In Silico Trials, Real Impacts!

What Underpins Model Credibility? Three Philosophical Perspectives

How do philosophers of science think about model evaluation? Dive deep into the crucial process of model evaluation, a fundamental step in digital science that determines the effectiveness and precision of computational models.
This episode explores three primary perspectives on model quality: the mirror view, relevant similarity, and fitness for purpose. Learn about the complexities and challenges involved in evaluating models, from data limitations to the intricate balance between accuracy and usability.
Whether you're a researcher, regulator, or a curious listener, discover how these cutting-edge methodologies are reshaping healthcare, improving safety, efficiency, and equitability, one simulated trial at a time. Join us as we delve into the philosophy behind model evaluation and unveil the real-world impact of digital innovation.
Parker WS. Model Evaluation: An Adequacy-for-Purpose View. Philos Sci. 2020;87(3):457-477.
Bokulich A, Parker W. Data models, representation and adequacy-for-purpose. Eur J Philos Sci. 2021;11(31). 
 
 
 
 

Exploring Digital Twins: The Philosophy Behind Healthcare's Digital Revolution

Have you ever wondered what makes a digital twin truly "twin-like" and not just another computer model? This fascinating question lies at the heart of modern healthcare innovation, where virtual copies of real-world systems are revolutionising how we approach medical research and patient care. Our latest episode delves into groundbreaking research that examines the philosophical underpinnings of digital twins, exploring what sets them apart from conventional computational models.
We unpack how digital twins are transforming healthcare through their unique ability to capture complex, emergent behaviours in ways traditional models cannot. From enabling safer drug development through in silico trials to advancing personalised medicine, these sophisticated virtual representations are bridging the gap between computational simulation and real-world applications. Our discussion reveals why digital twins represent more than just technological advancement - they embody a fundamental shift in how we understand and interact with healthcare systems, promising more precise, efficient, and safer medical innovations for the future.
Reference: Wagg DJ, Burr C, Shepherd J, Xuereb Conti Z, Enzer M, Niederer S. The philosophical foundations of digital twinning. Data-Centric Engineering. 2025;6:e12. doi:10.1017/dce.2025.4
 
 
 

Cracking the Code: Unveiling the Trustworthy World of In-Silico Trials

Are computational models ready to replace traditional clinical trials? This episode delves into the fascinating world of in silico trials and their growing role in regulatory evaluation of biomedical products. We explore a methodological framework based on the ASME VV-40-2018 standard that establishes credibility through verification, validation, and uncertainty quantification. From defining contextual use to conducting thorough risk analysis, we examine how these principles apply across statistical, machine learning, Bayesian, and agent-based models. We compare regulatory approaches between different authorities and make the case for wider adoption of credibility assessment standards to ensure reliable virtual evidence. Join us as we navigate the cutting edge of computational modelling in healthcare regulation.
 
Viceconti M, Pappalardo F, Rodriguez B, Horner M, Bischoff J, Musuamba Tshinanu F. In silico trials: Verification, validation and uncertainty quantification of predictive models used in the regulatory evaluation of biomedical products. Methods. 2021 Jan;185:120-127. 

Revolutionising Healthcare: The Power of Synthetic Data

Is the healthcare sector prepared to harness synthetic data's transformative potential whilst navigating its hidden pitfalls? This thought-provoking episode delves into the murky waters of synthetic data governance in medical research, examining how generative AI is reshaping our approach to patient information. As algorithms increasingly create convincing facsimiles of real clinical data, we explore the tantalising benefits—enhanced privacy protection, expanded research capabilities, and accelerated innovation cycles—alongside the concerning governance vacuum that threatens to undermine these advances. Our discussion unpacks why the absence of standardised creation and evaluation frameworks is stalling widespread adoption, despite synthetic data's promise. We make the case for urgent development of robust data standards, ethical guidelines and cross-disciplinary collaboration to ensure this powerful tool serves healthcare's highest goals. Join us as we navigate the complex terrain where technological innovation meets patient protection, and discover why establishing clear governance frameworks today is essential for tomorrow's medical breakthroughs.
 
Boraschi D, van der Schaar M, Costa A, Milne R. Governing synthetic data in medical research: the time is now. Lancet Digit Health. 2025 Feb 20:S2589-7500(25)00011-1. 

