TRANSFORMING BINARY DEPRESSIVE DATA INTO SEVERITY INSIGHTS THROUGH AUTOMATIC CLUSTERING AND LABELLING

The use of web-based search and social media can help identify epidemics, potentially earlier than clinical methods or even potentially identifying unreported outbreaks. Monitoring for eye-related epidemics, such as conjunctivitis outbreaks, can facilitate early public health intervention to reduce transmission and ocular comorbidities. However, monitoring social media content for conjunctivitis outbreaks is costly and laborious. Large language models (LLMs) could overcome these barriers by assessing the likelihood that real-world outbreaks are being described. However, public health actions for likely outbreaks could benefit more by knowing additional epidemiological characteristics, such as outbreak type, size, and severity. We aimed to assess whether and how well LLMs can classify epidemiological features from social media posts beyond conjunctivitis outbreak probability, including outbreak type, size, severity, etiology, and community setting. We used a validation framework comparing LLM classifications to those of other LLMs and human experts. We wrote code to generate synthetic conjunctivitis outbreak social media posts, embedded with specific preclassified epidemiological features to simulate various infectious eye disease outbreak and control scenarios. We used these posts to develop effective LLM prompts and test the capabilities of multiple LLMs. For top-performing LLMs, we gauged their practical utility in real-world epidemiological surveillance by comparing their assessments of Twitter/X, forum, and YouTube conjunctivitis posts. Finally, human raters also classified the posts, and we compared their classifications to those of a leading LLM for validation. Comparisons entailed correlation or sensitivity and specificity statistics. We assessed 7 LLMs for effectively classifying epidemiological data from 1152 synthetic posts, 370 Twitter/X posts, 290 forum posts, and 956 YouTube posts. Despite some discrepancies, the LLMs demonstrated a reliable capacity for nuanced epidemiological analysis across various data sources and compared to humans or between LLMs. Notably, GPT-4 and Mixtral 8x22b exhibited high performance, predicting conjunctivitis outbreak characteristics such as probability (GPT-4: correlation=0.73), size (Mixtral 8x22b: correlation=0.82), and type (infectious, allergic, or environmentally caused); however, there were notable exceptions. Assessing synthetic and real-world posts for etiological factors, infectious eye disease specialist validations revealed that GPT-4 had high specificity (0.83-1.00) but variable sensitivity (0.32-0.71). Interrater reliability analyses showed that LLM-expert agreement exceeded expert-expert agreement for severity assessment (intraclass correlation coefficient=0.69 vs 0.38), while agreement varied by condition type (κ=0.37-0.94). This investigation into the potential of LLMs for public health infoveillance suggests effectiveness in classifying key epidemiological characteristics from social media content about conjunctivitis outbreaks. Future studies should further explore LLMs' potential to support public health monitoring through the automated assessment and classification of potential infectious eye disease or other outbreaks. Their optimal role may be to act as a first line of documentation, alerting public health organizations for the follow-up of LLM-detected and -classified small, early outbreaks, with a focus on the most severe ones.

