Challenge: Proprietary Large Language Models (LLMs) have demonstrated promising capabilities in clinical text summarization tasks.
Approach: They propose a domain- and task-specific adaptation process for an open-source LLaMA-2 model . LLama-2 can generate high-quality clinical notes from outpatient patient-doctor dialogues .
Outcome: The proposed model can generate clinical notes comparable to those authored by physicians.

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Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes (2024.findings-acl)

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Challenge: Clinical notes are an extensive repository of information specific to individual patients.
Approach: They create synthetic large-scale clinical notes using publicly available case reports extracted from biomedical literature and train a clinical large language model, Asclepius.
Outcome: The proposed model outperforms several other models and is supported by detailed evaluations conducted by GPT-4 and medical professionals.
LlamaCare: An Instruction Fine-Tuned Large Language Model for Clinical NLP (2024.lrec-main)

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Challenge: Large language models have shown remarkable abilities in generating natural texts . applying LLMs to clinical domain still poses significant challenges .
Approach: They propose a method of instruction fine-tuning for adapting large language models to clinical domains . they generate instructions, inputs, and outputs covering a wide spectrum of clinical services .
Outcome: The proposed method outperforms baseline LLMs on clinical tasks . it requires domain adaptation, task-specific learning, and reliability .
Are LLMs reliable? An exploration of the reliability of large language models in clinical note generation (2025.acl-industry)

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Challenge: Clinical note generation (CNG) tools are being developed to address extended working hours and healthcare provider fatigue.
Approach: They evaluate the reliability of 12 open-weight and proprietary LLMs from Anthropic, Meta, Mistral, and OpenAI in CNG in terms of their ability to generate notes that are string equivalent (consistency rate), have the same meaning (semantic consistency) and are correct (symbol similarity)
Outcome: The results show that the LLMs generated notes that are string equivalent (consistency rate), have the same meaning (semantic consistency) and are correct (symbol similarity) overall, Meta’s Llama 70B was the most reliable, followed by Mistral’s Small model.
SYNFAC-EDIT: Synthetic Imitation Edit Feedback for Factual Alignment in Clinical Summarization (2024.emnlp-main)

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Challenge: Large Language Models (LLMs) struggle with factual inaccuracies, a critical issue in clinical NLP applications where errors could lead to serious consequences.
Approach: They propose a pipeline that leverages >100B parameter GPT variants to act as synthetic experts to generate edit feedback without additional human annotations.
Outcome: The proposed pipeline aims to improve the quality of clinical note summarizations by generating edit feedback without human annotations.
Large Language Models Are Poor Clinical Decision-Makers: A Comprehensive Benchmark (2024.emnlp-main)

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Challenge: Existing studies focus on evaluating large language models in close-ended QA tasks, but many clinical decisions involve answering open-ended questions without pre-set options.
Approach: They construct a benchmark to better understand large language models in the clinic . they use existing datasets to evaluate LLMs in clinical situations .
Outcome: The proposed model outperforms human experts in multiple medical tasks.
A Modular Approach for Clinical SLMs Driven by Synthetic Data with Pre-Instruction Tuning, Model Merging, and Clinical-Tasks Alignment (2025.acl-long)

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Challenge: Large language models such as GPT-4 have limited their deployment in clinical settings . a novel framework for adapting SLMs into high-performing clinical models is needed .
Approach: They propose a framework for adapting large language models into high-performing clinical models . they pre-instruct experts on relevant medical and clinical corpora and model merging .
Outcome: The proposed framework outperforms the existing model on the CLUE+ benchmark on medical entities and radiology reports.
Exploring LLM Annotation for Adaptation of Clinical Information Extraction Models under Data-sharing Restrictions (2025.findings-acl)

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Challenge: In-hospital text data often contains valuable clinical information, yet fine-tuned small language models (SLMs) for information extraction remain challenging due to differences in formatting and vocabulary across institutions.
Approach: They leverage large language models to annotate the target domain data for adaptation . they use in-hospital text data to extract clinical information .
Outcome: The proposed model outperforms manual annotation on four clinical information extraction tasks with a larger number of annotated data.
Beyond Fine-tuning: Unleashing the Potential of Continuous Pretraining for Clinical LLMs. (2024.findings-emnlp)

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Challenge: Current approaches to adapting large language models to clinical use-cases are limited.
Approach: They investigate the efficacy of four techniques in adapting large language models for clinical use-cases.
Outcome: The proposed techniques show that they improve performance across clinical tasks.
Process-Supervised Reward Models for Verifying Clinical Note Generation: A Scalable Approach Guided by Domain Expertise (2025.emnlp-main)

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Challenge: Currently, no automated, scalable method exists to evaluate the quality of LLM-generated clinical notes, leaving manual evaluation the gold standard.
Approach: They propose a framework for training PRMs to deliver step-level reward signals for LLM-generated clinical notes.
Outcome: The proposed framework outperforms reasoning and non-reasoning models on key evaluations and selects physician-preferred clinical notes with 56.2% accuracy.
Empowering Healthcare Practitioners with Language Models: Structuring Speech Transcripts in Two Real-World Clinical Applications (2025.emnlp-industry)

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Challenge: Large language models (LLMs) have demonstrated strong performance on clinical natural language processing tasks across multiple medical benchmarks.
Approach: They propose an agentic pipeline for generating realistic, non-sensitive nurse dictations, enabling structured extraction of clinical observations.
Outcome: The proposed pipeline generates realistic, non-sensitive nurse dictations, enabling structured extraction of clinical observations.

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