AI & Machine Learning
AI-driven models for clinical decision support and predictive analytics automation.
An engagement should start with the systems involved, the people responsible for them and the outcome the organization wants to achieve. Agree the work, dependencies, acceptance criteria and operating responsibilities before implementation. The scope and deliverables below are discussion points, subject to the requirements of each project.
Planning the engagement
- Select a specific use case and document its limitations
- Assess data suitability, evaluation criteria and human oversight
- Agree monitoring, change control and conditions for safe use
Questions to resolve
- What specific task is proposed for AI, and what are its limits? Define human review, unsuitable uses and a baseline for comparison.
- How will performance be evaluated on representative approved data? Agree criteria for errors, bias, robustness and meaningful benefit before release.
- Who can stop or change the system if its behavior deteriorates? Define monitoring, incident escalation and change control with responsible stakeholders.
Service requirements, credentials and project evidence can be discussed during a consultation. The published information describes areas of work; it does not promise a particular outcome or service level.