Contraception-related topics in chat dialogues between healthcare students and generative AI patients: a natural language processing analysis
Abstract
Background: Rapidly growing technology has enabled real-time digital services in healthcare, and new opportunities for sexual and reproductive health care. As assessing patient needs and communication effectively on digital platforms can be challenging, healthcare students must practise written communication, such as chat dialogues. Despite its increasing use in education, the way in which generative AI can enhance chat interactions between healthcare providers and patients remains poorly understood. The aim of this cross-sectional study was to explore contraception topics in chat dialogues between healthcare students and AI patients during AI simulations.
Methods: The AI application simulated a written chat dialogue between student and AI patient, using the CurreChat interface, to enable students to practise clinical skills and communication in digital health service chat dialogues. Purposive sampling was used to collect the data from fifth-year medical students (n = 24) and graduating midwifery students (n = 20) in higher education institutions of medicine and midwifery. Data were collected in August and October 2024. The data consisted of chat dialogues between healthcare students and generative AI patients. Natural language processing (NLP) and automated text analysis examined the contraception topics in the dialogues. The analysis software was based on pre-taught (self-supervised learning), industry-specific language models that detect meanings and their semantics in…
How strong is the evidence?
Evidence Hub reads this as a cross-sectional, done in people. Its evidence level is 4 of 5 (Low · observational). Observational designs can show an association but not prove cause. Reported size: 24 participants or records.
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