Healthcare Informatics Research最新文献

{"title":"Generative Pre-trained Transformer: Trends, Applications, Strengths and Challenges in Dentistry: A Systematic Review.","authors":"Sibyl Siluvai, Vivek Narayanan, Vinoo Subramaniam Ramachandran, Victor Rakesh Lazar","doi":"10.4258/hir.2025.31.2.189","DOIUrl":"10.4258/hir.2025.31.2.189","url":null,"abstract":"<p><strong>Objectives: </strong>The integration of large language models (LLMs), particularly those based on the generative pre-trained transformer (GPT) architecture, has begun to revolutionize various fields, including dentistry. Despite these promising applications, the use of GPT in dentistry presents several challenges. Ongoing research and the development of robust ethical frameworks are essential to mitigate these issues and enhance the responsible deployment of GPT technologies in clinical settings. Hence, this systematic review aims to explore the trends, applications, strengths, and challenges associated with the use of GPT in dentistry.</p><p><strong>Methods: </strong>Articles were selected if they contained detailed information on the application of GPT in dentistry. The search strategy used in systematic reviews follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our search of databases and other sources yielded a total of 704 studies. After removing duplicates and conducting a full-text screening, 16 articles were included in the review. The methodological quality of the research was evaluated using the Critical Appraisal Skills Programme (CASP) checklist.</p><p><strong>Results: </strong>Out of a total of 91 articles published on GPT in dentistry, 20 were editorials and 11 were narrative reviews; these were excluded, leaving 60 original research articles for further analysis. The articles were assessed based on the type of results they provided. Ultimately, 16 articles that reported positive findings with robust methodology were included in this review.</p><p><strong>Conclusions: </strong>The results highlight mixed responses; therefore, further research on integration into clinical workflows must be conducted with extensive methodological rigor.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"31 2","pages":"189-199"},"PeriodicalIF":2.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12086441/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144093377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large Language Models in Medicine.","authors":"Jinwook Choi","doi":"10.4258/hir.2025.31.2.111","DOIUrl":"10.4258/hir.2025.31.2.111","url":null,"abstract":"","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"31 2","pages":"111-113"},"PeriodicalIF":2.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12086435/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144093449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large Language Models in Medicine: Clinical Applications, Technical Challenges, and Ethical Considerations.","authors":"Kyu-Hwan Jung","doi":"10.4258/hir.2025.31.2.114","DOIUrl":"10.4258/hir.2025.31.2.114","url":null,"abstract":"<p><strong>Objectives: </strong>This study presents a comprehensive review of the clinical applications, technical challenges, and ethical considerations associated with using large language models (LLMs) in medicine.</p><p><strong>Methods: </strong>A literature survey of peer-reviewed articles, technical reports, and expert commentary from relevant medical and artificial intelligence journals was conducted. Key clinical application areas, technical limitations (e.g., accuracy, validation, transparency), and ethical issues (e.g., bias, safety, accountability, privacy) were identified and analyzed.</p><p><strong>Results: </strong>LLMs have potential in clinical documentation assistance, decision support, patient communication, and workflow optimization. The level of supporting evidence varies; documentation support applications are relatively mature, whereas autonomous diagnostics continue to face notable limitations regarding accuracy and validation. Key technical challenges include model hallucination, lack of robust clinical validation, integration issues, and limited transparency. Ethical concerns involve algorithmic bias risking health inequities, threats to patient safety from inaccuracies, unclear accountability, data privacy, and impacts on clinician-patient interactions.</p><p><strong>Conclusions: </strong>LLMs possess transformative potential for clinical medicine, particularly by augmenting clinician capabilities. However, substantial technical and ethical hurdles necessitate rigorous research, validation, clearly defined guidelines, and human oversight. Existing evidence supports an assistive rather than autonomous role, mandating careful, evidence-based integration that prioritizes patient safety and equity.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"31 2","pages":"114-124"},"PeriodicalIF":3.