Characterizing Health-Related Information Needs of Domain Experts (regular paper)

International audienceIn information retrieval literature, understanding the users' intents behind the queries is critically important to gain a better insight of how to select relevant results. While many studies investigated how users in general carry out exploratory health searches in digital environments, a few focused on how are the queries formulated, specifically by domain expert users. This study intends to fill this gap by studying 173 health expert queries issued from 3 medical information retrieval tasks within 2 different evaluation compaigns. A statistical analysis has been carried out to study both variation and correlation of health-query attributes such as length, clarity and specificity of either clinical or non clinical queries. The knowledge gained from the study has an immediate impact on the design of future health information seeking systems

Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Health

ChatGPT has drawn considerable attention from both the general public and domain experts with its remarkable text generation capabilities. This has subsequently led to the emergence of diverse applications in the field of biomedicine and health. In this work, we examine the diverse applications of large language models (LLMs), such as ChatGPT, in biomedicine and health. Specifically we explore the areas of biomedical information retrieval, question answering, medical text summarization, information extraction, and medical education, and investigate whether LLMs possess the transformative power to revolutionize these tasks or whether the distinct complexities of biomedical domain presents unique challenges. Following an extensive literature survey, we find that significant advances have been made in the field of text generation tasks, surpassing the previous state-of-the-art methods. For other applications, the advances have been modest. Overall, LLMs have not yet revolutionized the biomedicine, but recent rapid progress indicates that such methods hold great potential to provide valuable means for accelerating discovery and improving health. We also find that the use of LLMs, like ChatGPT, in the fields of biomedicine and health entails various risks and challenges, including fabricated information in its generated responses, as well as legal and privacy concerns associated with sensitive patient data. We believe this first-of-its-kind survey can provide a comprehensive overview to biomedical researchers and healthcare practitioners on the opportunities and challenges associated with using ChatGPT and other LLMs for transforming biomedicine and health

Analysis of biomedical and health queries: Lessons learned from TREC and CLEF evaluation benchmarks

International audienceBACKGROUND:Inherited ichthyoses represent a group of rare skin disorders characterized by scaling, hyperkeratosis and inconstant erythema, involving most of the tegument. Epidemiology remains poorly described. This study aims to evaluate the prevalence of inherited ichthyosis (excluding very mild forms) and its different clinical forms in France.METHODS:Capture - recapture method was used for this study. According to statistical requirements, 3 different lists (reference/competence centres, French association of patients with ichthyosis and internet network) were used to record such patients. The study was conducted in 5 areas during a closed period.RESULTS:The prevalence was estimated at 13.3 per million people (/M) (CI95\%, [10.9 - 17.6]). With regard to autosomal recessive congenital ichthyosis, the prevalence was estimated at 7/M (CI 95\% [5.7 - 9.2]), with a prevalence of lamellar ichthyosis and congenital ichthyosiform erythroderma of 4.5/M (CI 95\% [3.7 - 5.9]) and 1.9/M (CI 95\% [1.6 - 2.6]), respectively. Prevalence of keratinopathic forms was estimated at 1.1/M (CI 95\% [0.9 - 1.5]). Prevalence of syndromic forms (all clinical forms together) was estimated at 1.9/M (CI 95\% [1.6 - 2.6]).CONCLUSIONS:Our results constitute a crucial basis to properly size the necessary health measures that are required to improve patient care and design further clinical studies

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