Generative AI for Healthcare Students and Professionals
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Generative AI for Healthcare Students and Professionals
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What you'll learn
Explore real world clinical case studies of AI.
Discuss responsible and ethical use of generative AI in healthcare.
Skills you'll gain
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Details to know
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There are 3 modules in this course
This course aims to provide healthcare students and professionals with a solid foundation of how generative AI is used in their sector adopting a balanced discourse of information. This will be achieved by using case studies that will analyse the current landscape of AI in different fields such as Higher Education, NHS, Public Health and Clinical Research. These case studies will be complemented by bioethics, confidentiality and humanistic perspectives of this technology. For a comprehensive, but yet concise, overview of the topic learners will also delve into the historical perspectives of AI. The course will conclude with final reflective thoughts on what the future might hold for healthcare in terms of utilising responsibly generative AI.
As this is an introductory course on generative AI for healthcare, it will be targeted to students and qualified professionals who would like to learn more about this emerging technology in an easy-to-follow approach through the use of interactive case studies that will have real-world applications.
This week, we'll introduce the course, explore generative AI’s historical and emerging role in healthcare, and delve into AI and VR applications in medical imaging. We'll also discuss professional integrity, focusing on human oversight, and the impact of AI in academic and professional environments.
What's included
2 videos8 readings2 assignments1 discussion prompt
2 videos•Total 80 minutes
- AI to maximise VR medical imaging: current technologies and a case of application•32 minutes
- Professional integrity as a framework for encouraging responsible use of generative AI •49 minutes
8 readings•Total 93 minutes
- Intended learning outcomes•5 minutes
- Disclaimer•3 minutes
- MOOC instructors and content contributors •5 minutes
- Podcast - course introduction•5 minutes
- The teaching and scholarship podcast - episode 59•60 minutes
- Intended learning outcomes•5 minutes
- Intended learning outcomes•5 minutes
- Recommended optional reading•5 minutes
2 assignments•Total 60 minutes
- Week 1 MCQs •30 minutes
- Week 1 ungraded quiz•30 minutes
1 discussion prompt•Total 10 minutes
- Supporting open discussions on genAI in higher education•10 minutes
This week, we'll explore prompt engineering in AI for coding applications and develop an AI-generated application. We'll delve into generative AI's role in dental smile analysis, covering techniques like data augmentation and CNNs. We'll also learn about a multi-channel prediction model for pressure injuries in hospitalised patients. Lastly, we'll examine the ethical and humanistic aspects of AI in surgical training.
What's included
10 videos10 readings2 assignments1 discussion prompt
10 videos•Total 48 minutes
- Introduction and lesson outline•3 minutes
- Dental aesthetics: challenges and objectives•4 minutes
- Overview of proactive healthcare•2 minutes
- Internet of mirrors for healthcare•11 minutes
- Data curation using generative AI•3 minutes
- Data processing for dental smile analysis•3 minutes
- Gummy and normal smile classification•2 minutes
- CNN for gummy and normal smile classification•14 minutes
- Mirro user interface (UI) - demo•4 minutes
- Conclusions•1 minute
10 readings•Total 177 minutes
- Intended learning outcomes•5 minutes
- Prompt engineering in AI: a bespoke database application in python•35 minutes
- Intended learning outcomes•5 minutes
- Recommended optional reading•5 minutes
- Intended learning outcomes•5 minutes
- A fused multi-channel prediction model of pressure injury•35 minutes
- Optional activity•25 minutes
- Intended learning outcomes•5 minutes
- Podcast - humanistic perspective of generative AI in surgery •47 minutes
- Recommended optional reading•10 minutes
2 assignments•Total 60 minutes
- Week 2 MCQs•30 minutes
- Week 2 ungraded MCQs•30 minutes
1 discussion prompt•Total 10 minutes
- Reflective task and action plans•10 minutes
This week, we’ll explore AI’s role in supporting COPD care pathways, considering both design and patient perspectives. We’ll delve into AI’s application in Scotland’s health and social care system, focusing on discrete event simulation and Bayesian methods. We’ll also learn about AI’s use in radiology for screening and diagnosis. Lastly, we’ll examine responsible AI use in research, emphasising accountability, authenticity, curiosity, and legacy, with a focus on ethical reflections and broader social implications.
What's included
3 videos8 readings2 assignments4 discussion prompts
3 videos•Total 74 minutes
- Designing and deploying predictive AI in a COPD pathway•27 minutes
- Introduction to AI in healthcare•12 minutes
- Ethics and integrity•36 minutes
8 readings•Total 105 minutes
- Intended learning outcomes•5 minutes
- Intended learning outcomes•5 minutes
- Modelling the health and social care system with the help of artificial intelligence•40 minutes
- Intended learning outcomes•5 minutes
- AI in radiology – chest X-ray prioritisation in the lung cancer diagnostic pathway•25 minutes
- Detecting abnormalities on CT scans of the brain•10 minutes
- Intended learning outcomes•5 minutes
- Recommended optional reading•10 minutes
2 assignments•Total 60 minutes
- Week 3 MCQs•30 minutes
- Formative questions•30 minutes
4 discussion prompts•Total 40 minutes
- Discussion task•10 minutes
- Reflective task•10 minutes
- Reflective and analytical task•10 minutes
- Reflective task•10 minutes
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