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12086438/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144093451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing Korean Medical Large Language Models: Automated Pipeline for Korean Medical Preference Dataset Construction.","authors":"Jean Seo, Sumin Park, Sungjoo Byun, Jinwook Choi, Jinho Choi, Hyopil Shin","doi":"10.4258/hir.2025.31.2.166","DOIUrl":"10.4258/hir.2025.31.2.166","url":null,"abstract":"<p><strong>Objectives: </strong>Developing large language models (LLMs) in biomedicine requires access to high-quality training and alignment tuning datasets. However, publicly available Korean medical preference datasets are scarce, hindering the advancement of Korean medical LLMs. This study constructs and evaluates the efficacy of the Korean Medical Preference Dataset (KoMeP), an alignment tuning dataset constructed with an automated pipeline, minimizing the high costs of human annotation.</p><p><strong>Methods: </strong>KoMeP was generated using the DAHL score, an automated hallucination evaluation metric. Five LLMs (Dolly-v2-3B, MPT-7B, GPT-4o, Qwen-2-7B, Llama-3-8B) produced responses to 8,573 biomedical examination questions, from which 5,551 preference pairs were extracted. Each pair consisted of a \"chosen\" response and a \"rejected\" response, as determined by their DAHL scores. The dataset was evaluated when trained through two different alignment tuning methods, direct preference optimization (DPO) and odds ratio preference optimization (ORPO) respectively across five different models. The KorMedMCQA benchmark was employed to assess the effectiveness of alignment tuning.</p><p><strong>Results: </strong>Models trained with DPO consistently improved KorMedMCQA performance; notably, Llama-3.1-8B showed a 43.96% increase. In contrast, ORPO training produced inconsistent results. Additionally, English-to-Korean transfer learning proved effective, particularly for English-centric models like Gemma-2, whereas Korean-to-English transfer learning achieved limited success. Instruction tuning with KoMeP yielded mixed outcomes, which suggests challenges in dataset formatting.</p><p><strong>Conclusions: </strong>KoMeP is the first publicly available Korean medical preference dataset and significantly improves alignment tuning performance in LLMs. The DPO method outperforms ORPO in alignment tuning. Future work should focus on expanding KoMeP, developing a Korean-native dataset, and refining alignment tuning methods to produce safer and more reliable Korean medical LLMs.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"31 2","pages":"166-174"},"PeriodicalIF":2.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12086433/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144093370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing an Explainable Artificial Intelligence System for the Mobile-Based Diagnosis of Febrile Diseases Using Random Forest, LIME, and GPT.","authors":"Kingsley F Attai, Constance Amannah, Moses Ekpenyong, Daniel E Asuquo, Oryina K Akputu, Okure U Obot, Peterben C Ajuga, Jeremiah C Obi, Omosivie Maduka, Christie Akwaowo, Faith-Michael Uzoka","doi":"10.4258/hir.2025.31.2.125","DOIUrl":"10.4258/hir.2025.31.2.125","url":null,"abstract":"<p><strong>Objectives: </strong>This study proposes a mobile-based explainable artificial intelligence (XAI) platform designed for diagnosing febrile illnesses.</p><p><strong>Methods: </strong>We integrated the interpretability offered by local interpretable model-agnostic explanations (LIME) and the explainability provided by generative pre-trained transformers (GPT) to bridge the gap in understanding and trust often created by machine learning models in critical healthcare decision-making. The developed system employed random forest for disease diagnosis, LIME for interpretation of the results, and GPT-3.5 for generating explanations in easy-to-understand language.</p><p><strong>Results: </strong>Our model demonstrated robust performance in detecting malaria, achieving precision, recall, and F1-scores of 85%, 91%, and 88%, respectively. It performed moderately well in detecting urinary tract and respiratory tract infections, with precision, recall, and F1-scores of 80%, 65%, and 72%, and 77%, 68%, and 72%, respectively, maintaining an effective balance between sensitivity and specificity. However, the model exhibited limitations in detecting typhoid fever and human immunodeficiency virus/acquired immune deficiency syndrome, achieving lower precision, recall, and F1-scores of 69%, 53%, and 60%, and 75%, 39%, and 51%, respectively. These results indicate missed true-positive cases, necessitating further model fine-tuning. LIME and GPT-3.5 were integrated to enhance transparency and provide natural language explanations, thereby aiding decision-making and improving user comprehension of the diagnoses.</p><p><strong>Conclusions: </strong>The LIME plots revealed key symptoms influencing the diagnoses, with bitter taste in the mouth and fever showing the highest negative influence on predictions, and GPT-3.5 provided natural language explanations that increased the reliability and trustworthiness of the system, promoting improved patient outcomes and reducing the healthcare burden.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"31 2","pages":"125-135"},"PeriodicalIF":2.