Several challenges, including the need for early prediction of patient trajectories, the complexity of the analytical model

The gap

Evidence profile

Sourced from the stated challenges and inline gaps and recommendations and future work and future-work section of the source papers, classified as general, drawn from work published between 2025 and 2026, spanning 5 journals. Those papers have been cited 15 times in total.

Supporting evidence — 6 representative gaps

• Acute hypoxemic respiratory failure: from static severity to trajectory-based phenotyping (2026) · Intensive Care Medicine · cited 1× · doi

  The paper identifies several challenges, including the need for early prediction of patient trajectories, the complexity of the analytical model, and the potential for false positives and self-fulfilling prognostic bias. The authors note that the 72-hour prediction window may lag the therapeutic window, and that the classification may occur too late to be of therapeutic benefit. The study highlights the need for external validation and prospective testing of the prediction model in different settings.

  generalstated challenges
  Keywords: paper identifies several challenges including need early prediction
• Machine learning model for predicting hypotension following continuous renal replacement therapy initiation in end-stage kidney disease patients: a SHAP-interpretable approach (2026) · Frontiers in Medicine · doi

  Future work may focus on embedding the model into clinical informatics platforms to enable the real-time extraction of baseline characteristics, laboratory data, and treatment parameters, thereby facilitating automated risk stratification and early warning. Clinically, heightened vigilance is warranted in such patients, and prompt identification and management of the underlying primary disease should be prioritized. Future multicenter, large-scale cohort studies are warranted to further validate and enhance the robustness and applicability of the model.

  generalinline gapsevidence 5/5
  Keywords: future model cation warranted focus embedding clinical informatics platforms enable real time extraction baseline characteristics
• Multilayer network approaches to omics data integration in digital twins for cancer research (2026) · Frontiers in Systems Biology · doi

  increased surveillance. interventions or for therapeutic The effectiveness of DTs depends on their predictive capability (Fuller et al., 2020). Leveraging real-time data and historical health information helps DTs forecast various factors such as disease progression, outcomes of different treatment options, or early signs of health deterioration. This predictive capability is particularly valuable in the management of chronic diseases, influence disease where early progression. DTs can be used not only for optimizing disease treatments, but also to suggest strategies for preventing disease onset and mitigating its impact. Their predictive power comes from modeling disease trajectories, i.e., mapping a disease’s progression over time, including its onset, development and chronicity, when relevant, starting from clinical records, multi- omics data and real-time health metrics. Similar data-driven intervention can significantly they offer approaches have also been applied to predict disease risk and evolution using longitudinal health data and ML (Lasko et al., 2025). Therefore, for medical professionals to proactively adjust treatment plans, and anticipate significant turning points in the disease’s course. They can also be used to provide alerts for early indicators of health deterioration for chronic diseases, whose trajectory frequently includes periods of remission or relapse. the potential Moreover, the DTs framework can help in the creation of a learning healthcare system where continuous feedback from DTs informs clinical decisions and enhances healthcare delivery. This iterative process not only improves individual patient care, but also the broader knowledge base, driving key contributes advancements in medical research and clinical practice (Kamel Boulos and Zhang, 2021). to Potential and existing applications of DTs or DT-like models in healthcare There are three main uses that DTs could have in healthcare, namely, disease management and treatment optimization, virtual clinical trials, and patient education. Personalized medicine and disease management are a few of the areas in which DTs are gaining recognition for their transformative potential in the healthcare industry. Several existing systems integrate multi-omics data with clinical and lifestyle information to create a more complete and individualized health profile that can serve as a basis for in silico disease modeling. These models can form the basis for clinical decision support systems, providing predictions of responses to the different treatment options, a highly valuable tool for clinicians who can tailor interventions based on the unique characteristics of each patient. In addition, DTs enable customized interventions by utilizing real-time data to offer precise and dynamic insights into patient health; this involves continuous mapping of the physical/digital counterparts. Virtual representations play an important role in the prevention and management of chronic disease. The constant monitoring of physiological data allows DTs to identify early signs of disease onset but also aggravation, which is crucial for timely intervention. In the context of diabetes management, DTs have demonstrated the capacity to predict glucose fluctuations by integrating data on dietary intake, physical activity or medication adherence, making them a powerful tool to help clinicians with better management of the disease and preventing complications (Shamanna et al., 2021). Recently, this has led to the design of DT systems that administer insulin through real-time monitoring of patient glucose levels (Thamotharan et al., 2023). Other studies in cardiovascular health demonstrate how DTs can monitor vital or relevant cardiovascular signs (resting heart rate, heart rate variability, blood pressure) to detect abnormalities at an early stage and suggest preventive measures (Coorey et al., 2022). Specifically in oncology, advanced computational frameworks have long been used for modeling tumor growth and predicting how a tumor will respond to different therapeutic interventions (Benzekry et al., 2014), leading to optimization of cancer patient treatment plans by improving drug effectiveness and minimizing side effects (Bruynseels, Santoni de Sio and van den Hoven, 2018). Recent work at the interface of mathematical oncology and AI

