In Silico Trials, Real Impacts!

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

Unleashing the Potential of Health Data: Insights from the Sudlow Review

What is the roadmap to transform how we use health data in the UK? This episode discusses the Sudlow Review and highlights the immense potential of linking health data, tackling accessibility challenges, and the importance of public trust. We discuss its five critical recommendations to make health data a national infrastructure, streamlining access, and ensuring ethical governance across the UK.
Whether you're a researcher, policy maker, or simply curious about the future of medical innovation, tune in to discover how harnessing health data can revolutionize patient care and healthcare research.
Source: Sudlow C (2024) Uniting Health Data in the UK Review. Health Data Research UK. https://www.hdruk.ac.uk/helping-with-health-data/the-sudlow-review

Regulatory Frontiers: UK's Centres of Excellence in Regulatory Science and Innovation

How is the UK positioning itself at the forefront of regulatory science innovation through strategic investments in centres of excellence? This episode explores the UK Government-funded Centres of Excellence in Regulatory Science and Innovation (CERSI) Initiative and its pivotal role in shaping the future regulatory landscape for AI and in silico technologies in healthcare. We examine two of the seven CERSIs funded by UK Government. Brunel University's critical work on building trust frameworks and ethical governance for AI applications, alongside the University of Manchester's groundbreaking advances in in silico clinical trials that promise to revolutionise drug development timelines and efficiency.
This podcast delves into the regulatory science challenges and opportunities presented by these emerging technologies, offering insights into how the UK CERSI strategy is creating new pathways for innovation while ensuring patient safety. From establishing robust validation methods for in silico trials to developing adaptive regulatory frameworks for AI, discover how these centres of excellence are addressing the complex intersection of technology, healthcare, and regulation. Join us for an essential conversation about the transformative potential of these initiatives and their implications for researchers, regulators, and healthcare systems worldwide.Source: Developing regulatory science to advance healthcare (2025) www.ukri.org/news/developing-regulatory-science-to-advance-healthcare

Can NHS Secure Data Environments Unlock the Future of UK Life Sciences?

Could NHS Data Environments Revolutionise UK Life Sciences Innovation? This episode focuses on the incredible potential of NHS data in revolutionising medical product development and regulation. The Association of the British Pharmaceutical Industry (ABPI) report, published in February 2025, investigates how to improve the regional Secure Data Environment (SDE) network to better serve the life sciences industry's research needs. The report outlines findings from a consultation with ABPI, ABHI, and BIA members regarding their use of NHS health data. It highlights key industry requirements for the SDE network, including the need for federated data access, streamlined governance, and commercially competitive timeframes. The report also reveals concerns about insufficient industry involvement in the network's design and implementation. Ultimately, it provides recommendations to NHS England and other relevant bodies to align the SDE network's development with the demands of life sciences research, aiming to enhance the UK's attractiveness for R&D investment.
 
Source: Association of British Pharmaceutical Industries (2025)  The value of industry clinical trials to the UK. www.abpi.org.uk/publications/the-value-of-industry-clinical-trials-to-the-uk-extended-report

Virtual You vs Cancer: How Digital Twins Are Revolutionizing Treatment

What if your doctor could test hundreds of cancer treatments on a virtual version of your tumour before giving you a single dose of medication? In this episode, we dive into the fascinating concept of digital twins, particularly their application in oncology.
Discover how digital twins, virtual replicas of tumours, are being used to simulate treatment scenarios and predict outcomes, offering unprecedented precision in cancer care. Learn about the intricate process of creating these digital twins, from capturing medical imaging data to developing complex mathematical models. With insights tailored to the individual, digital twins represent the future of personalized medicine, enabling clinicians to experiment with different therapies without any risk to real patients.
Explore the real-world impact of this technology, as digital twins assist doctors in treatment planning and help predict patient responses to various drugs. As we stand on the cusp of a revolution in cancer care, join us in unravelling the potential of digital twins to transform how we diagnose, treat, and manage cancer. Tune in for this deep dive into the groundbreaking world of InSilico trials and their real impact on patient care.Wu C, Lorenzo G, Hormuth DA 2nd, Lima EABF, Slavkova KP, DiCarlo JC, Virostko J, Phillips CM, Patt D, Chung C, Yankeelov TE. Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology. Biophys Rev. 2022 Jun;3(2):021304.

Digital Footprints to Medical Breakthroughs: The Real-World Data Revolution

What if the future of healthcare innovation lies not in controlled trials, but in the digital footprints we leave every day? Real-world data from health records, wearables, and patient experiences revolutionises medical research. This episode explores the profound impact of real world data (RWD) in healthcare.
Discover how everyday data sources like Fitbits, electronic health records (EHRs), and insurance claims are reshaping the medical landscape. Learn about the nuances of real world data, from its messy nature to its invaluable insights for patient care.
Dive into the complexities of data standardization, the use of machine learning, and the challenges of privacy and bias. Explore how RWD is paving the way for personalized medicine and disease prevention, offering hope for rare disease research through global data pooling.
Join the conversation about the future of healthcare, privacy concerns, and the exciting potential of sharing patient-generated data. Tune in now and be part of the revolution where algorithms, data, and human insight converge to shape a healthier tomorrow.
 
