REVIEW article
Front. Digit. Health
Sec. Health Informatics
Volume 7 - 2025 | doi: 10.3389/fdgth.2025.1666415
AI Reshaping Life Sciences: Intelligent Transformation, Application Challenges, and Future Convergence in Neuroscience, Biology, and Medicine
Provisionally accepted
- 1Nantong University, Nantong, China
- 2First People's Hospital of Changshu City, Changshu, China
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The rapid advancement of artificial intelligence (AI) is profoundly transforming research paradigms and clinical practices across neuroscience, biology, and medicine with unprecedented depth and breadth. Leveraging its robust data-processing capabilities, precise pattern recognition techniques, and efficient real-time decision support, AI has catalyzed a paradigm shift toward intelligent, precision-oriented approaches in scientific research and healthcare. This review comprehensively reviews core AI applications within these domains. Within neuroscience, AI advances encompass brain-computer interface (BCI) development/optimization, intelligent analysis of neuroimaging data (e.g., fMRI, EEG), and early prediction/precise diagnosis of neurological disorders. In biological research, AI applications include enhanced gene-editing efficiency (e.g., CRISPR) with off-target effect prediction, genomic big-data interpretation, drug discovery/design (e.g., virtual screening), high-accuracy protein structure prediction (exemplified by AlphaFold), biodiversity monitoring, and ecological conservation strategy optimization. For medical research, AI empowers auxiliary medical image diagnosis (e.g., CT, MRI), pathological analysis, personalized treatment planning, health risk prediction with lifespan health management, and robot-assisted minimally invasive surgery (e.g., da Vinci Surgical System). This review not only synthesizes AI's pivotal role in enhancing research efficiency and overcoming limitations of conventional methodologies, but also critically examines persistent challenges, including data access barriers, algorithmic non-transparency, ethical governance gaps, and talent shortages. Building upon this analysis, we propose a tripartite framework ('Technology-Ethics-Talent') to advance intelligent transformation in scientific and medical domains. Through coordinated implementation, AI will catalyze a transition toward efficient, accessible, and sustainable healthcare, ultimately establishing a life-cycle preservation paradigm encompassing curative gene editing, proactive health management, and ecologically intelligent governance.
Keywords: artificial intelligence, Neuroscience, Biology, Medicine, Cross-disciplinary integration
Received: 15 Jul 2025; Accepted: 04 Sep 2025.
Copyright: © 2025 Gong, Zhao, Niu, Yanan, Sun, Shen, Chen and Wu. 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: Jiahuan Gong, Nantong University, Nantong, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.