ReviewPLoS computational biology2024
Credibility assessment of in silico clinical trials for medical devices.
Review in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
30 citing papers in PubMed.
- In Silico Clinical Trials in Drug Development: Virtual Patients, Applications, and Regulatory Convergence.Clinical pharmacology and therapeutics · 2026Review
- Bridging the AI chasm in oncology: a standardized platform to enable the in silico clinical validation of AI models within the CHAIMELEON Project.European radiology experimental · 2026Article
- Ensuring the quality of in silico evidence: application to medical devices.Briefings in bioinformatics · 2026Article
- Assessing the importance of sex and disease-specific anatomy in electrophysiology and mechanical simulations with a newly developed public virtual cohort of four-chamber heart models.PLoS computational biology · 2026Article
- Review
- Understanding the mechanisms of TAVI durability through computational modelling: a multidisciplinary review.European heart journal. Digital health · 2026Review
- Finite Element Modeling of the Scaphoid Shift Maneuver: Implications for Scapholunate Ligament injuries.bioRxiv : the preprint server for biology · 2026Article
- Advancing neuroengineering with Neuromorphic Twins.Nature communications · 2026Review
- Article
- Computational modelling for personalized transcatheter aortic valve replacement planning: a systematic review of complications and decision support.Frontiers in digital health · 2026Review
- Clinical In Silico Studies in Drug Development and Therapy: Simplified Guidance for Authors, Reviewers and Editors.Drug design, development and therapy · 2026Article
- Airway quantifications of bronchitis patients with photon-counting and energy-integrating computed tomography.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- AI-powered in silico twins: redefining precision medicine through simulation, personalization, and predictive healthcare.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2025Review
- A Step-by-Step Workflow for Performing In Silico Clinical Trials With Nonlinear Mixed Effects Models.CPT: pharmacometrics & systems pharmacology · 2025Article
- Pre-Clinical Models of Heart Failure with Preserved Ejection Fraction: Advancing Knowledge for Device Based Therapies.Annals of biomedical engineering · 2025Review
- Recommended methodologies for clinical investigations of high-risk medical devices-Conclusions from the European Union CORE-MD Project.The Lancet regional health. Europe · 2025Review
- In Silico Forensic Toxicology: Is It Feasible?Toxics · 2025Review
- Container applications for the development and integration of virtual imaging platforms.Medical physics · 2025Article
- Translation of Animal Study to Human: In Silico Based Development of Implantable Pulmonary Artery Pressure Sensor.International journal for numerical methods in biomedical engineering · 2025Article
- An open source statistical web application for validation and analysis of virtual cohorts.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In silico clinical trials (ISCTs) are an emerging method in modeling and simulation where medical interventions are evaluated using computational models of patients. ISCTs have the potential to provide cost-effective, time-efficient, and ethically favorable alternatives for evaluating the safety and effectiveness of medical devices. However, ensuring the credibility of ISCT results is a significant challenge. This paper aims to identify unique considerations for assessing the credibility of ISCTs and proposes an ISCT credibility assessment workflow based on recently published model assessment frameworks. First, we review various ISCTs described in the literature, carefully selected to showcase the range of methodological options available. These studies cover a wide variety of devices, reasons for conducting ISCTs, patient model generation approaches including subject-specific versus 'synthetic' virtual patients, complexity levels of devices and patient models, incorporation of clinician or clinical outcome models, and methods for integrating ISCT results with real-world clinical trials. We next discuss how verification, validation, and uncertainty quantification apply to ISCTs, considering the range of ISCT approaches identified. Based on our analysis, we then present a hierarchical workflow for assessing ISCT credibility, using a general credibility assessment framework recently published by the FDA's Center for Devices and Radiological Health. Overall, this work aims to promote standardization in ISCTs and contribute to the wider adoption and acceptance of ISCTs as a reliable tool for evaluating medical devices.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.