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This systematic literature review examines current approaches to automatically generating discharge summaries using Natural Language Processing and Large Language Models. Following Kitchenham guidelines, searches in Scopus and PubMed (2023-2026) found 9 primary studies from 102 initial papers. GPT-4 and variants lead most work, while open-source alternatives such as LLaMA show privacy-preserving promise. Evaluation relies on ROUGE/BLEU plus human assessment, with key risks including hallucinations (33%-64%), omissions, EHR integration hurdles, and context-window limits.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/automatic-generation-of-discharge-summaries-using-large-language-models-a-systematic-literature-review/640674/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/automatic-generation-of-discharge-summaries-using-large-language-models-a-systematic-literature-review/640674.png","ImageObject",300,407,{"name":92,"@type":93},"RizkyPratama_92","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-04","2026-10-22",true,{"@type":102,"interactionType":103,"userInteractionCount":19},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the review address?","Question",{"text":112,"@type":113},"The review addresses the manual burden of creating discharge summaries and the resulting strain on clinical staff and continuity of care.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which models dominate current research according to the review?",{"text":117,"@type":113},"GPT-4 and its variants dominate, appearing in 6 of the 9 included studies, while open-source options like LLaMA show promise for privacy-preserving deployments.",{"name":119,"@type":110,"acceptedAnswer":120},"How are generated discharge summaries evaluated and what challenges are reported?",{"text":121,"@type":113},"Evaluation typically combines automatic metrics (ROUGE, BLEU) with human expert assessment. Challenges include hallucination rates of 33% to 64%, information omission, EHR integration, and context window limitations.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},640674,1793090619,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":19,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":52,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144},962094243231,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Automatic Generation of Discharge Summaries Using Large Language Models: A Systematic Literature Review  \nLucas Molino Piñar, Manuel Carlos Díaz Galiano, María Teresa Martín Valdivia  \nSINAI Research Group, Universidad de Jaén, Spain  \n{lmolino, mcdiaz, [maite}@ujaen.es](maite}@ujaen.es)  \nAbstract  \nDischarge summaries are critical documents for continuity of care, yet their manual creation imposes significant burdens on clinical staff. This systematic literature review examines current approaches to automatic generation of discharge summaries using Natural Language Processing (NLP) and Large Language Models (LLMs) . Following the Kitchenham guidelines for systematic reviews in software engineering, we searched Scopus and PubMed databases for studies published between 2023 and 2026, identifying 9 primary studies from an initial pool of 102 papers. Our analysis reveals that GPT-4 and its variants dominate current research (appearing in 6 of 9 studies), while open-source alternatives like LLaMA show promise for privacy-preserving deployments. Evaluation primarily relies on automatic metrics (ROUGE, BLEU) combined with human expert assessment. Key challenges include hallucination rates ranging from 33% to 64%, information omission, integration with Electronic Health Record (EHR) systems, and context window limitations. Studies addressing factuality employ human-in-the-loop validation, prompt engineering techniques, and knowledge graph-based correction mechanisms. Despite these challenges, recent implementations demonstrate clinical feasibility, with one study achieving a 94 .35% System Usability Score. This review provides a comprehensive synthesis of the state-of-the-art and identifies opportunities for future research in this rapidly evolving field.  \nKeywords: Large Language Models, Discharge Summaries, Natural Language Processing, Clinical Documentation, Systematic Review  \n1. Introduction  \nDischarge summaries are essential clinical documents that communicate critical patient information between healthcare providers, ensuring continuity of care after hospitalization (Kripalani et al. , 2007) . These documents typically include diagnoses, treatments administered, hospital course, and discharge recommendations. However, their manual creation represents a significant burden for clinicians, contributing to documentation fatigue and reducing time available for direct patient care (Sinsky et al. , 2016) .  \nThe emergence of Large Language Models (LLMs) such as GPT-4 (OpenAI , 2023), LLaMA (Touvron et al. , 2023), and their clinical adaptations has created unprecedented opportunities for automating clinical documentation tasks. These transformer-based architectures have demonstrated remarkable capabilities in text generation, summarization, and domain adaptation, making them promising candidates for addressing the discharge summary automation challenge.  \n1.1. Motivation and Scope  \nThe motivation for this systematic review stems from three key observations. First, the clinical documentation burden continues to escalate, with physicians spending up to 50% of their time on documentation tasks. Second, recent advances in LLM tech-  \nnology (2023-2026) have dramatically improved the feasibility of automatic text generation in specialized domains. Third, there is a critical need to understand the current state of research, including both achievements and limitations, to guide future development efforts in clinical NLP.  \nThis review focuses specifically on the automatic generation of discharge summaries intended for healthcare professionals (physician-to-physician communication) . We explicitly exclude studies focusing on patient-friendly simplification of existing summaries, as this represents a distinct NLP task with different objectives. Our temporal scope (2023- 2026) captures the most recent developments in LLM-based clinical text generation, particularly following the release of GPT-4 and similar advanced models.  \n1.2. Objectiv","cbCaig1wByPaW7E2","https://ap.wps.com/l/cbCaig1wByPaW7E2","pdf",302332,"English","# Introduction\n## Motivation and Scope\n## Objectives and Research Questions","[{\"question\":\"What problem does the review address?\",\"answer\":\"The review addresses the manual burden of creating discharge summaries and the resulting strain on clinical staff and continuity of care.\"},{\"question\":\"Which models dominate current research according to the review?\",\"answer\":\"GPT-4 and its variants dominate, appearing in 6 of the 9 included studies, while open-source options like LLaMA show promise for privacy-preserving deployments.\"},{\"question\":\"How are generated discharge summaries evaluated and what challenges are reported?\",\"answer\":\"Evaluation typically combines automatic metrics (ROUGE, BLEU) with human expert assessment. Challenges include hallucination rates of 33% to 64%, information omission, EHR integration, and context window limitations.\"}]","Automatic Generation of Discharge Summaries Using Large Language Models - A Systematic Literature Review | PDF",1792710240,25]