Â鶹Íø

This course prepares learners to understand and apply generative AI tools across healthcare contexts—whether as future clinicians, researchers, health educators, administrators, or innovators in health technology.

It equips learners with knowledge and practical skills to use generative AI responsibly and effectively for clinical documentation, patient education, diagnostic support, research synthesis, and specialty‑specific workflows.

This is a 3 credit‑point course with approximately 120 hours of learning. Learners progress from foundational concepts about AI capabilities and limitations to text‑based and multimodal applications that integrate images and clinical data into complex workflows.

Emphasis is placed on critical judgment: determining when AI can safely augment care, when outputs require rigorous validation, and when human expertise must remain primary. The curriculum foregrounds healthcare imperatives—accuracy, evidence‑based practice, patient safety, privacy, and professional responsibility—and trains learners to identify and mitigate risks such as hallucinations, bias, and threats to equity.The course aligns with COP‑ODL guidance for online delivery and assessment. Through scaffolded, hands‑on activities and weekly projects, learners practise prompting strategies, validation methods, quality assurance processes, and documentation of prompt histories and validation steps. Social learning and peer feedback support comparative critique and shared learning across specialties. Learners specialise by selecting clinical scenarios relevant to their practice and build sustainable, ethically grounded AI workflows. Summative assessment is objective and graded.

On completing the course, students will be able to:

• Apply basic generative AI tools to simple healthcare‑related tasks such as documentation, summarisation and patient‑education text
• Describe key ethical, privacy and safety considerations when using AI tools in healthcare
• Use guided AI platforms and Colab notebooks to perform simple workflow steps and interpret basic model outputs.
• Reflect on personal responsibilities when using AI tools in healthcare and communicate basic AI‑generated outputs clearly to different audiences.