  generalrecommendationsevidence 5/5
  Keywords: disease health management clinical patient time treatment early healthcare interventions real predictive progression different signs
• The Holistic Perioperative Management and Recovery Strategies in Lumbar Spine Surgery (2026) · Quality in Sport · doi

  Future research should prioritize the development of predictive, multi-domain algorithms that integrate demographic, physiological, and biopsychosocial recovery trajectories to create highly personalized, patient-specific care plans [32]. By utilizing machine learning to synthesize large, heterogeneous datasets—including preoperative psychological metrics, frailty indices, and real-time data from wearable sensing devices—such models enable clinicians to identify patients at risk of delayed functional recovery or specific complications with greater precision [32, 33]. Furthermore, integrating multi-modal, longitudinal data into these predictive frameworks shifts clinical decision-making beyond static patient-reported outcome measures toward a dynamic, data-driven paradigm [32]. By leveraging these analytical capabilities, multidisciplinary teams can systematically tailor perioperative interventions to an individual’s unique risk profile, thereby maximizing long-term functional independence and holistically optimizing surgical outcomes for an increasingly complex and heterogeneous patient population [32, 33]. Declarations & Statements Author’s contribution Conceptualization: Wiktor Wardyn 10 Software: not applicable; Verification: Laura Magdalena Sikorska Formal analysis:; Magdalena Aleksandra Boniecka Research: Dominika Adrianna Ossowska Resources: Julia Anna Kujawa Writing - rough preparation: Anna Jankowska Writing - review and editing: Wiktor Wardyn Visualization: Klaudia Ścibior Supervision: Wiktor Wardyn Project administration: Aleksandra Kostańska Funding acquisition, not applicable. All authors have read and agreed with the published version of the manuscript.

  generalfuture workevidence 5/5
  Keywords: patient wiktor wardyn predictive multi recovery specific heterogeneous risk functional applicable magdalena aleksandra anna writing
• Risk stratification and scoring systems in upper and lower gastrointestinal bleeding: review of performance and limitations in the emergency department (2025) · Frontiers in Medicine · cited 13× · doi

  Funding With the increasing complexity of clinical presentations and the rising demand for precision medicine, future efforts should focus on dynamic and adaptable risk prediction models. Artificial intelligence-driven tools, capable of integrating real-time data from electronic health records, have the potential to outperform static scoring systems by providing more individualized and context- sensitive predictions. clinical Moreover, the development of hybrid models—combining traditional variables with continuous physiologic monitoring—may enhance early warning capabilities, especially in settings with limited endoscopic access.

  generalfuture workevidence 5/5
  Keywords: clinical models funding increasing complexity presentations rising demand precision medicine future orts focus dynamic adaptable
• A Five-Year Study on Treatment Changes in Hypoglycemia-Associated Medications: Towards Personalized Diabetes Management (2026) · Journal of Personalized Medicine · cited 1× · doi

  Further research on the benefits of integrating advanced risk prediction tools into clinical practices that support more proactive management of T2D is needed. Longitudinal studies with long follow-up periods are important to provide deeper insights into the long-term effects of treatment changes and patient outcomes. Future research should utilize longitudinal modeling approaches to investigate long-term treatment patterns in diabetes management. Studies are required to evaluate personalized treatment strategies based on the risk of hypoglycemia, sex, age, and other patient-specific factors such as diabetes phenotype in various clinical settings.

  generalfuture-work sectionevidence 5/5
  Keywords: further research benefits integrating advanced risk prediction tools

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