jakir.hossen@mmu.edu.my
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