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# System prepended metadata

title: TEA-DT Workshop (Health)

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# TEA-DT Workshop (Health)

![](https://raw.githubusercontent.com/alan-turing-institute/turing-commons/main/docs/assets/images/illustrations/stakeholder-engagement.jpg)

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⏱️ **Agenda**

**Times:** 13:00–16:50 (UK Time)
**Breaks:** we will take breaks every hour, but please feel free to step away from the computer whenever you need.

1. Introduction to project and workshops
2. Introductions from participants
3. Structured discussion around assurance needs and capabilities
4. Presentation 1: What is TEA?
5. Wrap-Up
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ℹ️ **About this Document**

Please use this document to answer each of the following questions. You can provide your answers anonymously. To do so, please ensure you are not signed in, and do not write anything that may reveal identifying information.
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## Questions

### 1) What does ‘assurance’ mean to you?

- Confidence and Trust in digital twin, its ethical use, reliability and "free from bias" for algorithms used within.
- Internal reviews when it comes to developing and evaluating models
- Maintenance within assurance guidelines for deployed tools.
- Reproducibility as key for building trust in models
- Providing confidence to patients around clinical data use (PPIE)
- Public/Patient engagement often required by funders now, becomes part of assurance process?
- compliance to regulatory guidelines for medical devices
- meeting requirements in contracts with hospitals
- assurance around data use for training future models (data as protected good), data provenance becomes important
- restricting access to PHI and handling that along with the data
- assurance integral for clinical translation, doctors need to be convinced that research/models are "working" / accurate
- peer review as covering some aspects of assurance in the academic setting
- assurance as necessity for onboarding clients
- burden of documentation around assurance
- overlap between assurance and general efforts towards reproducibility
- degree of assurance highly dependent on use case (building a general-purpose simulation platform vs use by clinician in real settings) i.e. context of use
- Degree of assurance, and methods of assurance, should follow a specific context of use for the tool in question, and be re-evaluated for each independent use of that tool. (i.e. a different research question)

### 2) What processes of assurance do you currently follow? (and Why?)

- Across analysis process there are strict regulations, which require documentation for regulatory bodies and auditors.
- Building trust and confidence (scientific integrity?) with stakeholders (e.g. industrial partners, healthcare professionals, patients)
- Use of generative AI to reproduce 

### 3) Are there any guiding principles that you follow?

- (ex: Patient Safety)
- FDA established standards around digital twins
- Privacy: Protecting Individual data (eg individual geometric meshes from biobank)
    - Ethical approval for data release differing on what data is protected (eg are meshes personal data or not?)
- Context of Use
- Performance/Accuracy
- Contestability
- Explainability/Interpretability


### 4) What techniques or mechanisms do you use to assure these principles?

- Sensitivity analysis
- Uncertainty quantification
- Accuracy benchmarking
- Strong alignment of results with existing literature on topic
- Model validation against independent clinical datasets
- Comparison of model predictions against an unseen dataset (i.e. in-silico drug trials)

## Feedback

### 1. Do you have any feedback about the TEA platform?

- Answer 1
- Answer 2
- Answer 3

### 2. Do you have any feedback about the TEA-DT workshops?

- Answer 1
- Answer 2
- Answer 3

### 3. Do you have any feedback about the TEA-DT project, more generally?

- Answer 1
- Answer 2
- Answer 3
