Introduction: Creating a robust technological infrastructure is vital for integrating autonomous systems into healthcare effectively.
Components:
- AI and Machine Learning: Utilize AI to enhance decision-making and personalization.
- Interoperability: Ensure systems can seamlessly communicate and share data.
- Security: Implement stringent security measures to protect sensitive healthcare data.
Objectives:
- Develop a scalable, reliable infrastructure.
- Support real-time data processing and analysis.
- Enable smooth human-machine interaction.
Introduction: Ethics are crucial in the integration of autonomous systems within society.
Key Concepts:
- Trust Agreement: Framework ensuring ethical behavior and trustworthiness of systems.
- Stakeholder Involvement: Include input from all relevant parties, including patients and healthcare providers.
- Compliance: Adherence to ethical guidelines and regulations.
Objectives:
- Foster trust between users and technology.
- Ensure ethical decision-making in autonomous systems.
- Promote transparency and accountability.
Introduction: Explainability in AI is critical for building user trust.
Techniques:
- Transparent Algorithms: Design algorithms that are understandable to users.
- User-Friendly Interfaces: Develop interfaces that provide clear explanations of system decisions.
- Continuous Feedback: Implement mechanisms for user feedback and system improvement.
Objectives:
- Enhance user understanding of AI decisions.
- Build and maintain user trust.
- Ensure ethical and transparent AI operations.
We will devise a first working prototype of our system. Such a prototype will have to be fully refined and deployed for general use in future projects. During this project, the system will be experimented in two case studies. Patients involved (on a voluntary basis) in the experiments will be monitored by their PAs, which can be embodied in assistive robots with a different range of autonomy (in the project, a telepresence robot with the possibility of autonomous behaviors will be used). Via Complex Event Processing Techniques (we will adapt ISEQL [Pe19]), the system will be able to detect event models in the healthcare domain. Strict collaborations with medical doctors will help define patterns to be detected, and based upon tests in real clinical settings, validate the developed event detection approach. Through assistive robots, our system will be able to support patients also at the cognitive level [GS07]. In fact, social robots can provide companionship and assistance in the daily life of children, older, and disabled people but have also a potential as educational technology [R+20]. Positive effects on users’ behavior have been shown in many contexts [M+13, M+20]. In the healthcare domain, a robot can provide also personalized cognitive interventions and contribute to improving adherence to a healthy lifestyle.
Introduction: Testing and pilot applications are essential for evaluating autonomous systems in real-world scenarios.
Components:
- Testbed Environment: Controlled setting for testing system performance and behavior.
- Pilot Applications: Real-world implementations to gather data and feedback.
- Evaluation Metrics: Criteria for assessing system effectiveness and reliability.
Objectives:
- Validate system performance in real-world conditions.
- Identify and address potential issues.
- Gather user feedback for improvements.