How Can We Trust Computational Models in Healthcare? A Framework for In Silico Clinical Trials

Are you curious about how computer simulations are changing the way we evaluate medical devices? In this episode, we delve into the fascinating world of in silico clinical trials (ISCTs), a cutting-edge approach that uses computational models to assess the safety and effectiveness of medical interventions. We explore a recent paper that highlights the unique challenges of ensuring the credibility of ISCT results. The authors present a comprehensive workflow for assessing this credibility, drawing on various methodologies and frameworks, including those from the FDA. By reviewing a range of ISCT studies, we discuss innovative patient model generation techniques, the integration of clinical outcomes, and how these virtual trials can complement traditional clinical studies. Join us as we unpack the potential of ISCTs to revolutionize medical device evaluation, making it more cost-effective, time-efficient, and ethically sound.
Source: Bodner J, Kaul V. A framework for in silico clinical trials for medical devices using concepts from model verification, validation, and uncertainty quantification. J Verif Valid Uncertain Quantif. 2022;7(2):021001. 
 
 
 
 
 
 
 
 
 
 
 
 
 

Probability & Patients: The Stochastic Science Behind In Silico Trial Breakthroughs

𝗪𝗵𝗮𝘁 𝗶𝗳 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝗰𝗹𝗶𝗻𝗶𝗰𝗮𝗹 𝘁𝗿𝗶𝗮𝗹 𝗯𝗿𝗲𝗮𝗸𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗶𝘀 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝘂𝘁𝗲𝗿? This episode explores the fascinating world of virtual patient models and stochastic engineering, unveiling how digital simulations are revolutionising healthcare clinical trials. Discover how simulated trials using sophisticated virtual patient models—complete with variations in age, body size, and disease progression—are introducing groundbreaking innovations in patient care.
We delve into stochastic engineering models that cleverly integrate uncertainty into device design, offering more precise and realistic predictions of clinical outcomes. Learn about the power prior method—a dynamic approach to combining digital and real-world evidence in clinical trials, enhancing efficiency and accelerating device delivery to patients whilst maintaining rigorous safety and accuracy standards.
Join us for an illuminating discussion at the cutting edge of healthcare innovation, where virtual modelling meets real-world patient impact.
 
Haddad T, Himes A, Thompson L, Irony T, Nair R; MDIC Computer Modeling and Simulation Working Group Participants. Incorporation of stochastic engineering models as prior information in Bayesian medical device trials. J Biopharm Stat. 2017;27(6):1089-1103. 

How can we properly evaluate the true economic impact of AI in healthcare?

How are the CHEERS-AI guidelines transforming economic evaluations of healthcare AI? In our latest podcast episode, we delve into how the Consolidated Health Economic Evaluation Reporting Standards for AI (CHEERS-AI) are revolutionizing the economic assessment landscape for artificial intelligence in healthcare. Discover why transparency and consistency are crucial, and how this comprehensive framework helps determine the true financial value of AI innovations in the medical field.
This episode navigates the intricate challenges of healthcare AI integration, including uncovering hidden costs and addressing the "black box" problem—where understanding AI's decision-making processes remains difficult yet essential for proper evaluation and implementation.
Join our conversation as we explore the broader ethical considerations and societal impacts of healthcare AI, emphasizing the importance of responsible innovation. Whether you're a health economist, researcher, or simply interested in AI's transformative potential in healthcare, this episode offers valuable perspectives on balancing innovation with responsibility to ensure AI advancements benefit both patients and healthcare systems alike.
Elvidge J, Hawksworth C, Avşar TS, Zemplenyi A, Chalkidou A, Petrou S, Petykó Z, Srivastava D, Chandra G, Delaye J, Denniston A, Gomes M, Knies S, Nousios P, Siirtola P, Wang J, Dawoud D; CHEERS-AI Steering Group. Consolidated Health Economic Evaluation Reporting Standards for Interventions That Use Artificial Intelligence (CHEERS-AI). Value Health. 2024 Sep;27(9):1196-1205. 

Revolutionising Healthcare: Inside the UK's MHRA Data Strategy

Will the MHRA's New Data Strategy Transform Healthcare Through Computational Innovation? In this episode, we explore the MHRA's groundbreaking data strategy for 2024-2027, a visionary plan heralding a fourth industrial revolution in healthcare. From supporting data-driven innovation to harnessing AI and real-world evidence, we dive deep into the strategic themes aimed at revolutionizing medical product development and regulation.
Learn how AI is set to change the game, enabling quicker development of treatments tailored to individual needs and monitoring safety like never before. We also address the challenges of data fragmentation, quality issues, and privacy concerns, while highlighting ambitious initiatives like the Centers of Excellence in Regulatory Science and Innovation.
Tune in to uncover how this strategy could position the UK as a leader in data-driven healthcare, inspiring global shifts towards personalized and equitable treatment for all.
Source: MHRA (2024) MHRA Data Strategy 2024 - 2024. www.gov.uk/government/publications/mhra-data-strategy-2024-2027