LEGO®-Maker: Autoregressive Image-Conditioned LEGO® Model Creation

On the role of the UMLS in supporting diagnosis generation proposed by Large Language Models

Background and Aims Large language models (LLMs) have gained significant attention in the field of natural language processing (NLP), marking a shift from traditional techniques like Term Frequency-Inverse Document Frequency (TF-IDF). We developed a traditional NLP model to predict arteriovenous fistula (AVF) failure within next 30 days using clinical notes. The goal of this analysis was to investigate whether LLMs would outperform traditional NLP techniques, specifically in the context of predicting AVF failure within the next 30 days using clinical notes. Method We defined AVF failure as the change in status from active to permanently unusable status or temporarily unusable status. We used data from a large kidney care network from January 2021 to December 2021. Two models were created using LLMs and traditional TF-IDF technique. We used “distilbert-base-uncased”, a distilled version of BERT base model [1], and compared its performance with traditional TF-IDF-based NLP techniques. The dataset was randomly divided into 60% training, 20% validation and 20% test dataset. The test data, comprising of unseen patients’ data was used to evaluate the performance of the model. Both models were evaluated using metrics such as area under the receiver operating curve (AUROC), accuracy, sensitivity, and specificity. Results The incidence of 30 days AVF failure rate was 2.3% in the population. Both LLMs and traditional showed similar overall performance as summarized in Table 1. Notably, LLMs showed marginally better performance in certain evaluation metrics. Both models had same AUROC of 0.64 on test data. The accuracy and balanced accuracy for LLMs were 72.9% and 59.7%, respectively, compared to 70.9% and 59.6% for the traditional TF-IDF approach. In terms of specificity, LLMs scored 73.2%, slightly higher than the 71.2% observed for traditional NLP methods. However, LLMs had a lower sensitivity of 46.1% compared to 48% for traditional NLP. However, it is worth noting that training on LLMs took considerably longer than TF-IDF. Moreover, it also used higher computational resources such as utilization of graphics processing units (GPU) instances in cloud-based services, leading to higher cost. Conclusion In our study, we discovered that advanced LLMs perform comparably to traditional TF-IDF modeling techniques in predicting the failure of AVF. Both models demonstrated identical AUROC. While specificity was higher in LLMs compared to traditional NLP, sensitivity was higher in traditional NLP compared to LLMs. LLM was fine-tuned with a limited dataset, which could have influenced its performance to be similar to that of traditional NLP methods. This finding suggests that while LLMs may excel in certain scenarios, such as performing in-depth sentiment analysis of patient data for complex tasks, their effectiveness is highly dependent on the specific use case. It is crucial to weigh the benefits against the resources required for LLMs, as they can be significantly more resource-intensive and costly compared to traditional TF-IDF methods. This highlights the importance of a use-case-driven approach in selecting the appropriate NLP technique for healthcare applications.

Custom Large Language Models Improve Accuracy: Comparing Retrieval Augmented Generation and Artificial Intelligence Agents to Non-Custom Models for Evidence-Based Medicine

