Computational modeling and simulation for medical devices: a summary of the 2024 FDA/MDIC Symposium
Bibliographic record
Abstract
Abstract Computational modeling and simulation (CM&S) is a powerful tool that can be used to support the development, evaluation, and regulatory authorization of medical devices. CM&S can provide valuable insights into device performance, safety, and effectiveness, as well as reduce the need for animal or human testing. Computational models are, however, idealized digital representations that often have many assumptions and need to be credible before they are used in decision making that could incur patient harm. While the medical device community has made great strides to advance the use of CM&S, a number of challenges remain. To begin addressing these challenges, the US Food and Drug Administration (FDA) and the Medical Device Innovation Consortium (MDIC) co-sponsored the FDA/MDIC Symposium on Computational Modeling and Simulation on April 16–17, 2024 in College Park, Maryland, USA, where attendees from around the world convened to hear from leaders in the field through a unique blend of invited presentations and interactive panel discussions. The symposium agenda covered several major themes, including credibility considerations for CM&S used across the medical device total product life cycle, practical examples of performing model credibility assessment, and the use of CM&S for clinical decision making and the emerging areas of in silico clinical trials and digital twins. The objective of this article is to summarize the major takeaways of the symposium. We first provide an overview of the invited presentations followed by summaries of the topics covered during the interactive panel discussions. In doing so, we highlight the main takeaways and identify areas in which panelists had shared perspectives or differences of opinion. Next, we present the results of a survey conducted at the symposium that sought attendees’ perspectives on different aspects of medical device CM&S. Finally, we conclude by summarizing the major outcomes of the symposium, including areas where more work and investment are needed to advance the field.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".