Treffer: Regulatory Adoption of AI, ML, Computational Modeling & Simulation in In-Silico Clinical Trials for Medical Devices: A Systematic Review.
Title:
Regulatory Adoption of AI, ML, Computational Modeling & Simulation in In-Silico Clinical Trials for Medical Devices: A Systematic Review.
Authors:
Source:
Therapeutic innovation & regulatory science [Ther Innov Regul Sci] 2026 Jan; Vol. 60 (1), pp. 45-62. Date of Electronic Publication: 2025 Oct 07.
Publication Type:
Journal Article; Systematic Review
Language:
English
Journal Info:
Publisher: Springer International Publishing Country of Publication: Switzerland NLM ID: 101597411 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2168-4804 (Electronic) Linking ISSN: 21684790 NLM ISO Abbreviation: Ther Innov Regul Sci Subsets: MEDLINE
Imprint Name(s):
Publication: 2020- : Cham, Switzerland : Springer International Publishing
Original Publication: Thousand Oaks, CA : Sage Publications, [2013]-
Original Publication: Thousand Oaks, CA : Sage Publications, [2013]-
MeSH Terms:
References:
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Pappalardo F, Russo G, Tshinanu FM, Viceconti M. In silico clinical trials: concepts and early adoptions. Brief Bioinform. 2019;20(5):1699–708. https://doi.org/10.1093/bib/bby043 . (PMID: 10.1093/bib/bby04329868882)
Scuoppo R, Catalano C, Crascì F, Pasta S. Credibility assessment of the patient-specific modeling of the aneurysmal ascending thoracic aorta: verification, validation and uncertainty quantification. Cardiovasc Eng Tech (2025). https://doi.org/10.1007/s13239-025-00801-1. (PMID: 10.1007/s13239-025-00801-1)
Mehmood K, Arshad M, Lazoglu I, Küçükaksu DS, Bakuy V. In-silico hemodynamic ramp testing of ventricular assist device implanted patients using acausal cardiovascular-VAD modeling. Artif Organs. 2023;47(9):1452–63. https://doi.org/10.1111/aor.14597 . (PMID: 10.1111/aor.1459737306082)
Abadi E, Barufaldi B, Lago M, et al. Toward widespread use of virtual trials in medical imaging innovation and regulatory science. Med Phys. 2024;51(12):9394–404. https://doi.org/10.1002/mp.17442 . (PMID: 10.1002/mp.1744239369717)
Viceconti M, Juárez MA, Curreli C, Pennisi M, Russo G, Pappalardo F. Credibility of in silico trial technologies—a theoretical framing. IEEE J Biomed Health Inform. 2020;24(1):4–13. https://doi.org/10.1109/JBHI.2019.2949888 . (PMID: 10.1109/JBHI.2019.294988831670687)
Roesler D, Czypionka T, Eisenberg S, Kraus M, Reiss M, Zech C. Impact of in-silico technologies in the development of high-risk medical devices on healthcare. Eur J Public Health. 2024;34:44.1210. https://doi.org/10.1093/eurpub/ckae144.1210 . (PMID: 10.1093/eurpub/ckae144.1210)
Simalatsar A. Synthetic biomedical data generation in support of In silico clinical trials. Front Big Data. 2023. https://doi.org/10.3389/fdata.2023.1085571 . (PMID: 10.3389/fdata.2023.10855713765511310466133)
Jiang Z, Abbas H, Jang KJ, et al. In-silico pre-clinical trials for implantable cardioverter defibrillators. In: 2016 38th Annual international conference of the IEEE engineering in medicine and biology society (EMBC); 2016. pp. 169–172. https://doi.org/10.1109/EMBC.2016.7590667 .