BACKGROUND The use of web-based search and social media can help identify epidemics, potentially earlier than clinical methods or even potentially identifying unreported outbreaks. Monitoring for eye-related epidemics, such as conjunctivitis outbreaks, can facilitate early public health intervention to reduce transmission and ocular comorbidities. However, monitoring social media content for conjunctivitis outbreaks is costly and laborious. Large language models (LLMs) could overcome these barriers by assessing the likelihood that real-world outbreaks are being described. However, public health actions for likely outbreaks could benefit more by knowing additional epidemiological characteristics, such as outbreak type, size, and severity. OBJECTIVE We aimed to assess whether and how well LLMs can classify epidemiological features from social media posts beyond conjunctivitis outbreak probability, including outbreak type, size, severity, etiology, and community setting. We used a validation framework comparing LLM classifications to those of other LLMs and human experts. METHODS We wrote code to generate synthetic conjunctivitis outbreak social media posts, embedded with specific preclassified epidemiological features to simulate various infectious eye disease outbreak and control scenarios. We used these posts to develop effective LLM prompts and test the capabilities of multiple LLMs. For top-performing LLMs, we gauged their practical utility in real-world epidemiological surveillance by comparing their assessments of Twitter/X, forum, and YouTube conjunctivitis posts. Finally, human raters also classified the posts, and we compared their classifications to those of a leading LLM for validation. Comparisons entailed correlation or sensitivity and specificity statistics. RESULTS We assessed 7 LLMs for effectively classifying epidemiological data from 1152 synthetic posts, 370 Twitter/X posts, 290 forum posts, and 956 YouTube posts. Despite some discrepancies, the LLMs demonstrated a reliable capacity for nuanced epidemiological analysis across various data sources and compared to humans or between LLMs. Notably, GPT-4 and Mixtral 8x22b exhibited high performance, predicting conjunctivitis outbreak characteristics such as probability (GPT-4: correlation=0.73), size (Mixtral 8x22b: correlation=0.82), and type (infectious, allergic, or environmentally caused); however, there were notable exceptions. Assessing synthetic and real-world posts for etiological factors, infectious eye disease specialist validations revealed that GPT-4 had high specificity (0.83-1.00) but variable sensitivity (0.32-0.71). Interrater reliability analyses showed that LLM-expert agreement exceeded expert-expert agreement for severity assessment (intraclass correlation coefficient=0.69 vs 0.38), while agreement varied by condition type (κ=0.37-0.94). CONCLUSIONS This investigation into the potential of LLMs for public health infoveillance suggests effectiveness in classifying key epidemiological characteristics from social media content about conjunctivitis outbreaks. Future studies should further explore LLMs’ potential to support public health monitoring through the automated assessment and classification of potential infectious eye disease or other outbreaks. Their optimal role may be to act as a first line of documentation, alerting public health organizations for the follow-up of LLM-detected and -classified small, early outbreaks, with a focus on the most severe ones.

BackgroundPopularized by ChatGPT, large language models (LLMs) are poised to transform the scalability of clinical natural language processing (NLP) downstream tasks such as medical question answering (MQA) and automated data extraction from clinical narrative reports. However, the use of LLMs in the health care setting is limited by cost, computing power, and patient privacy concerns. Specifically, as interest in LLM-based clinical applications grows, regulatory safeguards must be established to avoid exposure of patient data through the public domain. The use of open-source LLMs deployed behind institutional firewalls may ensure the protection of private patient data. In this study, we evaluated the extraction performance of a locally deployed LLM for automated MQA from surgical pathology reports.ObjectiveWe compared the performance of human reviewers and a locally deployed LLM tasked with extracting key histologic and staging information from surgical pathology reports.MethodsA total of 84 thyroid cancer surgical pathology reports were assessed by two independent reviewers and the open-source FastChat-T5 3B-parameter LLM using institutional computing resources. Longer text reports were split into 1200-character-long segments, followed by conversion to embeddings. Three segments with the highest similarity scores were integrated to create the final context for the LLM. The context was then made part of the question it was directed to answer. Twelve medical questions for staging and thyroid cancer recurrence risk data extraction were formulated and answered for each report. The time to respond and concordance of answers were evaluated. The concordance rate for each pairwise comparison (human-LLM and human-human) was calculated as the total number of concordant answers divided by the total number of answers for each of the 12 questions. The average concordance rate and associated error of all questions were tabulated for each pairwise comparison and evaluated with two-sided t tests.ResultsOut of a total of 1008 questions answered, reviewers 1 and 2 had an average (SD) concordance rate of responses of 99% (1%; 999/1008 responses). The LLM was concordant with reviewers 1 and 2 at an overall average (SD) rate of 89% (7%; 896/1008 responses) and 89% (7.2%; 903/1008 responses). The overall time to review and answer questions for all reports was 170.7, 115, and 19.56 minutes for Reviewers 1, 2, and the LLM, respectively.ConclusionsThe locally deployed LLM can be used for MQA with considerable time-saving and acceptable accuracy in responses. Prompt engineering and fine-tuning may further augment automated data extraction from clinical narratives for the provision of real-time, essential clinical insights.

