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Timely and relevant information enables clinicians to make informed decisions about patient care outcomes. However, discovering related and understandable information from the vast medical literature is challenging. To address this problem, we aim to enable the development of search engines that meet the needs of medical practitioners by incorporat...
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... Ethical Considerations and Limitations As AI systems become more prevalent in sensitive domains, ethical implications have come to the forefront. Frihat et al. [9] discussed the importance of considering document difficulty for medical practitioners in personalized search engines, highlighting the need for AI systems to adapt to varying levels of user expertise. ...
This paper presents CaseGPT, an innovative approach that combines Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) technology to enhance case-based reasoning in the healthcare and legal sectors. The system addresses the challenges of traditional database queries by enabling fuzzy searches based on imprecise descriptions, thereby improving data searchability and usability. CaseGPT not only retrieves relevant case data but also generates insightful suggestions and recommendations based on patterns discerned from existing case data. This functionality proves especially valuable for tasks such as medical diagnostics, legal precedent research, and case strategy formulation. The paper includes an in-depth discussion of the system's methodology, its performance in both medical and legal domains, and its potential for future applications. Our experiments demonstrate that CaseGPT significantly outperforms traditional keyword-based and simple LLM-based systems in terms of precision, recall, and efficiency.
... Frihat et al. [20] apply natural language processing techniques and regression to assess and predict the readability and technicality of abstracts extracted from PubMed documents. The authors propose that these evaluative aspects can be integrated into the information retrieval process to facilitate search results and classify documents relevant to healthcare professionals. ...
The lack of quality in scientific documents affects how documents can be retrieved depending on a user query. Existing search tools for scientific documentation usually retrieve a vast number of documents, of which only a small fraction proves relevant to the user’s query. However, these documents do not always appear at the top of the retrieval process output. This is mainly due to the substantial volume of continuously generated information, which complicates the search and access not properly considering all metadata and content. Regarding document content, the way in which the author structures it and the way the user formulates the query can lead to linguistic differences, potentially resulting in issues of ambiguity between the vocabulary employed by authors and users. In this context, our research aims to address the challenge of evaluating the machine-processing quality of scientific documentation and measure its influence on the processes of indexing and information retrieval. To achieve this objective, we propose a set of indicators and metrics for the construction of the evaluation model. This set of quality indicators have been grouped into three main areas based on the principles of Open Science: accessibility, content, and reproducibility. In this sense, quality is defined as the value that determines whether a document meets the requirements to be retrieved successfully. To prioritize the different indicators, a hierarchical analysis process (AHP) has been carried out with the participation of three referees, obtaining as a result a set of nine weighted indicators. Furthermore, a method to implement the quality model has been designed to support the automatic evaluation of quality and perform the indexing and retrieval process. The impact of quality in the retrieval process has been validated through a case study comprising 120 scientific documents from the field of the computer science discipline and 25 queries, obtaining as a result 21% high, 39% low, and 40% moderate quality.
