The field of large language models (LLMs) is rapidly advancing, with a focus on improving their performance and reliability in various healthcare applications. Recent developments highlight the potential of LLMs to support clinical decision-making, automate medical text generation, and enhance patient-centered care. Notably, researchers are exploring the use of LLMs to generate structured and clinically grounded text, such as discharge summaries and progress notes, which can reduce the documentation burden on healthcare providers. Additionally, LLMs are being investigated for their ability to predict depression and retinopathy of prematurity risk, as well as to detect forced labor in supply chains. Overall, the field is moving towards more transparent, explainable, and human-centered AI systems that can augment clinical care and improve patient outcomes. Noteworthy papers in this regard include LCDS, which proposes a logic-controlled discharge summary generation system, and MedReadCtrl, which introduces a readability-controlled instruction tuning framework for personalized medical text generation. Furthermore, papers like DocCHA and MedThink-Bench demonstrate the potential of LLMs to support interactive online diagnosis and evaluate medical reasoning capability.
Advances in Large Language Models for Healthcare Applications
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LCDS: A Logic-Controlled Discharge Summary Generation System Supporting Source Attribution and Expert Review
Empowering Healthcare Practitioners with Language Models: Structuring Speech Transcripts in Two Real-World Clinical Applications
Divergent Realities: A Comparative Analysis of Human Expert vs. Artificial Intelligence Based Generation and Evaluation of Treatment Plans in Dermatology
Development and Evaluation of HopeBot: an LLM-based chatbot for structured and interactive PHQ-9 depression screening
CLI-RAG: A Retrieval-Augmented Framework for Clinically Structured and Context Aware Text Generation with LLMs
Medical Red Teaming Protocol of Language Models: On the Importance of User Perspectives in Healthcare Settings
Toward Real-World Chinese Psychological Support Dialogues: CPsDD Dataset and a Co-Evolving Multi-Agent System
Enhancing Vaccine Safety Surveillance: Extracting Vaccine Mentions from Emergency Department Triage Notes Using Fine-Tuned Large Language Models