Online public health discourse is becoming more and more important in shaping public health dynamics. Large Language Models (LLMs) offer a scalable solution for analysing the vast amounts of unstructured text found on online platforms. Here, we explore the effectiveness of Large Language Models (LLMs), including GPT models and open-source alternatives, for extracting public stances towards vaccination from social media posts. Using an expert-annotated dataset of social media posts related to vaccination, we applied various LLMs and a rule-based sentiment analysis tool to classify the stance towards vaccination. We assessed the accuracy of these methods through comparisons with expert annotations and annotations obtained through crowdsourcing. Our results demonstrate that few-shot prompting of best-in-class LLMs are the best performing methods, and that all alternatives have significant risks of substantial misclassification. The study highlights the potential of LLMs as a scalable tool for public health professionals to quickly gauge public opinion on health policies and interventions, offering an efficient alternative to traditional data analysis methods. With the continuous advancement in LLM development, the integration of these models into public health surveillance systems could substantially improve our ability to monitor and respond to changing public health attitudes.

Social support (SS) and social isolation (SI) are social determinants of health (SDOH) associated with psychiatric outcomes. In electronic health records (EHRs), individual-level SS/SI is typically documented in narrative clinical notes rather than as structured coded data. Natural language processing (NLP) algorithms can automate the otherwise labor-intensive process of extraction of such information. Psychiatric encounter notes from Mount Sinai Health System (MSHS, n = 300) and Weill Cornell Medicine (WCM, n = 225) were annotated to create a gold-standard corpus. A rule-based system (RBS) involving lexicons and a large language model (LLM) using FLAN-T5-XL were developed to identify mentions of SS and SI and their subcategories (eg, social network, instrumental support, and loneliness). For extracting SS/SI, the RBS obtained higher macroaveraged F1-scores than the LLM at both MSHS (0.89 versus 0.65) and WCM (0.85 versus 0.82). For extracting the subcategories, the RBS also outperformed the LLM at both MSHS (0.90 versus 0.62) and WCM (0.82 versus 0.81). Unexpectedly, the RBS outperformed the LLMs across all metrics. An intensive review demonstrates that this finding is due to the divergent approach taken by the RBS and LLM. The RBS was designed and refined to follow the same specific rules as the gold-standard annotations. Conversely, the LLM was more inclusive with categorization and conformed to common English-language understanding. Both approaches offer advantages, although additional replication studies are warranted.

The subject of the study is the analysis and improvement of methods for determining the relevance of project names to the information content of purchases using large language models. The object of the study is a database containing the names of projects and purchases in the field of electric power industry, collected from open sources. The author examines in detail such aspects of the topic as the use of TF-IDF and cosine similarity metrics for primary data filtering, and also describes in detail the integration and evaluation of the effectiveness of large language models such as GigaChat, GPT-3.5, and GPT-4 in text data matching tasks. Special attention is paid to the methods of clarifying the similarity of names based on reflection introduced into the prompta of large language models, which makes it possible to increase the accuracy of data comparison. The study uses TF-IDF and cosine similarity methods for primary data analysis, as well as large GigaChat, GPT-3.5 and GPT-4 language models for detailed verification of the relevance of project names and purchases, including reflection in model prompta to improve the accuracy of results. The novelty of the research lies in the development of a combined approach to determining the relevance of project names and purchases, combining traditional methods of processing text information (TF-IDF, cosine similarity) with the capabilities of large language models. A special contribution of the author to the research of the topic is the proposed methodology for improving the accuracy of data comparison by clarifying the results of primary selection using GPT-3.5 and GPT-4 models with optimized prompta, including reflection. The main conclusions of the study are confirmation of the prospects of using the developed approach in the tasks of information support for procurement processes and project implementation, as well as the possibility of using the results obtained for the development of text data mining systems in various sectors of the economy. The study showed that the use of language models makes it possible to improve the value of the F2 measure to 0.65, which indicates a significant improvement in the quality of data comparison compared with basic methods.

