Short answer
Shift focus from 'black-box' high-performance algorithms to transparent, user-centric systems that provide clear rationales for their outputs.
- Field
- User-Centred Design
- Source
- BMJ (2025)
- Method
- Delphi consensus method and interdisciplinary expert consultation.
- Sample
- 117 interdisciplinary experts
- Evidence
- Strong effect
The deployment of healthcare AI depends on a consensus-based framework of fairness, universality, traceability, usability, robustness, and explainability to ensure clinical safety and user trust. This user-centred design research insight is drawn from a 2025 study published in BMJ. Using Delphi consensus method and interdisciplinary expert consultation. with 117 interdisciplinary experts, researchers explored how this design variable affects real-world outcomes. The key design takeaway: Shift focus from 'black-box' high-performance algorithms to transparent, user-centric systems that provide clear rationales for their outputs.
Standardized 'FUTURE-AI' framework increases medical AI adoption through 6 core trust principles
The deployment of healthcare AI depends on a consensus-based framework of fairness, universality, traceability, usability, robustness, and explainability to ensure clinical safety and user trust.
BMJ · 2025
Key Findings
- 01AI adoption is hindered by a lack of trust and standardized validation.
- 02Trustworthy AI requires six pillars: Fairness, Universality, Traceability, Usability, Robustness, and Explainability.
- 03Best practices must cover the entire lifecycle from design to post-market monitoring.
Application
Design takeaway
Shift focus from 'black-box' high-performance algorithms to transparent, user-centric systems that provide clear rationales for their outputs.
How to apply
Implement 'Explainable AI' (XAI) features in the UI, such as heatmaps or confidence scores, to help users validate the AI's logic.
Project actions
- 01Use the 'Explainability' principle in your project by showing how your product communicates its status to the user.
- 02Reference 'Fairness' when discussing target markets and user research to avoid bias.
Method & Evidence
Variables
Strengths & Limitations
Strengths
- +High expert consensus
- +Covers the full product lifecycle
- +Multidisciplinary approach
Limitations
Students may find it difficult to technically implement 'Robustness' or 'Traceability' in a simple prototype.
Reliability & validity
High reliability due to the Delphi consensus method involving over 100 global experts.
Think critically
If an AI is 99% accurate but cannot explain its reasoning, is it a better design than an AI that is 90% accurate but fully transparent?
Design Principles
"The FUTURE-AI Framework: Fairness, Universality, Traceability, Usability, Robustness, Explainability."
In design, User-Centred Design (design topics) emphasizes usability and the emotional relationship between user and product. This research operationalizes 'trust'—a psychological factor—into technical requirements, bridging the gap between complex innovation and practical human adoption.
What This Means for Your Design
If people don't trust a high-tech product, they won't use it. This study lists exactly what designers need to do—like making the AI explain its 'thinking'—to make medical technology successful.
How to use in your project
- 1.Cite the FUTURE-AI framework when justifying your design specifications for any digital or smart product to show you are following international best practices for trust.
Add to My Project
Quick Cite
Paragraph starter
According to the FUTURE-AI framework (Lekadir et al., 2025), trustworthy design in complex systems relies on six pillars, notably usability and explainability. This design incorporates these by providing clear visual feedback on the system's decision-making process, thereby increasing user confidence and adoption.
Source
BMJ
FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare
journal · 2025
View sourceQuestions About This Research
- What does the research say about standardized 'future-ai' framework increases medical ai adoption through 6 core trust principles?
- Shift focus from 'black-box' high-performance algorithms to transparent, user-centric systems that provide clear rationales for their outputs. Evidence: BMJ (2025).
- Why does "Standardized 'FUTURE-AI' framework increases medical AI adoption through 6 core trust principles" matter for design?
- In IB DT, User-Centred Design (Topic 7) emphasizes usability and the emotional relationship between user and product. This research operationalizes 'trust'—a psychological factor—into technical requirements, bridging the gap between complex innovation and practical human adoption.
- How can designers apply this research?
- Shift focus from 'black-box' high-performance algorithms to transparent, user-centric systems that provide clear rationales for their outputs.
- What were the main findings?
- AI adoption is hindered by a lack of trust and standardized validation.. Trustworthy AI requires six pillars: Fairness, Universality, Traceability, Usability, Robustness, and Explainability.. Best practices must cover the entire lifecycle from design to post-market monitoring.
- What research method was used?
- Delphi consensus method and interdisciplinary expert consultation. with 117 interdisciplinary experts.
- How strong is the evidence?
- Evidence strength is rated Strong effect, based on a 2025 journal from BMJ.
- What should I do differently in my next project?
- Implement 'Explainable AI' (XAI) features in the UI, such as heatmaps or confidence scores, to help users validate the AI's logic.
- What are the limitations?
- The guidelines are high-level and require specific technical translation for different medical specialties.