Regulatory Adoption of AI, ML, Computational Modeling & Simulation in In-Silico Clinical Trials for Medical Devices: A Systematic Review
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 FD...
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| Vydáno v: | Therapeutic innovation & regulatory science |
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07.10.2025
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| Abstract | 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.
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.
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.
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. |
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| AbstractList | 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.
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.
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.
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. 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.OBJECTIVEThis 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.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.METHODSA 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.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.RESULTISCTs 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.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.CONCLUSIONISCTs 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. |
| Author | Lohani, Alka De, Arindam |
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| Cites_doi | 10.21275/ART20203995 10.1093/eurheartj/ehad784 10.1088/1742-6596/1679/2/022057 10.1080/10255842.2018.1545017 10.1001/jamanetworkopen.2022.31609 10.1136/bmj.n71 10.1016/j.heares.2023.108744 10.1117/12.2255746 10.1007/s13311-023-01384-2 10.1007/978-3-030-17971-7_72 10.1007/s11095-017-2215-2 10.1016/j.cmpb.2023.107739 10.1038/s41467-021-23998-w 10.1093/bib/bby043 10.1147/rd.363.0329 10.1109/EMBC.2016.7590667 10.1016/j.cmpb.2023.107735 10.1038/s41598-018-25229-7 10.1016/j.jbiomech.2014.12.019 10.1007/s12553-019-00386-5 10.1111/aor.14597 10.1109/FMEC.2019.8795362 10.1088/2516-1091/ad04c0 10.1109/TBME.2019.2915526 10.48550/arXiv.2209.09023 10.1007/s11517-024-03215-8 10.3389/fdata.2023.1085571 10.1098/rsos.172096 10.1146/annurev-publhealth-040617-014317 10.1016/j.drudis.2025.104322 10.1007/s12553-023-00738-2 10.17219/dmp/142447 10.1038/s41746-025-01679-y 10.3390/biomimetics5030044 10.1073/pnas.082080899 10.1007/s13239-025-00801-1 10.1007/978-3-030-76951-2_10 10.3389/fphy.2020.593111 10.1371/journal.pone.0184216 10.1057/jos.2016.7 10.1007/978-3-030-26837-4_12 10.1080/02713683.2016.1175019 10.1109/JBHI.2019.2949888 10.1111/cts.13897 10.1016/S0140-6736(22)02243-7 10.1093/eurpub/ckae144.1210 10.1056/NEJMra1512592 10.1002/psp4.12868 10.1002/mp.17442 10.1109/COMPSAC.2017.164 10.1007/978-1-0716-0368-0_13 10.1016/j.ymeth.2020.01.011 10.1016/j.cmpb.2023.107525 10.1177/0954411917702931 10.1109/WSC.2014.7019874 10.1002/pds.4882 |
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| References | P Favre (871_CR71) 2023; 241 F Crea (871_CR69) 2023; 44 A Simalatsar (871_CR10) 2023 E Sulis (871_CR32) 2023; 236 G Wiederhold (871_CR37) 1992; 36 871_CR38 871_CR39 M Calder (871_CR17) 2018; 5 S Kijima (871_CR62) 2022; 11 F Pappalardo (871_CR4) 2019; 20 JB Fink (871_CR27) 2017; 34 S Li (871_CR35) 2019; 22 MI Miller (871_CR40) 2023; 20 E Bonabeau (871_CR29) 2002; 99 R Colasanti (871_CR34) 2022; 400 WJ Eppich (871_CR16) 2019 MJ Page (871_CR3) 2021; 372 CM Macal (871_CR30) 2016; 10 M Wang (871_CR21) 2018; 8 E Zdravevski (871_CR46) 2017; 12 871_CR28 L Spahić (871_CR45) 2020 A Badano (871_CR51) 2023; 5 Ž Kovačević (871_CR44) 2020; 10 AA Vidovszky (871_CR50) 2024; 17 MM Bhatti (871_CR24) 2020 M Viceconti (871_CR53) 2021; 185 M Viceconti (871_CR8) 2020; 24 T Leclère (871_CR18) 2023; 432 R Scuoppo (871_CR47) 2025; 63 N Kladovasilakis (871_CR22) 