This paper introduces an agentic AI architecture that augments Large Language Models (LLMs) with tool-guided workflows to achieve consistent and dependable mathematical extraction and computation. By guiding LLMs through structured, agentic workflows, the proposed system addresses their inherent underperformance in mathematical operations and numerical consistency, despite strong contextual understanding. The system decomposes tasks into a sequence of modular, autonomous agents—each assigned a distinct function—ensuring high-precision outcomes in data-driven engineering applications. The architecture operates through a well-defined process: a Document Agent ingests and preprocesses unstructured inputs (e.g., PDF engineering reports); an Extraction Agent utilizes an LLM to identify and extract structured information (e.g., acid volumes, fluid stages, depth ranges); a Validation Agent performs schema-based validation to ensure data integrity; a Computation Agent executes deterministic calculations on validated data (e.g., summing acid volumes, determining total Viscoelastic Diverting Acid volumes, and calculating depth intervals); and a Reporting Agent formats and outputs the results as structured tables or JSON. The framework was tested using acid job reports, successfully parsing, validating, and processing examples such as "PUMP 43.53 BBLS PRE FLUSH" and "15% HCl MAIN ACID 21.3 BBLS". This demonstrated the robustness of the agentic workflow in handling technical language and numerical operations, even in cases prone to LLM misinterpretations like fluid type differentiation or concentration calculations. Key observations include improved consistency due to schema enforcement, significant mitigation of common LLM errors (e.g., incorrect unit interpretation, misclassification of fluid types, erroneous percentage calculations), and accurate results even in ambiguous cases. By eliminating numerical hallucinations and ensuring accurate data aggregation, the framework consistently outperforms raw LLM approaches, which often yield inconsistent or incorrect calculations despite prompt tuning. This guided, tool-augmented approach enhances LLMs’ contextual understanding while delegating mathematical fidelity to deterministic tools, providing a robust method for deriving accurate engineering insights from complex data. The framework is generalizable to other domains such as cementing, fracturing, and economic evaluation, bridging natural language understanding with deterministic computation for scalable, trustworthy LLM deployment in real-world energy-sector applications.

The purpose of this paper is to explore the implementation of retrieval-augmented generation (RAG) technology with open-source large language models (LLMs). A dedicated web-based application, PaSSER, was developed, integrating RAG with Mistral:7b, Llama2:7b, and Orca2:7b models. Various software instruments were used in the application’s development. PaSSER employs a set of evaluation metrics, including METEOR, ROUGE, BLEU, perplexity, cosine similarity, Pearson correlation, and F1 score, to assess LLMs’ performance, particularly within the smart agriculture domain. The paper presents the results and analyses of two tests. One test assessed the performance of LLMs across different hardware configurations, while the other determined which model delivered the most accurate and contextually relevant responses within RAG. The paper discusses the integration of blockchain with LLMs to manage and store assessment results within a blockchain environment. The tests revealed that GPUs are essential for fast text generation, even for 7b models. Orca2:7b on Mac M1 was the fastest, and Mistral:7b had superior performance on the 446 question–answer dataset. The discussion is on technical and hardware considerations affecting LLMs’ performance. The conclusion outlines future developments in leveraging other LLMs, fine-tuning approaches, and further integration with blockchain and IPFS.

Recent developments suggest that Large Language Models (LLMs) provide a promising approach for approximating empirical correlation matrices of item responses by utilizing item embeddings and their cosine similarities. In this paper, we introduce a novel tool, which we label SEMbeddings. This tool integrates mpnet-personality (a fine-tuned embedding model) with latent measurement models to assess model fit or misfit prior to data collection. To support our statement, we apply SEMbeddings to the 96 items of the VIA-IS-P, which measures 24 different character strengths, using responses from 31,697 participants. Our analysis shows a significant, though not perfect, correlation (r = 0.67) between the cosine similarities of embeddings and empirical correlations among items. We then demonstrate how to fit confirmatory factor analyses on the cosine similarity matrices produced by mpnet-personality and interpret the outcomes using modification indices. We found that relying on traditional fit indices when using SEMbeddings can be misleading as they often lead to more conservative conclusions compared to empirical results. Nevertheless, they provide valuable suggestions about possible misfit, and we argue that the modification indices obtained from these models could serve as a useful screening tool to make informed decisions about items prior to data collection. As LLMs become increasingly precise and new fine-tuned models are released, these procedures have the potential to deliver more reliable results, potentially transforming the way new questionnaires are developed.