Viceconti M, Cobelli C, Haddad T, Himes A, Kovatchev B, Palmer M. In silico assessment of biomedical products: the conundrum of rare but not so rare events in two case studies. Proc Inst Mech Eng [H]. 2017;231(5):455–66. https://doi.org/10.1177/0954411917702931 . (PMID: 10.1177/0954411917702931)
Faris O, Shuren J. An FDA viewpoint on unique considerations for medical-device clinical trials. New Engl J Med. 2017;376(14):1350–7. https://doi.org/10.1056/NEJMra1512592 . (PMID: 10.1056/NEJMra151259228379790)
Mirinejad H, Parvinian B, Ricks M, et al. Evaluation of fluid resuscitation control algorithms via a hardware-in-the-loop test bed. IEEE Trans Biomed Eng. 2020;67(2):471–81. https://doi.org/10.1109/TBME.2019.2915526 . (PMID: 10.1109/TBME.2019.291552631071014)
Badano A, Badal A, Glick S, Graff CG, Samuelson F, Sharma D, et al. In silico imaging clinical trials for regulatory evaluation: initial considerations for VICTRE, a demonstration study. In: Medical Imaging 2017: Physics of Medical Imaging. SPIE; 2017. pp. 494–9. https://doi.org/10.1117/12.2255746 .
Eppich WJ, Gormley GJ, Teunissen PW. In-depth interviews. In: Nestel D, Hui J, Kunkler K, Scerbo MW, Calhoun AW, editors. Healthcare Simulation research: a practical guide. New York: Springer International Publishing; 2019. p. 85–91. (PMID: 10.1007/978-3-030-26837-4_12)
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Contributed Indexing:
Keywords: Artificial intelligence; Computer modelling and simulation; EMA; In-silico clinical trials; Medical device; PMDA; USFDA
Entry Date(s):
Date Created: 20251007 Date Completed: 20251230 Latest Revision: 20260112
Update Code:
20260112
DOI:
10.1007/s43441-025-00871-2
PMID:
41055689
Database:
MEDLINE
Weitere Informationen
Objective: This study explores the revolutionary potential of in-silico clinical trials (ISCTs) in medical device development, emphasizing the integration of computational modeling and simulation (CM&S), artificial intelligence (AI), and machine learning (ML). It evaluates regulatory advancements by the FDA, EMA, and PMDA, identifies barriers to global ISCTs adoption, and proposes strategies to enhance credibility, standardization, and ethical alignment.
Methods: A systematic review following PRISMA 2020 guidelines reviewed 72 studies (2014-2025) from Scopus, PubMed, Web of Science, and regulatory reports. Excluding non-regulatory or non-medical device research, inclusion criteria emphasized ISCTs technologies and regulatory frameworks.
Result: ISCTs employ CM&S techniques, including finite element analysis, computational fluid dynamics, and agent-based modeling, to simulate medical device performance and generate synthetic patient cohorts, thereby reducing costs and addressing ethical concerns. AI/ML further enhances predictive accuracy and optimizes trial design. Regulatory agencies have developed advanced frameworks like the FDA's model credibility and AI guidelines, the EMA promotes its 3R Guidelines, and the PMDA supports computational validation through dedicated subcommittees. Key challenges include regulatory fragmentation, limited data accessibility, computational complexity, and ethical risks such as algorithmic bias. Proposed solutions include global harmonization of regulatory guidelines, explainable AI implementation, federated learning adoption for secure data collaboration, and hybrid trial designs that integrate ISCTs with traditional methodologies.
Conclusion: ISCTs can revolutionize the development and assessment of medical devices. Standardized validation frameworks, regulatory standards, and interdisciplinary cooperation are required to address these issues. Clear guidelines must ensure ISCTs legitimacy and acceptance and promote safer and ethical medical innovations.
(© 2025. The Author(s), under exclusive licence to The Drug Information Association, Inc.)
Declarations. Competing interests: The authors declare no competing interests. Ethics Approval and Consent to Participate: Not applicable, as this study is based on a systematic review of published literature and does not involve human or animal participants. Informed Consent: Not applicable.