Simulation foundations, methods and applications, Journal Year: 2024, Volume and Issue: unknown, P. 249 - 266
Published: Jan. 1, 2024
Language: Английский
Simulation foundations, methods and applications, Journal Year: 2024, Volume and Issue: unknown, P. 249 - 266
Published: Jan. 1, 2024
Language: Английский
npj Digital Medicine, Journal Year: 2025, Volume and Issue: 8(1)
Published: Jan. 17, 2025
Digital twins in precision medicine provide tailored health recommendations by simulating patient-specific trajectories and interventions. We examine the critical role of Verification, Validation, Uncertainty Quantification (VVUQ) for digital ensuring safety efficacy, with examples cardiology oncology. highlight challenges opportunities developing personalized trial methodologies, validation metrics, standardizing VVUQ processes. frameworks are essential integrating into clinical practice.
Language: Английский
Citations
5Robotics and Computer-Integrated Manufacturing, Journal Year: 2025, Volume and Issue: 94, P. 102982 - 102982
Published: Feb. 10, 2025
Language: Английский
Citations
2Information Fusion, Journal Year: 2025, Volume and Issue: unknown, P. 103033 - 103033
Published: Feb. 1, 2025
Language: Английский
Citations
2FEBS Open Bio, Journal Year: 2025, Volume and Issue: unknown
Published: Feb. 24, 2025
Medical digital twins (MDTs) are virtual representations of patients that simulate the biological, physiological, and clinical processes individuals to enable personalized medicine. With increasing complexity omics data, particularly multiomics, there is a growing need for advanced computational frameworks interpret these data effectively. Foundation models (FMs), large‐scale machine learning pretrained on diverse types, have recently emerged as powerful tools improving interpretability decision‐making in precision This review discusses integration FMs into MDT systems, their role enhancing multiomics data. We examine current challenges, recent advancements, future opportunities leveraging analysis MDTs, with focus application
Language: Английский
Citations
1Information Fusion, Journal Year: 2024, Volume and Issue: unknown, P. 102888 - 102888
Published: Dec. 1, 2024
Language: Английский
Citations
6Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown
Published: Jan. 16, 2025
Language: Английский
Citations
0medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown
Published: Jan. 22, 2025
Abstract Artificial intelligence (AI) is increasingly applied to clinical trial risk assessment, aiming improve safety and efficiency. This scoping review analyzes 142 studies published between 2013 2024, focusing on (n=55), efficacy (n=46), operational (n=45) prediction. AI techniques, including traditional machine learning, deep learning (e.g., graph neural networks, transformers), causal are used for tasks like adverse drug event prediction, treatment effect estimation, phase transition These methods utilize diverse data sources, from molecular structures protocols patient scientific publications. Recently, large language models (LLMs) have seen a surge in applications, representing over 20% of 2023. While some achieve high performance (AUROC up 96%), challenges remain, selection bias, limited prospective studies, quality issues. Despite these limitations, AI-based assessment holds substantial promise transforming trials, particularly through improved risk-based monitoring frameworks.
Language: Английский
Citations
0npj Digital Medicine, Journal Year: 2025, Volume and Issue: 8(1)
Published: April 8, 2025
Digital phenotyping collects health data digitally, supporting early disease diagnosis and management. This paper systematically reviews the diversity of research methods in digital its clinical benefits, while also focusing on importance within P4 medicine paradigm core role advancing application biobanks. Furthermore, envisions continued benefits phenotyping, driven by technological innovation, global collaboration, policy support.
Language: Английский
Citations
0JMIR AI, Journal Year: 2025, Volume and Issue: 4, P. e64845 - e64845
Published: May 5, 2025
Abstract Background Insufficient participant enrollment is a major factor responsible for clinical trial failure. Objective We formulated machine learning (ML)–based framework using laboratory parameters to identify participants eligible in bioequivalence study. Methods acquired records of 11,592 patients with gastric cancer from the electronic medical Kyungpook National University Hospital Korea. The ML model was developed 8 parameters, including complete blood count and liver kidney function tests, along dates acquisition. Two datasets were collected: (1) training dataset design an ML-based candidate selection method (2) test evaluate performance proposed method. generalization confirmed F 1 - score area under curve (AUC). compared random its efficacy recruiting participants. Results weighted ensemble achieved strong -score above 0.8 AUC value exceeding 0.8, demonstrating ability accurately valid candidates while minimizing misclassification. Its high sensitivity further enhanced model’s efficiency prioritizing screening. In case study, reduced workload by 57%, efficiently identifying 150 pool 209, 485 required selection. Conclusions can be used trial, enabling faster enrollment.
Language: Английский
Citations
0The Lancet Digital Health, Journal Year: 2025, Volume and Issue: unknown, P. 100851 - 100851
Published: May 1, 2025
Randomised controlled trials are the gold standard to assess effectiveness and safety of clinical interventions; however, many paediatric discontinued early due challenges in patient enrolment. Hence, most suffer from lack adequate power. Additionally, expensive might expose patients unproven therapies. Alternatives overcome these issues using virtual data-namely, digital twins, synthetic data, in-silico trials-are now possible rapid advances health-care tools interventions. However, such innovations have been rarely used trials. In this Viewpoint, we propose data empower The use has advantages decreased exposure children potentially ineffective or risky interventions, shorter trial durations leading more ascertainment faster drug approvals. Use could lead personalised treatment options with low costs result implementation interventions children. ethical regulatory concerns, including replacing humans privacy, security should be addressed sustainability innovation ensured before adopted widely.
Language: Английский
Citations
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