Liu F, Panagiotakos D. Real-world data: a brief review of the methods, applications, challenges and opportunities. BMC Med Res Methodol. 2022 Nov 5;22(1):287. doi: 10.1186/s12874-022-01768-6. Erratum in: BMC Med Res Methodol. 2023 May 2;23(1):109. doi: 10.1186/s12874-023-01937-1.
 

The Science Behind Making Virtual Trials Trustworthy: Ten Rules

Would you trust a computer to help decide your medical treatment? Imagine doctors using virtual experiments to test treatments before giving them to real patients - sounds like sci-fi, right? But it's happening now! Just like pilots train in flight simulators before flying real planes, scientists are using powerful computer models to improve healthcare.
But here's the million-pound question: How do we know these digital predictions are reliable?
In this episode, we'll reveal the 10 golden rules scientists follow to ensure their virtual experiments are trustworthy. From making sure they're using the right data to having independent experts check their work (like having a second opinion from another doctor!), these rules are changing the game in modern medicine.
 
Erdemir A, Mulugeta L, Ku JP, Drach A, Horner M, Morrison TM, Peng GCY, Vadigepalli R, Lytton WW, Myers JG Jr. Credible practice of modeling and simulation in healthcare: ten rules from a multidisciplinary perspective. J Transl Med. 2020 Sep 29;18(1):369. doi: 10.1186/s12967-020-02540-4. 
Manchel A, Erdemir A, Mulugeta L, Ku JP, Rego BV, Horner M, Lytton WW, Myers JG Jr, Vadigepalli R. A rubric for assessing conformance to the Ten Rules for credible practice of modeling and simulation in healthcare. medRxiv [Preprint]. 2024 Nov 2. doi: 10.1101/2024.10.31.24316520.
 

STANDING Together: Equitable AI and In Silico Technologies in Healthcare

What if In Silico Technologies in healthcare could truly serve everyone? The STANDING Together recommendations are revolutionising how we think about health data diversity and inclusion. From bustling cities to remote villages, discover how this groundbreaking initiative is reshaping healthcare technologies development through the voices of patients, practitioners, and policymakers. Join us as we explore the journey towards equitable healthcare technology that leaves no one behind
We'll explore the importance of data diversity and patient involvement in shaping the future of medical technology—be it AI diagnostic systems or in silico technologies predicting drug interactions. Understand how these recommendations propose a shift towards a more responsible and inclusive approach, requiring the commitment of researchers, developers, policymakers, and patients themselves.
This episode highlights patient advocacy, urging everyone to become proactive in their healthcare journey and data literacy. As AI becomes increasingly integrated into our lives, ensuring fairness and equity becomes paramount—a responsibility we all share.www.datadiversity.org
Alderman JE, Palmer J, Laws E, McCradden MD, Ordish J, Ghassemi M, Pfohl SR, Rostamzadeh N, Cole-Lewis H, Glocker B, Calvert M, Pollard TJ, Gill J, Gath J, Adebajo A, Beng J, Leung CH, Kuku S, Farmer LA, Matin RN, Mateen BA, McKay F, Heller K, Karthikesalingam A, Treanor D, Mackintosh M, Oakden-Rayner L, Pearson R, Manrai AK, Myles P, Kumuthini J, Kapacee Z, Sebire NJ, Nazer LH, Seah J, Akbari A, Berman L, Gichoya JW, Righetto L, Samuel D, Wasswa W, Charalambides M, Arora A, Pujari S, Summers C, Sapey E, Wilkinson S, Thakker V, Denniston A, Liu X. Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations. Lancet Digit Health. 2025 Jan;7(1):e64-e88. doi: 10.1016/S2589-7500(24)00224-3.
 

Can Computer Simulations Make Medical Products Safer?

Ever wondered how we can make shoulder replacements safer without endless clinical trials? Step into the future of medical innovation where virtual patients help test new drugs and devices. Discover how computer simulations could make medicine safer, faster, and more ethical. Learn why experts believe digital trials might be the game-changer healthcare has been waiting for.
Viceconti M, Emili L, Afshari P, Courcelles E, Curreli C, Famaey N, Geris L, Horner M, Jori MC, Kulesza A, Loewe A, Neidlin M, Reiterer M, Rousseau CF, Russo G, Sonntag SJ, Voisin EM, Pappalardo F. Possible Contexts of Use for In Silico Trials Methodologies: A Consensus-Based Review. IEEE J Biomed Health Inform. 2021 Oct;25(10):3977-3982. doi: 10.1109/JBHI.2021.3090469.