The editorial, "Dynamics-driven medical big data mining: dynamic approaches to early disease forecasting and individualized care," published in Intelligent Medicine (February 2026, Volume 6, Issue 1), was written by Lu Wang (Tianjin Medical University), Han Lyu (Beijing Friendship Hospital, Capital Medical University), and Bin Sheng (Shanghai Jiao Tong University). It argues that the future of medical AI lies not only in diagnosing disease once it is visible, but in detecting the early dynamic changes that happen before symptoms fully appear. By analyzing how health data evolve over time, from omics and medical records to imaging and wearable devices, AI may help identify “tipping points” when the body is moving toward disease. The authors also stress that these systems must be rigorously validated and used to support, not replace, clinical judgment.
From population averages to individual tipping points
At the heart of this framework is dynamic network biomarker (DNB) theory, which detects impending disease transitions by monitoring sharp rises in fluctuations and correlations within biomolecular networks. Prior work summarized in the editorial has validated DNB-based approaches across two clinically important scenarios: flagging heightened gene-expression instability in influenza infection days before symptoms appear, and identifying genomic tipping points where cells shift from benign to malignant states, with tumor progression prediction accuracies exceeding 80%.
For busy clinicians, the most immediately relevant advance may be individual-specific edge-network analysis (iENA), which transforms molecular data into edge networks and assesses critical transitions using a single patient's own longitudinal data, without requiring a control group. In transcriptomic applications, this single-sample approach has achieved area-under-the-curve (AUC) values greater than 0.9, bringing real-time, bedside-applicable dynamic assessment within reach for the first time in this class of methods.
Hybrid AI narrows the gap between models and patients
The editorial also presents evidence that combining mechanistic physiological knowledge with deep learning, rather than relying on data-driven models alone, substantially improves clinical utility. In type 1 diabetes management, physiology-informed long short-term memory (LSTM) networks reduced mean absolute error in blood-glucose prediction to 35.0 mg/dL, compared with 79.7 mg/dL for traditional simulators, achieving a reduction of more than 55%. These models create patient-specific digital twins that can be used to test therapeutic strategies in silico before clinical application.
Beyond metabolic disease, the editorial describes parallel advances across data modalities: temporal graph neural networks applied to EHRs improved diagnosis prediction accuracy by 10–15% on the MIMIC-III dataset; dynamic graph models derived from functional MRI predicted treatment outcomes in tinnitus; and Transformer-based architectures trained on longitudinal EHRs have shown capacity to forecast multi-disease risks, including diabetes and hypertension, through hierarchical attention mechanisms.
Augmenting, not replacing, clinical judgment
"These dynamics-driven approaches are designed to augment, not replace, clinical expertise," said Professor Bin Sheng, corresponding author and professor at the School of Computer Science, Shanghai Jiao Tong University. "They provide timely early-warning signals that empower proactive intervention, moving medicine from reactive treatment to genuine prevention, while preserving the irreplaceable role of human judgment in complex medical decision-making."
Current limitations demand careful deployment
The editorial is equally direct about the challenges that must be resolved before these tools can deliver equitable, real-world benefits. Data heterogeneity and missing values can produce false positives in critical transition detection, inflating network fluctuations in ways that generate erroneous alerts. A more fundamental challenge is that current methods excel at identifying statistical associations but cannot reliably distinguish correlation from causation without incorporating medical domain knowledge and experimental validation. Interpretability remains a significant barrier: although tools such as SHAP and LIME provide partial explanations for model decisions, full transparency in deep architectures is yet to be achieved, and opaque predictions risk eroding the clinical trust that adoption requires.
Ethical and regulatory concerns also demand attention. Privacy risks persist in federated learning despite distributed training architectures, and algorithmic bias is a particular concern when models trained on specific populations are deployed in underrepresented groups, with the potential to widen rather than narrow healthcare inequalities.
The path forward: multimodal integration and prospective validation
Looking ahead, the editorial identifies two priorities. The first is multimodal integration: fusing omics, imaging, EHR, and wearable data through advanced Transformers, graph neural networks, and causal inference methods, including instrumental variables and counterfactual simulations, to construct comprehensive, causal models of individual disease trajectories. The second, and arguably more critical, is rigorous prospective validation. The authors stress that the gap between theoretical promise and clinical implementation can only be closed through well-designed prospective clinical trials and real-world deployment studies across diverse populations and healthcare settings.
Published as open access, the editorial serves as both a state-of-the-field reference and a practical roadmap for clinicians, researchers, and healthcare leaders working at the intersection of medicine and artificial intelligence.
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Reference
DOI: 10.1016/j.imed.2025.10.001
About the Corresponding Author
Professor Bin Sheng received his Ph.D. in Computer Science and Engineering from The Chinese University of Hong Kong in 2011. He currently serves as a full professor at the School of Computer Science of Shanghai Jiao Tong University. His research focuses on virtual reality, computer graphics, and medical artificial intelligence. Sheng has published extensively in leading journals, including JAMA, Nature Medicine, The Lancet Digital Health, and IEEE Transactions on Pattern Analysis and Machine Intelligence. He is the Managing Editor of The Visual Computer and has co-chaired multiple international conferences and AI challenges.
About the Journal
Intelligent Medicine is a peer-reviewed, open-access journal focusing on the integration of artificial intelligence, data science, and digital technology in clinical medicine and public health. It is published by the Chinese Medical Association in partnership with Elsevier. To learn more about Intelligent Medicine, please visit: https://www.sciencedirect.com/journal/intelligent-medicine
Funding information
This work was supported by the Youth Fund of the National Natural Science Foundation of China (Grant No. 32300519, 62522119, and T2525004).
Intelligent Medicine
Commentary/editorial
Not applicable
Dynamics-driven medical big data mining: dynamic approaches to early disease forecasting and individualized care
26-Feb-2026
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.