Brief hospital course (BHC) summaries are clinical documents that summarize a patient's hospital stay. While large language models (LLMs) depict remarkable capabilities in automating real-world tasks, their capabilities for healthcare applications such as synthesizing BHCs from clinical notes have not been shown. We introduce a novel preprocessed dataset, the MIMIC-IV-BHC, encapsulating clinical note and BHC pairs to adapt LLMs for BHC synthesis. Furthermore, we introduce a benchmark of the summarization performance of 2 general-purpose LLMs and 3 healthcare-adapted LLMs. Using clinical notes as input, we apply prompting-based (using in-context learning) and fine-tuning-based adaptation strategies to 3 open-source LLMs (Clinical-T5-Large, Llama2-13B, and FLAN-UL2) and 2 proprietary LLMs (Generative Pre-trained Transformer [GPT]-3.5 and GPT-4). We evaluate these LLMs across multiple context-length inputs using natural language similarity metrics. We further conduct a clinical study with 5 clinicians, comparing clinician-written and LLM-generated BHCs across 30 samples, focusing on their potential to enhance clinical decision-making through improved summary quality. We compare reader preferences for the original and LLM-generated summary using Wilcoxon signed-rank tests. We further request optional qualitative feedback from clinicians to gain deeper insights into their preferences, and we present the frequency of common themes arising from these comments. The Llama2-13B fine-tuned LLM outperforms other domain-adapted models given quantitative evaluation metrics of Bilingual Evaluation Understudy (BLEU) and Bidirectional Encoder Representations from Transformers (BERT)-Score. GPT-4 with in-context learning shows more robustness to increasing context lengths of clinical note inputs than fine-tuned Llama2-13B. Despite comparable quantitative metrics, the reader study depicts a significant preference for summaries generated by GPT-4 with in-context learning compared to both Llama2-13B fine-tuned summaries and the original summaries (P<.001), highlighting the need for qualitative clinical evaluation. We release a foundational clinically relevant dataset, the MIMIC-IV-BHC, and present an open-source benchmark of LLM performance in BHC synthesis from clinical notes. We observe high-quality summarization performance for both in-context proprietary and fine-tuned open-source LLMs using both quantitative metrics and a qualitative clinical reader study. Our research effectively integrates elements from the data assimilation pipeline: our methods use (1) clinical data sources to integrate, (2) data translation, and (3) knowledge creation, while our evaluation strategy paves the way for (4) deployment.

Generative large language models (LLMs) are a subset of transformers-based neural network architecture models. LLMs have successfully leveraged a combination of an increased number of parameters, improvements in computational efficiency, and large pre-training datasets to perform a wide spectrum of natural language processing (NLP) tasks. Using a few examples (few-shot) or no examples (zero-shot) for prompt-tuning has enabled LLMs to achieve state-of-the-art performance in a broad range of NLP applications. This article by the American Medical Informatics Association (AMIA) NLP Working Group characterizes the opportunities, challenges, and best practices for our community to leverage and advance the integration of LLMs in downstream NLP applications effectively. This can be accomplished through a variety of approaches, including augmented prompting, instruction prompt tuning, and reinforcement learning from human feedback (RLHF). Our focus is on making LLMs accessible to the broader biomedical informatics community, including clinicians and researchers who may be unfamiliar with NLP. Additionally, NLP practitioners may gain insight from the described best practices. We focus on 3 broad categories of NLP tasks, namely natural language understanding, natural language inferencing, and natural language generation. We review the emerging trends in prompt tuning, instruction fine-tuning, and evaluation metrics used for LLMs while drawing attention to several issues that impact biomedical NLP applications, including falsehoods in generated text (confabulation/hallucinations), toxicity, and dataset contamination leading to overfitting. We also review potential approaches to address some of these current challenges in LLMs, such as chain of thought prompting, and the phenomena of emergent capabilities observed in LLMs that can be leveraged to address complex NLP challenge in biomedical applications.

A comparative study of recent large language models on generating hospital discharge summaries for lung cancer patients.