2020; 5 A Corti (871_CR36) 2023; 241 E Abadi (871_CR7) 2024; 51 871_CR60 871_CR61 871_CR63 871_CR64 871_CR65 871_CR66 871_CR67 871_CR68 G Cafri (871_CR42) 2019; 28 871_CR15 871_CR59 B Mahesh (871_CR43) 2020; 9 D Roesler (871_CR9) 2024; 34 IA Magomedov (871_CR19) 2020; 1679 M Taylor (871_CR20) 2015; 48 F Castiglione (871_CR33) 2020 VK Dommeti (871_CR23) 2023; 60 M Tracy (871_CR31) 2018; 39 S Askin (871_CR70) 2023; 13 D Roesler (871_CR49) 2024; 34 871_CR1 A Sertkaya (871_CR2) 2022; 5 L Reid (871_CR25) 2021 K Mehmood (871_CR6) 2023; 47 BP Kovatchev (871_CR52) 2025; 8 M Viceconti (871_CR12) 2017; 231 871_CR54 871_CR11 871_CR55 871_CR56 O Faris (871_CR13) 2017; 376 871_CR57 871_CR58 871_CR48 M Caixinha (871_CR41) 2017; 42 R Scuoppo (871_CR5) 2025 H Mirinejad (871_CR14) 2020; 67 A Sarrami-Foroushani (871_CR26) 2021; 12 K Wang (871_CR72) 2025; 30 |
| References_xml | – volume: 9 start-page: 381 issue: 1 year: 2020 ident: 871_CR43 publication-title: IJSR doi: 10.21275/ART20203995 – ident: 871_CR54 – volume: 44 start-page: 4815 issue: 46 year: 2023 ident: 871_CR69 publication-title: Eur Heart J doi: 10.1093/eurheartj/ehad784 – volume: 1679 issue: 2 year: 2020 ident: 871_CR19 publication-title: J Phys: Conf Ser doi: 10.1088/1742-6596/1679/2/022057 – volume: 22 start-page: 217 issue: 2 year: 2019 ident: 871_CR35 publication-title: Comput Methods Biomech Biomed Eng doi: 10.1080/10255842.2018.1545017 – volume: 5 issue: 9 year: 2022 ident: 871_CR2 publication-title: JAMA Netw Open doi: 10.1001/jamanetworkopen.2022.31609 – volume: 372 year: 2021 ident: 871_CR3 publication-title: BMJ doi: 10.1136/bmj.n71 – volume: 432 year: 2023 ident: 871_CR18 publication-title: Hear Res doi: 10.1016/j.heares.2023.108744 – ident: 871_CR15 doi: 10.1117/12.2255746 – volume: 20 start-page: 1066 issue: 4 year: 2023 ident: 871_CR40 publication-title: Neurotherapeutics doi: 10.1007/s13311-023-01384-2 – ident: 871_CR60 – start-page: 483 volume-title: CMBEBIH 2019 year: 2020 ident: 871_CR45 doi: 10.1007/978-3-030-17971-7_72 – volume: 34 start-page: 2568 issue: 12 year: 2017 ident: 871_CR27 publication-title: Pharm Res doi: 10.1007/s11095-017-2215-2 – volume: 241 year: 2023 ident: 871_CR36 publication-title: Comput Methods Programs Biomed doi: 10.1016/j.cmpb.2023.107739 – ident: 871_CR67 – ident: 871_CR63 – volume: 12 start-page: 3861 issue: 1 year: 2021 ident: 871_CR26 publication-title: Nat Commun doi: 10.1038/s41467-021-23998-w – ident: 871_CR58 – volume: 20 start-page: 1699 issue: 5 year: 2019 ident: 871_CR4 publication-title: Brief Bioinform doi: 10.1093/bib/bby043 – volume: 36 start-page: 329 issue: 3 year: 1992 ident: 871_CR37 publication-title: IBM J Res Dev doi: 10.1147/rd.363.0329 – ident: 871_CR11 doi: 10.1109/EMBC.2016.7590667 – volume: 241 year: 2023 ident: 871_CR71 publication-title: Comput Methods Programs Biomed doi: 10.1016/j.cmpb.2023.107735 – volume: 8 issue: 1 year: 2018 ident: 871_CR21 publication-title: Sci Rep doi: 10.1038/s41598-018-25229-7 – volume: 48 start-page: 767 issue: 5 year: 2015 ident: 871_CR20 publication-title: J Biomech doi: 10.1016/j.jbiomech.2014.12.019 – volume: 10 start-page: 151 issue: 1 year: 2020 ident: 871_CR44 publication-title: Health Technol doi: 10.1007/s12553-019-00386-5 – volume: 47 start-page: 1452 issue: 9 year: 2023 ident: 871_CR6 publication-title: Artif Organs doi: 10.1111/aor.14597 – ident: 871_CR39 doi: 10.1109/FMEC.2019.8795362 – volume: 5 issue: 4 year: 2023 ident: 871_CR51 publication-title: Prog Biomed Eng doi: 10.1088/2516-1091/ad04c0 – volume: 67 start-page: 471 issue: 2 year: 2020 ident: 871_CR14 publication-title: IEEE Trans Biomed Eng doi: 10.1109/TBME.2019.2915526 – ident: 871_CR48 doi: 10.48550/arXiv.2209.09023 – ident: 871_CR55 – volume: 63 start-page: 467 issue: 2 year: 2025 ident: 871_CR47 publication-title: Med Biol Eng Comput doi: 10.1007/s11517-024-03215-8 – year: 2023 ident: 871_CR10 