Responsible AI, Privacy & Output Verification in Healthcare
Course modules
01Define appropriate uses and boundaries
Compare fictional administrative and research-support tasks with activities requiring clinical expertise. Set clear limits and identify who is accountable for reviewing each output.
02Recognise sensitive information
Identify unnecessary personal details in fictional records and practise minimising inputs. Discuss why removing a name alone does not establish that information is safe to share.
03Review tools and access arrangements
Use a checklist to raise questions about tool access, data handling and organisational approval. Record unanswered questions for the appropriate specialist rather than assuming a tool is suitable.
04Verify outputs and references
Check generated statements, figures and citations against appropriate original sources. Flag invented details, contradictions and unsupported conclusions for review.
05Explore bias and uncertainty
Compare fictional outputs for missing context, inconsistent treatment and overconfident wording. Document limitations and escalate issues that need qualified judgement.
06Build a review and escalation plan
Create a checklist covering source checks, reviewer sign-off and change records. Practise responding to a fictional error with a clear pause, correction and escalation process.
Course Enquiry
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Curriculum, duration and tuition are indicative and subject to confirmation. Use fictional examples; do not bring patient-identifiable information. Educational awareness training only, not legal advice, compliance certification, clinical licensure or authorisation for independent clinical decisions. Real-world use requires appropriate organisational and specialist review. Software requirements will be confirmed before enrolment. An enquiry does not confirm a place.