Self-Admitted Technical Debt (SATD), cases where developers intentionally acknowledge suboptimal solutions in code through comments, poses a significant challenge to software maintainability. Left unresolved, SATD can degrade code quality, increase maintenance costs, and hinder long-term software sustainability. Manually identifying and resolving SATD is time-consuming and error-prone, often competing with feature development and release deadlines. Automating SATD repayment is therefore essential to alleviate developer burden. While Large Language Models (LLMs) have shown promise in tasks like code generation and program repair, their potential in automated SATD repayment remains underexplored. In this paper, we identify three key technical challenges in training and evaluating LLMs for SATD repayment: (1) dataset representativeness and scalability, (2) removal of irrelevant SATD repayment samples, and (3) limitations of existing evaluation metrics, i.e., BLEU and CrystalBLEU. To address the first two dataset-related challenges, we adopt a language-independent SATD tracing tool and design a 10-step filtering pipeline to extract SATD repayments from repository commit histories, leveraging LLM-as-judge for relevance filtering. This results in two new large-scale SATD repayment datasets: 58,722 items for Python and 97,347 items for Java. To improve evaluation, we introduce two diff-based metrics, BLEU-diff and CrystalBLEU-diff, which measure code changes rather than whole code, thereby mitigating the impact of SATD-irrelevant source code. Additionally, we propose a new metric, Line-Level Exact Match on Diff (LEMOD), which is both interpretable and informative, providing fine-grained insights into SATD repayment quality. Using our new benchmarks and evaluation metrics, we evaluate two types of automated SATD repayment methods: fine-tuning smaller models (a few hundred million parameters) and prompt engineering with large-scale models, including four SOTA open-source LLMs and GPT-4o-mini. Our results reveal that while fine-tuned smaller models achieve Exact Match (EM) scores comparable to prompt-based approaches, they underperform on BLEU-based metrics and LEMOD. The best EM performance is achieved by Gemma-2-9B, correctly addressing 10.1% of Python SATDs and 8.1% of Java SATDs with a simple prompt. The only previous study in this domain focused solely on Java, achieving an EM score of 2.3%. When using BLEU-diff, CrystalBLEU-diff, and LEMOD metrics, Llama-3.1-70B-Instruct and GPT-4o-mini deliver the highest performance. While our three proposed metrics demonstrate strong correlations with EM (0.65~0.84), both BLEU and CrystalBLEU show almost no correlation with this intuitive metric (0.01~0.08), highlighting their largely unpredictable behavior when applied directly to the entire code rather than the diff. Our work contributes a robust benchmark, improved evaluation metrics, and a comprehensive evaluation of LLMs, advancing research on automated SATD repayment.

Background:Even though patients have easy access to their electronic health records and lab test results data through patient portals, lab results are often confusing and hard to understand. Many patients turn to online forums or question and answering (Q&A) sites to seek advice from their peers. However, the quality of answers from social Q&A on health-related questions varies significantly, and not all the responses are accurate or reliable. Large language models (LLMs) such as ChatGPT have opened a promising avenue for patients to get their questions answered.Objective:We aim to assess the feasibility of using LLMs to generate relevant, accurate, helpful, and unharmful responses to lab test-related questions asked by patients and to identify potential issues that can be mitigated with augmentation approaches.Methods:We first collected lab test results related question and answer data from Yahoo! Answers and selected 53 Q&A pairs for this study. Using the LangChain framework and ChatGPT web portal, we generated responses to the 53 questions from four LLMs including GPT-4, Meta LLaMA 2, MedAlpaca, and ORCA_mini. We first assessed the similarity of their answers using standard QA similarity-based evaluation metrics including ROUGE, BLEU, METEOR, BERTScore. We also utilized an LLM-based evaluator to judge whether a target model has higher quality in terms of relevance, correctness, helpfulness, and safety than the baseline model. Finally, we performed a manual evaluation with medical experts for all the responses of seven selected questions on the same four aspects.Results:Regarding the similarity of the responses from 4 LLMs, where GPT-4 output was used as the reference answer, the responses from LLaMa 2 are the most similar ones, followed by LLaMa 2, ORCA_mini, and MedAlpaca. Human answers from Yahoo data were scored lowest and thus least similar to GPT-4-generated answers. The results of Win Rate and medical expert evaluation both showed that GPT-4’s responses achieved better scores than all the other LLM responses and human responses on all the four aspects (relevance, correctness, helpfulness, and safety). However, LLM responses occasionally also suffer from lack of interpretation in one’s medical context, incorrect statements, and lack of references.Conclusions:By evaluating LLMs in generating responses to patients’ lab test results related questions, we find that compared to other three LLMs and human answer from the Q&A website, GPT-4’s responses are more accurate, helpful, relevant, and safer. However, there are cases that GPT-4 responses are inaccurate and not individualized. We identified a number of ways to improve the quality of LLM responses including prompt engineering, prompt augmentation, retrieval augmented generation, and response evaluation.