Thematic Analysis (TA) is a powerful tool for human factors, HCI, and UX researchers to gather system usability insights from qualitative data like open-ended survey questions. However, TA is both time consuming and difficult, requiring researchers to review and compare hundreds, thousands, or even millions of pieces of text. Recently, this has driven many to explore using Large Language Models (LLMs) to support such an analysis. However, LLMs have their own processing limitations and usability challenges when implementing them reliably as part of a research process – especially when working with a large corpus of data that exceeds LLM context windows. These challenges are compounded when using locally hosted LLMs, which may be necessary to analyze sensitive and/or proprietary data. However, little human factors research has rigorously examined how various prompt engineering techniques can augment an LLM to overcome these limitations and improve usability. Accordingly, in the present paper, we investigate the impact of several prompt engineering techniques on the quality of LLM-mediated TA. Using a local LLM (Llama 3.1 8b) to ensure data privacy, we developed four LLM variants with progressively complex prompt engineering techniques and used them to extract themes from user feedback regarding the usability of a novel knowledge management system prototype. The LLM variants were as follows:1.A “baseline” variant with no prompt engineering or scalability2.A “naïve batch processing” variant that sequentially analyzed small batches of the user feedback to generate a single list of themes3.An “advanced batch processing” variant that built upon the naïve variant by adding prompt engineering techniques (e.g., chain-of-thought prompting)4.A “cognition-inspired” variant that incorporated advanced prompt engineering techniques and kept a working memory-like log of themes and their frequencyContrary to conventional approaches to studying LLMs, which largely rely upon descriptive statistics (e.g., % improvement), we systematically applied a set of evaluation methods from behavioral science and human factors. We performed three stages of evaluation of the outputs of each LLM variant: we compared the LLM outputs to our team’s original TA, we had human factors professionals (N = 4) rate the quality and usefulness of the outputs, and we compared the Inter-Rater Reliability (IRR) of other human factors professionals (N = 2) attempting to code the original data with the outputs generated by each variant. Results demonstrate that even small, locally deployed LLMs can produce high-quality TA when guided by appropriate prompts. While the “baseline” variant performed surprisingly well for small datasets, we found that the other, scalable methods were dependent upon advanced prompt engineering techniques to be successful. Only our novel "cognition-inspired" approach performed as well as the “baseline” variant in qualitative and quantitative comparisons of ratings and coding IRR. This research provides practical guidance for human factors researchers looking to integrate LLMs into their qualitative analysis workflows, disentangling and uncovering the importance of context window limitations, batch processing strategies, and advanced prompt engineering techniques. The findings suggest that local LLMs can serve as valuable and scalable tools in thematic analysis.

Evaluating the Accuracy of Artificial Intelligence(AI)-Generated Synopses for Plasma Cell Disorder Treatment Regimens

Interacting with Large Language Models (LLMs) via declarative queries is increasingly popular for tasks like question answering and data extraction, thanks to their ability to process vast unstructured data. However, LLMs often struggle with answering complex factual questions, exhibiting low precision and recall in the returned data. This challenge highlights that executing queries on LLMs remains a largely unexplored domain, where traditional data processing assumptions often fall short. Conventional query optimization, typically cost-driven, overlooks LLM-specific quality challenges such as contextual understanding. Just as new physical operators are designed to address the unique characteristics of LLMs, optimization must consider these quality challenges. Our results highlight that adhering strictly to conventional query optimization principles fails to generate the best plans in terms of result quality. To tackle this challenge, we present a novel approach to enhance SQL results by applying query optimization techniques specifically adapted for LLMs. We introduce a database system, GALOIS, that sits between the query and the LLM, effectively using the latter as a storage layer. We design alternative physical operators tailored for LLM-based query execution and adapt traditional optimization strategies to this novel context. For example, while pushing down operators in the query plan reduces execution cost (fewer calls to the model), it might complicate the call to the LLM and deteriorate result quality. Additionally, these models lack a traditional catalog for optimization, leading us to develop methods to dynamically gather such metadata during query execution. Our solution is compatible with any LLM and balances the trade-off between query result quality and execution cost. Experiments show up to 144% quality improvement over questions in Natural Language and 29% over direct SQL execution, highlighting the advantages of integrating database solutions with LLMs.