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12086442/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144093374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LLM-Based Response Generation for Korean Adolescents: A Study Using the NAVER Knowledge iN Q&A Dataset with RAG.","authors":"Junseo Kim, Seok Jun Kim, Junseok Ahn, Suehyun Lee","doi":"10.4258/hir.2025.31.2.136","DOIUrl":"10.4258/hir.2025.31.2.136","url":null,"abstract":"<p><strong>Objectives: </strong>This research aimed to develop a retrieval-augmented generation (RAG) based large language model (LLM) system that offers personalized and reliable responses to a wide range of concerns raised by Korean adolescents. Our work focuses on building a culturally reflective dataset and on designing and validating the system's effectiveness by comparing the answer quality of RAG-based models with non-RAG models.</p><p><strong>Methods: </strong>Data were collected from the NAVER Knowledge iN platform, concentrating on posts that featured adolescents' questions and corresponding expert responses during the period 2014-2024. The dataset comprises 3,874 cases, categorized by key negative emotions and the primary sources of worry. The data were processed to remove irrelevant or redundant content and then classified into general and detailed causes. The RAG-based model employed FAISS for similarity-based retrieval of the top three reference cases and used GPT-4o mini for response generation. The responses generated with and without RAG were evaluated using several metrics.</p><p><strong>Results: </strong>RAG-based responses outperformed non-RAG responses across all evaluation metrics. Key findings indicate that RAG-based responses delivered more specific, empathetic, and actionable guidance, particularly when addressing complex emotional and situational concerns. The analysis revealed that family relationships, peer interactions, and academic stress are significant factors affecting adolescents' worries, with depression and stress frequently co-occurring.</p><p><strong>Conclusions: </strong>This study demonstrates the potential of RAG-based LLMs to address the diverse and culture-specific worries of Korean adolescents. By integrating external knowledge and offering personalized support, the proposed system provides a scalable approach to enhancing mental health interventions for adolescents. Future research should concentrate on expanding the dataset and improving multiturn conversational capabilities to deliver even more comprehensive support.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"31 2","pages":"136-145"},"PeriodicalIF":2.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12086440/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144093452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data Mining to Identify the Right Interventions for the Right Patient for Heart Failure: A Real-World Study.","authors":"Keni Lee, Ramzi Argoubi, Halley Costantino","doi":"10.4258/hir.2025.31.1.66","DOIUrl":"10.4258/hir.2025.31.1.66","url":null,"abstract":"<p><strong>Objectives: </strong>To identify the right interventions for the right heart failure (HF) patients in the real-world setting using machine learning (ML) trained on individual-level clinical data linked with social determinants of health (SDOH) data.</p><p><strong>Methods: </strong>In this retrospective cohort study, point-of-care claims data from Komodo Health and SDOH data from the National Health and Wellness Survey (NHWS), from January 2014-December 2020, were linked. Data mining was conducted using K-means clustering, an ML tool. Komodo Health data were used to access longitudinal data for the selected patient cohorts and crosssectional data from NHWS for additional patient information. The primary outcome was HF-related hospitalizations; secondary outcomes, all-cause hospitalization and all-cause mortality. Use of digital healthcare (DHC)/non-DHC interventions and related outcomes were also assessed.</p><p><strong>Results: </strong>The study population included 353 HF patients (mean age, 63.5 years; 57.2% women). The use of non-DHC (75.9%-81.9%) and DHC (4.0%-9.1%) interventions increased from baseline to followup. Overall, 17.0% of patients had HF-related hospitalizations (DHC, 6.9%; non-DHC, 16.5%) and 45.0% had all-cause hospitalization (DHC, 75.0%; non-DHC, 50.9%). Two archetypes with distinct patient profiles were identified. Archetype 1 (vs. 2) characterised by older age, greater disease severity, more comorbidities, more medication use, took steps to prevent heart attack/problems, had better lifestyle, higher HF-related hospitalizations (18.3% vs. 16.3%) and lower all-cause hospitalizations (42.9% vs. 46.3%). The trends remained the same regardless of the intervention type.</p><p><strong>Conclusions: </strong>Identification of patient archetypes with distinct profiles can be useful to understand underlying disease subtypes, identify specific interventions, predict clinical outcomes, and define the right intervention for the right patient.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"31 1","pages":"66-87"},"PeriodicalIF":2.