The usability principle states that the end users should be able to use an AI tool to achieve a clinical goal efficiently and safely in their real world environment. On one hand, this means that end users should be able to use the AI tool’s functionalities and interfaces easily and with minimal errors. On the other hand, the AI tool should be clinically useful and safe, for example, improve the clinicians’ productivity and/or lead to better health outcomes for the patients and avoid harm. To this end, five recommendations for usability are defined in the FUTURE-AI framework.
| Recommendations | Operations | Examples |
|---|---|---|
| Define intended use and user requirements (usability 1) | Define the clinical need and AI tool’s goal | Risk prediction, disease detection, image quantification |
| Define the AI tool’s end users | Patients, cardiologists, radiologists, nurses | |
| Define the AI model’s inputs | Symptoms, heart rate, blood pressure, ECG, image scan, genetic test | |
| Define the AI tool’s functionalities and interfaces | Data upload, AI prediction, AI explainability, uncertainty estimation | |
| Define requirements for human oversight | Visual quality control, manual corrections | |
| Adjust user requirements for all end user subgroups | According to role, age group, digital literacy level | |
| Establish mechanisms for human-AI interactions (usability 2) | Implement mechanisms to standardise data preprocessing and labelling | Data preprocessing pipeline, data labelling plugin |
| Implement an interface for using the AI model | Application programming interface | |
| Implement interfaces for explainable AI | Visual explanations, heatmaps, feature importance bars | |
| Implement mechanisms for user centred quality control of the AI results | Visual quality control, uncertainty estimation | |
| Implement mechanism for user feedback | Feedback interface | |
| Provide training (usability 3) | Create user manuals | User instructions, capabilities, limitations, troubleshooting steps, examples, and case studies |
| Develop training materials and activities | Online courses, workshops, hands-on sessions | |
| Use formats and languages accessible to intended end users | Multiple formats (text, video, audio) and languages (English, Chinese, Swahili) | |
| Customise training to all end user groups | Role specific modules for specialists, nurses, and patients | |
| Include training to enhance AI and health literacy | On application specific AI concepts (eg, radiomics, explainability), AI driven clinical decision making | |
| Evaluate user experience (usability 4) | Evaluate usability with diverse end users | According to sex, age, digital proficiency level, role, clinical profile |
| Evaluate user satisfaction using usability questionnaires | System usability scale | |
| Evaluate user performance and productivity | Diagnosis time with and without AI tool, image quantification time | |
| Assess training of new end users | Average time to reach competency, training difficulties | |
| Evaluate clinical utility and safety (usability 5) | Define clinical evaluation plan | Randomised control trial, in silico trial |
| Evaluate if AI tool improves patient outcomes | Better risk prevention, earlier diagnosis, more personalised treatment | |
| Evaluate if AI tool enhances productivity or quality of care | Enhanced patient triage, shorter waiting times, faster diagnosis, higher patient intake | |
| Evaluate if AI tool results in cost savings | Reduction in diagnosis costs, reduction in overtreatment | |
| Evaluate AI tool’s safety | Side effects or major adverse events in randomised control trials |