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Using artificial intelligence to create diverse and inclusive medical case vignettes for education

Abstract Aims: Medical case vignettes play a crucial role in medical education, yet they often fail to authentically represent diverse patients. Moreover, these vignettes tend to oversimplify the complex relationship between patient characteristics and medical conditions, leading to biased and potentially harmful perspectives among students. Displaying aspects of patient diversity, such as ethnicity, in written cases proves challenging. Additionally, creating these cases places a significant burden on teachers in terms of labour and time. Our objective is to explore the potential of artificial intelligence (AI)-assisted computer-generated clinical cases to expedite case creation and enhance diversity, along with AI-generated patient photographs for more lifelike portrayal. Methods: In this study, we employed ChatGPT (OpenAI, GPT 3.5) to develop diverse and inclusive medical case vignettes. We evaluated various approaches and identified a set of eight consecutive prompts that can be readily customized to accommodate local contexts and specific assignments. To enhance visual representation, we utilized Adobe Firefly beta for image generation. Results: Using the described prompts, we consistently generated cases for various assignments, producing sets of 30 cases at a time. We ensured the inclusion of mandatory checks and formatting, completing the process within approximately 60 min per set. Conclusions: Our approach significantly accelerated case creation and improved diversity, although prioritizing maximum diversity compromised representativeness to some extent. While the optimized prompts are easily reusable, the process itself demands computer skills not all educators possess. To address this, we aim to share all created patients as open educational resources, empowering educators to create cases independently.

Programming like it's 1984

Hoe ga je als directeur om met ChatGPT?

De ontwikkelingen op het gebied van artificial intelligence (AI) gaan razendsnel. Hoe ga je als schooldirecteur om met de kansen en keerzijden van generatieve AI in het onderwijs, zoals ChatGPT? Onderwijskundige en specialist digitale geletterdheid Josien Boetje geeft uitleg, inzichten en tips om jou en je team te inspireren.

AI in onderzoek [blog]

Het doen van onderzoek is een tijdrovend en inspannend proces. Dat is enerzijds heel goed, als we die tijd besteden aan het diep verwerken en nadenken over de onderzoeksinhoud. Maar bij andere aspecten van onderzoek zoals het eindeloos verdwalen in de literatuur of het verwerken van data kunnen we wel wat hulp van AI gebruiken. Gelukkig zijn er allerlei tools die ons hierbij kunnen ondersteunen in de verschillende fases van het onderzoeksproces.

Using ChatGPT-4 to grade open question exams

This research investigates the potential and challenges of using artificial intelligence, specifically the ChatGPT-4 model developed by OpenAI, in grading and providing feedback in an educational setting. By comparing the grading of a human lecturer and ChatGPT-4 in an experiment with 105 students, our study found a strong positive correlation between the scores given by both, despite some mismatches. In addition, we observed that ChatGPT-4's feedback was effectively personalized and understandable for students, contributing to their learning experience. While our findings suggest that AI technologies like ChatGPT-4 can significantly speed up the grading process and enhance feedback provision, the implementation of these systems should be thoughtfully considered. With further research and development, AI can potentially become a valuable tool to support teaching and learning in education. https://saiconference.com/FICC

A Thought-provoking Question Matrix to Guide the Development of Foundation-Model-based Applications

Organizations feel an urgency to develop and implement applications based on foundation models: AI-models that have been trained on large-scale general data and can be finetuned to domain-specific tasks. In this process organizations face many questions, regarding model training and deployment, but also concerning added business value, implementation risks and governance. They express a need for guidance to answer these questions in a suitable and responsible way. We intend to offer such guidance by the question matrix presented in this paper. The question matrix is adjusted from the model card, to match well with development of AIapplications rather than AI-models. First pilots with the question matrix revealed that it elicited discussions among developers and helped developers explicate their choices and intentions during development.