Although large language models (LLMs) have flourished in various scientific applications, their applications in the specific task of molecular property prediction have not reached a satisfactory level, even for the specific chemistry LLMs. This work addresses a highly crucial and significant challenge existing in the field of drug discovery: accurately predicting the molecular properties by effectively leveraging LLMs enhanced with profound domain knowledge. We propose a Knowledge-Fused Large Language Model for dual-Modality (KFLM2) learning for molecular property prediction. The aim is to utilize the capabilities of advanced LLMs, strengthened with specialized knowledge in the field of drug discovery. We identified DeepSeek-R1-Distill-Qwen-1.5B as the optimal base model from three DeepSeek-R1 distilled LLMs and one chemistry LLM named ChemDFM, by fine-tuning with the ZINC and ChEMBL datasets. We obtained the SMILES embeddings from the fine-tuned model and subsequently integrated the embeddings with the molecular graph to leverage complementary information for predicting molecular properties. Finally, we trained the hybrid neural network on the combined dual modality inputs and predicted the molecular properties. Through benchmarking on regression and classification tasks, our proposed method can obtain higher prediction performance for nine out of ten datasets in the downstream regression and classification tasks. Visualization of the output of hidden layers indicates that the combination of the embedding with the molecular graph can offer complementary information to further improve the prediction accuracy compared with either the LLM embedding or the molecular graph inputs. Larger models do not inherently guarantee superior performance; instead, their effectiveness hinges on our ability to leverage relevant knowledge from both pretraining and fine-tuning. Implementing LLMs with domain knowledge would be a rational approach to making precise predictions that could potentially revolutionize the process of drug development and discovery.

Large language models (LLMs) have been shown to have significant potential in few-shot learning across various fields, even with minimal training data. However, their ability to generalize to unseen tasks in more complex fields, such as biology and medicine has yet to be fully evaluated. LLMs can offer a promising alternative approach for biological inference, particularly in cases where structured data and sample size are limited, by extracting prior knowledge from text corpora. Here we report our proposed few-shot learning approach, which uses LLMs to predict the synergy of drug pairs in rare tissues that lack structured data and features. Our experiments, which involved seven rare tissues from different cancer types, demonstrate that the LLM-based prediction model achieves significant accuracy with very few or zero samples. Our proposed model, the CancerGPT (with ~ 124M parameters), is comparable to the larger fine-tuned GPT-3 model (with ~ 175B parameters). Our research contributes to tackling drug pair synergy prediction in rare tissues with limited data, and also advancing the use of LLMs for biological and medical inference tasks.

e13609 Background: Precision oncology revolutionized cancer treatment by identifying molecular biomarkers to guide personalized care. The ever-growing body of medical literature presents a challenge for oncologists researching targeted therapies. While recent studies investigated large language models (LLMs) to streamline this process, LLM reliance on general rather than medical knowledge limits clinical relevance and trustworthiness. To address these limitations, we developed a retrieval augmented generation (RAG) system that integrates PubMed clinical studies, trial databases and oncological guidelines with LLMs to support targeted treatment recommendations. The Molecular Tumor Board (MTB) at the Center of Personalized Medicine (ZPMTUM) guided and evaluated treatment options proposed by the LLM to assess their applicability for clinical decision support. Methods: We used 10 publicly accessible fictional patient cases with 7 tumor types and 59 distinct molecular alterations. Our LLM system MEREDITH (Medical Evidence Retrieval and Data Integration for Tailored Healthcare) consists of Google's Gemini Pro, enhanced with RAG and Chain-of-Thought (CoT) prompting. To establish a benchmark, clinical experts at ZPMTUM manually annotated the cases. Informed by MTB expert feedback, we iteratively improved our LLM system from a draft system relying on PubMed-indexed data to an enhanced system, which replicated expert annotation processes by incorporating oncology guidelines, drug availability and trial databases (ClinicalTrials.gov, QuickQueck.de). ZPMTUM assessed credibility and clinical relevance of manually annotated and LLM-generated recommendations. Patient-level data on (likely) pathogenic molecular alterations and recommended treatment options were summarized using median and interquartile range (IQR). Semantic similarity between LLM and clinician responses was assessed using cosine similarity of text vector embeddings; paired t-test evaluated significance. Results: The median of (likely) pathogenic molecular alterations per patient was 2.5 (IQR: 2-3). ZPMTUM identified a median of 2 treatment options per patient (IQR: 1-3), while the enhanced LLM identified a median of 4 (IQR: 3-5). MEREDITH proposed multiple relevant treatment suggestions, including therapies based on preclinical studies, and molecular interactions, for further assessment by the MTB. ZPMTUM prioritized the most suitable clinical option. The mean semantic textual similarity of LLM responses increased significantly from 0.69 in the draft system to 0.76 in the enhanced system (p <0.001). Thus, feedback from ZPMTUM enhanced the model's ability to align its responses with clinician thought processes. Conclusions: Leveraging expert thought processes to instruct LLMs holds promise as a novel decision support tool for precision oncology.