A framework for generative AI-driven extraction of clinical user needs in pediatric device development

Introduction: Generative artificial intelligence (GenAI) is becoming an important tool in medical product development. A main component of this development includes annotating, summarizing, and extracting key insights from expert interviews to identify clinical pain points and curate device requirements. These tasks are time- and labor-intensive, resulting in increased administrative burden and reduced efficiency. As a result, researchers have developed large language models (LLMs) that can disseminate research and interview findings with reduced workload and improved productivity. This study explores the use of GenAI, specifically GPT-4o, to extract user functional and design requirements from medical professional interviews for the iterative development of an infant heart rate detector for neonatal resuscitation. Methods: A total of 29 healthcare practitioners were interviewed using a semistructured interview format. The interviews were recorded and transcribed. GPT-4o was used to extract user insights from the transcripts, and the results were compared with manual interviewer notes. Results: A total of 26 h of interview data were collected. All interviewees validated the clinical need for a modality that enables quick and accurate heart rate (HR) measurement during neonatal resuscitation. A set of user requirements was extracted from the interviews and curated under the themes of ease of use, fast and accurate HR measurement, reusability, display, battery life, start-up time, and cost. Also, quantitative analyses of the interviewee's years of experience, clinical settings, and specialties were conducted. Discussion: These analyses were conducted using GPT-4o and compared with ground-truth manual annotations to determine the accuracy and reliability of GenAI in content extraction and summarization. Conclusion: Overall, this study explored the user requirements identified through in-depth interviews for the development of a pediatric medical device. It also aimed to demonstrate the potential of GenAI in curating these design requirements, offering a framework for researchers and product designers to explore the use of LLMs in curating user requirements and design specifications for medical devices.