Knowledge base question answering (KBQA) refers to the system that produces answers to user queries by reasoning with a large-scale structured knowledge base. Advanced works have achieved great success either by generating logical forms (LF) or directly generating answers. Although the former typically yields better performance, these generated LF could be inaccurate, e.g., non-executable. In this regard, large language models (LLMs) have shown exciting potential for accurate generation. However, it is challenging to fine-tune LLMs to generate LF. This is because the context retrieved for prediction typically leads to an excessive number of reasoning paths. In this context, LLMs can generate numerous LF corresponding to these reasoning paths, but a few LF can result in correct answers. Thus, fine-tuning LLMs to generate answer-relevant LF would conflict with the prior knowledge of the LLMs. In this work, we propose a novel learning framework, FM-KBQA, to fine-tune LLMs using multi-task learning for KBQA. Specifically, we propose to fine-tune LLMs using an additional objective: generating the index of reasoning paths that lead to correct answers. This will direct LLMs to pay attention to answer-relevant paths among numerous reasoning paths by completing a simple task where the selected reasoning paths can be supplementary for non-executable LF. Directly generating answers can make LLMs pay attention to the answer-relevant reasoning paths, but it is much more challenging than generating the index of reasoning paths. To verify FM-KBQA's effectiveness, we conduct experiments on mainstream benchmarks, such as WebQuestionsSP (WQSP) and ComplexWebQuestions (CWQ). Extensive evaluations across two public benchmark datasets underscore the superiority of FM-KBQA over current state-of-the-art methods.

Forgetting is the complementary process to memory, enabling both biological and artificial systems to prioritize relevant information while discarding outdated or redundant data. In large language models (LLMs), forgetting occurs through computational mechanisms such as attention bottlenecks, catastrophic interference, and optimization dynamics, mirroring neurobiological processes such as synaptic decay and memory consolidation. While existing research has extensively studied memory mechanisms in LLMs, there remains a critical gap in systematically analyzing forgetting phenomena and their implications for AI system design. This study presents a comprehensive review of forgetting mechanisms in LLMs through a neuroscience-inspired lens. This study first establishes parallels between human forgetting processes and computational forgetting patterns in LLMs. Building on these foundations, this study proposes a novel taxonomy that classifies LLM forgetting across four key dimensions. This taxonomy is further supported by the introduction of quantitative evaluation metrics to provide additional evidence. Furthermore, this study demonstrates how controlled forgetting can be transformed from a limitation into a design feature through bio-inspired optimization strategies. These approaches enable critical applications in privacy-compliant AI, adaptive education systems, and sustainable computing. The study concludes by identifying open challenges in generalization, ethical governance of machine forgetting, and computational efficiency, while outlining future directions for developing LLMs with human-like memory adaptability.

