Sep 2026· European Journal of Clinical Investigation· Vol 56· 0 citations· 70 references
Medicine
TL;DR
This work states that network medicine (NM), which models biological systems as dynamic, interconnected networks, enables integration of multiomic data and better characterizes disease mechanisms to guide precision therapies when combined with AI.
Abstract
Artificial intelligence (AI) is transforming global health care through innovations in deep learning, generative models and agentic AI systems. Traditional reductionist approaches to complex pathophysiological pathways fail to capture the true complexity of disease, motivating the adoption of network medicine (NM), which models biological systems as dynamic, interconnected networks. When combined with AI, NM enables integration of multiomic data and better characterizes disease mechanisms to guide precision therapies. Nevertheless, the rapid diffusion of AI in health care also raises profound ethical, regulatory and social challenges, since only a small fraction of AI tools have achieved routine clinical use, often due to limited generalizability, opaque algorithms and workflow incompatibility.
AI offers considerable potential to reduce costs, increase diagnostic accuracy, personalize care and expand access to quality services, provided its deployment overcomes technical, ethical and regulatory barriers through proactive, adaptive and patient-centered governance.
Laura Beatriz Fernández Feitó, Mariam Alejandra González González, Andy Alonso Pazos et al.· SAP Social AI· 0 citations
This review explores the current state of AI in medicine, highlighting that current clinically mature and regulatory-approved products are based on deep learning architecture and are predominantly diagnostic.
Ahmad Guni, Wan-Heng Hu, Jess Morley et al.· Frontiers in Science· 1 citation
Artificial Intelligence (AI) is rapidly transforming Medicine, with applications ranging from diagnostic imaging and clinical decision support to generative AI, scientific research, public health surveillance, and personalized care. The integration of machine learning, large language models, and multimodal systems offe...
Roberto Badaró· JPC - Journal of Pediatrics...· 0 citations
Artificial intelligence (AI) is changing the way biomedical research is conducted by making it possible to integrate diverse types of biological data and tackle questions that were previously difficult to address. At the same time, its growing use also raises important questions about data quality, reliability, interpr...
D. Koller, Pei-Long Lu, Laura Cantini et al.· Cell· 0 citations
The reviewed literature indicates that machine learning, deep learning, explainable AI, and human–AI collaboration can improve diagnostic accuracy, efficiency, and patient-centered care, but challenges related to data privacy, algorithmic bias, explainability, cybersecurity, clinical validation, regulation, and unequal...
Nasheeda T. M.· International Journal of Tec...· 0 citations
Evidence is synthesised on agentic AI and LLMs applied to precision medicine, encompassing drug discovery, genomics, oncology, rare disease diagnostics and clinical pharmacology, which examines architectural components, recent validation milestones and emerging challenges.
Fabricio F. Costa· Drug Discovery Today· 0 citations
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