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11854618/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143457610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utility of Treatment Pattern Analysis Using a Common Data Model: A Scoping Review.","authors":"Eun-Gee Park, Min Jung Kim, Jinseo Kim, Kichul Shin, Borim Ryu","doi":"10.4258/hir.2025.31.1.4","DOIUrl":"10.4258/hir.2025.31.1.4","url":null,"abstract":"<p><strong>Objectives: </strong>We aimed to derive observational research evidence on treatment patterns through a scoping review of common data model (CDM)-based publications.</p><p><strong>Methods: </strong>We searched the medical literature databases PubMed and EMBASE, as well as the Observational Health Data Sciences and Informatics (OHDSI) website, for papers published between January 1, 2010 and August 21, 2023 to identify research papers relevant to our topic.</p><p><strong>Results: </strong>Eighteen articles satisfied the inclusion criteria for this scoping review. We summarized study characteristics such as phenotypes, patient numbers, data periods, countries, Observational Medical Outcomes Partnership (OMOP) CDM databases, and definitions of index date and target cohort. Type 2 diabetes mellitus emerged as the most frequently studied disease, covered in five articles, followed by hypertension and depression, each addressed in four articles. Biguanides, with metformin as the primary drug, were the most commonly prescribed first-line treatments for type 2 diabetes mellitus. Most studies utilized sunburst plots to visualize treatment patterns, whereas two studies used Sankey plots. Various software tools were employed for treatment pattern analysis, including JavaScript, the open-source ATLAS by OHDSI, R code, and the R package \"TreatmentPatterns.\"</p><p><strong>Conclusions: </strong>This study provides a comprehensive overview of research on treatment patterns using the CDM, highlighting the growing importance of OMOP CDM in enabling multinational observational network studies and advancing collaborative research in this field.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"31 1","pages":"4-15"},"PeriodicalIF":2.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11854637/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143457776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interactive Engagement with Self-Paced Learning Content in a Didactic Course.","authors":"Nazlee Sharmin, Shahram Houshyar, Thomas R Stevenson, Ava K Chow","doi":"10.4258/hir.2025.31.1.96","DOIUrl":"10.4258/hir.2025.31.1.96","url":null,"abstract":"<p><strong>Objectives: </strong>A growing number of health professional institutions around the world are embracing innovative technologies to increase student engagement, primarily to improve clinical and simulated learning experiences. Didactic learning is an essential component of dental and medical curricula. However, limited research is available regarding the implementation of technology-infused teaching in classroom settings. We developed self-paced interactive learning content using the HTML5 Package (H5P) to promote student engagement in a didactic course within a dental hygiene program.</p><p><strong>Methods: </strong>A total of 52 interactive artifacts were created and administered to students as supplementary learning material. A descriptive study was conducted to explore student perceptions and engagement with the H5P content, as well as to evaluate the impact of these artifacts on academic performance.</p><p><strong>Results: </strong>Students performed significantly better on exam questions associated with interactive H5P content posted in the learning management system compared to other questions. Most students were highly engaged with the H5P content during the week leading up to each summative assessment. However, two of the three students with the highest course grades demonstrated consistent engagement with this content throughout the course.</p><p><strong>Conclusions: </strong>Our results highlight the effectiveness of interactive content created using the H5P platform in fostering student engagement. The development of self-paced interactive materials may benefit various aspects of didactic teaching, including both synchronous and asynchronous online learning.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"31 1","pages":"96-106"},"PeriodicalIF":2.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11854625/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143457641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Review of the 2024 Fall Conference of the Korean Society of Medical Informatics-AI's Role in Shaping Modern Healthcare.","authors":"Jisan Lee, Taehoon Ko, Kwangmo Yang, Younghee Lee","doi":"10.4258/hir.2025.31.1.1","DOIUrl":"10.4258/hir.2025.31.1.1","url":null,"abstract":"","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"31 1","pages":"1-3"},"PeriodicalIF":2.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11854613/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143457695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}