Serious Bacterial Infection (SBI) in neonates and young infants often exhibit nonspecific symptoms and clinical signs in the early stages of illness, making early diagnosis challenging. Timely recognition and appropriate treatment are essential to prevent adverse outcomes. While several clinical algorithms are widely used for SBI risk stratification, these tools have limitations, particularly low positive predictive value. This study evaluates the diagnostic accuracy of general-purpose large language models (LLMs) in detecting SBI in neonates and infants under 90 days of age admitted to the emergency department. Our objective is to improve diagnostic precision, reduce unnecessary interventions, and enhance patient outcomes. LLM performance was compared against traditional machine learning models, state-of-the-art rule-based methods, and an ensemble of physicians to assess their potential as clinical decision-support tools in scenarios of diagnostic uncertainty. On a dataset of 742 patients, LLMs demonstrated diagnostic accuracy comparable to traditional machine learning models and state-of-the-art rule-based methods. The optimized CatBoost (class-weighted) model achieved the best overall performance, with a PPV of 0.70, NPV of 0.90, sensitivity of 0.54, specificity of 0.95, F1-score of 0.60, and MCC of 0.54, outperforming the baseline CatBoost model and achieving results on par with large language models (LLMs) and physicians. When optimally prompted, LLMs performed on par with ensembles of experienced clinicians. Additionally, LLMs exhibited effective medical reasoning and provided credible diagnostic predictions, particularly valuable in cases of clinician uncertainty. The models achieved balanced performance across multiple evaluation metrics, including PPV, NPV, sensitivity, specificity, F1-score, and Matthew’s correlation coefficient (MCC). ChatGPT-4o achieved a sensitivity of 0.65 and specificity of 0.83, with an MCC of 0.41. Claude Sonnet 3.5 reached a sensitivity of 0.60 and specificity of 0.86, MCC 0.42 and Google Gemini 2.0 Flash had lower sensitivity (0.43) but the highest specificity (0.94), with an MCC of 0.43. In comparison, the best-performing individual pediatrician achieved a higher sensitivity (0.74) but lower specificity (0.68), with an MCC of 0.33, while the pediatricians’ majority vote yielded sensitivity of 0.69, specificity of 0.81, and MCC of 0.43 — comparable to the top-performing LLMs. These Artificial intelligence tools offer a promising direction for SBI risk prediction, achieving performance comparable to that of experienced pediatric specialists, while maintaining simplicity of use/data-preprocessing for potential real-world applications.

Development and evaluation of a retrieval-augmented large language model framework for enhancing endodontic education.

341P Artificial intelligence-supported early detection of lung cancer from chest x-ray in routine clinical practice: Real-world, multicenter study across Czech hospitals

ObjectivesThis study evaluated the efficacy of integrating a retrieval-augmented generation (RAG) model and a large language model (LLM) to improve the accuracy of drug name mapping across international vocabularies.MethodsDrug ingredient names were translated into English using the Japanese Accepted Names for Pharmaceuticals. Drug concepts were extracted from the standard vocabulary of OHDSI, and the accuracy of mappings between translated terms and RxNorm was assessed by vector similarity, using the BioBERT-generated embedded vectors as the baseline. Subsequently, we developed LLMs with RAG that distinguished the final candidates from the baseline. We assessed the efficacy of the LLM with RAG in candidate selection by comparing it with conventional methods based on vector similarity.ResultsThe evaluation metrics demonstrated the superior performance of the combined LLM + RAG over traditional vector similarity methods. Notably, the hit rates of the Mixtral 8x7b and GPT-3.5 models exceeded 90%, significantly outperforming the baseline rate of 64% across stratified groups of PO drugs, injections, and all interventions. Furthermore, the r-precision metric, which measures the alignment between model judgment and human evaluation, revealed a notable improvement in LLM performance, ranging from 41% to 50% compared to the baseline of 23%.ConclusionsIntegrating an RAG and an LLM outperformed conventional string comparison and embedding vector similarity techniques, offering a more refined approach to global drug information mapping.

The paper introduces a Knowledge Retrieval-Based Intelligent Question and Answer Generation Framework for Education, leveraging Retrieval Augmented Generation (RAG) to enhance Large Language Models (LLMs) in producing high-quality, contextually relevant examination questions and answers across subjects. The framework addresses challenges in ensuring comprehensive subject coverage, educational standards, and evaluation metrics. Key components include Optical Character Recognition (OCR), data chunking, vectorization using multilingual BERT, and custom retrieval functions. The RAG system's effectiveness is evaluated using the RAGAs metric, covering metrics like faithfulness, answer relevancy, context recall, and context precision. Comparative results reveal OpenAI models surpassing Google's Gemini in coherence and context relevance due to differing architectures and training methods. Concluding, the paper highlights potential for both large and small language models in tailored educational applications, noting limitations such as hallucinations, high resource demands, and contextual drift.