publication-title: Front Big Data doi: 10.3389/fdata.2023.1085571 – volume: 5 issue: 6 year: 2018 ident: 871_CR17 publication-title: R Soc Open Sci doi: 10.1098/rsos.172096 – volume: 39 start-page: 77 year: 2018 ident: 871_CR31 publication-title: Annu Rev Public Health doi: 10.1146/annurev-publhealth-040617-014317 – ident: 871_CR66 – volume: 30 issue: 3 year: 2025 ident: 871_CR72 publication-title: Drug Discov Today doi: 10.1016/j.drudis.2025.104322 – volume: 13 start-page: 203 issue: 2 year: 2023 ident: 871_CR70 publication-title: Health Technol doi: 10.1007/s12553-023-00738-2 – ident: 871_CR59 – volume: 60 start-page: 385 issue: 3 year: 2023 ident: 871_CR23 publication-title: Dent Med Probl doi: 10.17219/dmp/142447 – volume: 8 issue: 1 year: 2025 ident: 871_CR52 publication-title: NPJ Digit Med doi: 10.1038/s41746-025-01679-y – volume: 5 start-page: 44 issue: 3 year: 2020 ident: 871_CR22 publication-title: Biomimetics doi: 10.3390/biomimetics5030044 – volume: 99 start-page: 7280 issue: suppl_3 year: 2002 ident: 871_CR29 publication-title: Proc Natl Acad Sci U S A doi: 10.1073/pnas.082080899 – year: 2025 ident: 871_CR5 publication-title: Cardiovasc Eng Tech doi: 10.1007/s13239-025-00801-1 – ident: 871_CR1 – ident: 871_CR56 – start-page: 205 volume-title: Biomedical visualisation year: 2021 ident: 871_CR25 doi: 10.1007/978-3-030-76951-2_10 – year: 2020 ident: 871_CR24 publication-title: Front Phys doi: 10.3389/fphy.2020.593111 – volume: 12 issue: 9 year: 2017 ident: 871_CR46 publication-title: PLoS ONE doi: 10.1371/journal.pone.0184216 – volume: 10 start-page: 144 issue: 2 year: 2016 ident: 871_CR30 publication-title: J Simul doi: 10.1057/jos.2016.7 – start-page: 85 volume-title: Healthcare Simulation research: a practical guide year: 2019 ident: 871_CR16 doi: 10.1007/978-3-030-26837-4_12 – volume: 42 start-page: 1 issue: 1 year: 2017 ident: 871_CR41 publication-title: Curr Eye Res doi: 10.1080/02713683.2016.1175019 – ident: 871_CR65 – volume: 24 start-page: 4 issue: 1 year: 2020 ident: 871_CR8 publication-title: IEEE J Biomed Health Inform doi: 10.1109/JBHI.2019.2949888 – ident: 871_CR61 – volume: 17 issue: 7 year: 2024 ident: 871_CR50 publication-title: Clin Transl Sci doi: 10.1111/cts.13897 – volume: 400 start-page: S33 year: 2022 ident: 871_CR34 publication-title: Lancet doi: 10.1016/S0140-6736(22)02243-7 – volume: 34 start-page: 44.1210 year: 2024 ident: 871_CR9 publication-title: Eur J Public Health doi: 10.1093/eurpub/ckae144.1210 – volume: 376 start-page: 1350 issue: 14 year: 2017 ident: 871_CR13 publication-title: New Engl J Med. doi: 10.1056/NEJMra1512592 – volume: 11 start-page: 1552 issue: 12 year: 2022 ident: 871_CR62 publication-title: CPT Pharmacomet Syst Pharmacol doi: 10.1002/psp4.12868 – volume: 51 start-page: 9394 issue: 12 year: 2024 ident: 871_CR7 publication-title: Med Phys doi: 10.1002/mp.17442 – ident: 871_CR38 doi: 10.1109/COMPSAC.2017.164 – start-page: 661 volume-title: Complex social and behavioral systems : game theory and agent-based models year: 2020 ident: 871_CR33 doi: 10.1007/978-1-0716-0368-0_13 – ident: 871_CR68 – volume: 185 start-page: 120 year: 2021 ident: 871_CR53 publication-title: Methods doi: 10.1016/j.ymeth.2020.01.011 – volume: 34 issue: Supplement_3 year: 2024 ident: 871_CR49 publication-title: Eur J Public Health doi: 10.1093/eurpub/ckae144.1210 – volume: 236 year: 2023 ident: 871_CR32 publication-title: Comput Methods Programs Biomed doi: 10.1016/j.cmpb.2023.107525 – ident: 871_CR64 – volume: 231 start-page: 455 issue: 5 year: 2017 ident: 871_CR12 publication-title: Proc Inst Mech Eng [H] doi: 10.1177/0954411917702931 – ident: 871_CR28 doi: 10.1109/WSC.2014.7019874 – ident: 871_CR57 – volume: 28 start-page: 1440 issue: 11 year: 2019 ident: 871_CR42 publication-title: Pharmacoepidemiol Drug Saf doi: 10.1002/pds.4882 |
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