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The system was built with OpenAI GPT-4 and a Streamlit interface, using medical guideline references, iterative prompt optimization (context, question chaining, and constraints), and human-in-the-loop plus two independent doctor evaluations. 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BMC Medical Informatics and Decision Making (2025) 25:242  \n[https://doi.org/10.1186/s12911-025-03088-3](https://doi.org/10.1186/s12911-025-03088-3)  \nBMC Medical Informatics and Decision Making  \nRESEARCH Open Access  \nExploring the possibilities and limitations of customized large language model to support and improve cervical cancer screening  \nViola Angyal 1*, Ádám Bertalan 1, Péter Domján2 and Elek Dinya 1  \nAbstract  \nBackground The rapid advancement of artificial intelligence, driven by Generative Pre-trained Transformers (GPT), has transformed natural language processing. Prompt engineering plays a key role in guiding model outputs effectively. Our primary objective was to explore the possibilities and limitations of a custom GPT, developed via prompt engineering, as a patient education tool, which delivers publicly available information through a user-friendly design that facilitates more effective access to cervical cancer screening knowledge.  \nMethod The system was developed using the OpenAI GPT-4 model and Python programming language, with the interface built on Streamlit for cloud-based accessibility and testing. It initially presented questions to testers for preliminary assessment. For cervical cancer-related information, we referenced medical guidelines. Iterative testing optimized the prompts for quality and relevance; techniques like context provision, question chaining, and promptbased constraints were used. Human-in-the-loop and two independent medical doctor evaluations were employed. Additionally, system performance metrics were measured.  \nResult The web application was tested 115 times over a three-week period in 2024, with 87 female (76%) and 28 male (24%) participants. A total of 112 users completed the user experience questionnaire. Statistical analysis showed a significant association between age and perceived personalization (p = 0 . 047) and between gender and system customization (p = 0 . 037) . Younger participants reported higher engagement, though not significantly. Females valued guidance on screening schedules and early detection, while males highlighted the usefulness of information regarding HPV vaccination and its role in preventing HPV-related cancers. Independent evaluations by medical doctors demonstrated consistent assessments ofthe system’s responses in terms of accuracy, clarity, and usefulness. Discussion While the system demonstrates potential to enhance public health awareness and promote preventive behaviors, encouraging individuals to seek information on cervical cancer screening and HPV vaccination, its conversational capabilities remain constrained by the inherent limitations of current language model technology.  \n*Correspondence:  \nViola Angyal[angyal.viola@phd.semmelweis.hu](angyal.viola@phd.semmelweis.hu)  \nFull list of author information is available at the end of the article  \n© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/l](http://creativecommons.org/l)icenses/by/4.0/.  \nAngyal et al. BMC Medical Informatics and Decision Making (2025) 25:242 Page 2 of 16  \nConclusions Although custom GPTs can not substitute a healthcare consultations, these tools can streamline","cbCaibfPXDl1iAER","https://ap.wps.com/l/cbCaibfPXDl1iAER","pdf",2120598,16,"English","# Abstract\n## Background\n## Method\n## Result\n## Discussion\n## Conclusions","[{\"question\":\"What was the primary objective of the customized GPT in this study?\",\"answer\":\"To explore the possibilities and limitations of a custom GPT, built via prompt engineering, as a patient education tool that helps users access cervical cancer screening knowledge.\"},{\"question\":\"How was the system developed and evaluated?\",\"answer\":\"It used OpenAI GPT-4 with Python and a Streamlit interface, referenced medical guidelines, employed iterative prompt testing, and included human-in-the-loop with evaluations by two independent medical doctors.\"},{\"question\":\"What were the key findings from the user testing and doctor evaluations?\",\"answer\":\"User testing (115 times) and questionnaires showed significant associations involving age and gender with perceived personalization and customization, while independent doctors consistently assessed the responses as accurate, clear, and useful.\"}]","Exploring the possibilities and limitations of customized large language model to support and improve cervical cancer screening | PDF",1790192482]