Pipeline-agent: A comprehensive decision support system for natural gas pipelines via verifiable computational reasoning

Large Language Models (LLMs) have garnered increasing attention in neurosurgery and possess significant potential to improve the field. However, the breadth and performance of LLMs across diverse neurosurgical tasks have not been systematically examined, and LLMs come with their own challenges and unique terminology. We seek to identify key models, establish reporting guidelines for replicability, and highlight progress in key application areas of LLM use in the neurosurgical literature. We searched PubMed and Google Scholar using terms related to LLMs and neurosurgery ("large language model" OR "LLM" OR "ChatGPT" OR "GPT-3" OR "GPT3" OR "GPT-3.5" OR "GPT3.5" OR "GPT-4" OR "GPT4" OR "LLAMA" OR "MISTRAL" OR "BARD") AND "neurosurgery". The final set of articles was reviewed for publication year, application area, specific LLM(s) used, control/comparison groups used to evaluate LLM performance, whether the article reported specific LLM prompts, prompting strategy types used, whether the LLM query could be reproduced in its entirety (including both the prompt used and any adjoining data), measures of hallucination, and reported performance measures. Fifty-one articles met inclusion criteria, and were categorized into six application areas, with the most common being Generation of Text for Direct Clinical Use (n = 14, 27.5%), Answering Standardized Exam Questions (n = 12, 23.5%), and Clinical Judgement and Decision-Making Support (n = 11, 21.6%). The most frequently used LLMs were GPT-3.5 (n = 30, 58.8%), GPT-4 (n = 20, 39.2%), Bard (n = 9, 17.6%), and Bing (n = 6, 11.8%). Most studies (n = 43, 84.3%) used LLMs directly out-of-the-box, while 8 studies (15.7%) conducted advanced pre-training or fine-tuning. Large language models show advanced capabilities in complex tasks and hold potential to transform neurosurgery. However, research typically addresses basic applications and overlooks enhancing LLM performance, facing reproducibility issues. Standardizing detailed reporting, considering LLM stochasticity, and using advanced methods beyond basic validation are essential for progress.

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.

Large language models present new questions for decision support

In today’s rapidly evolving business landscape, Artificial Intelligence (AI), and specifically Large Language Models (LLMs), are redefining how organizations operate, make decisions, and engage with customers. AI-driven technologies have become indispensable, providing businesses with powerful tools to streamline operations, derive actionable insights from vast data, and foster more meaningful customer interactions. For business leaders, scholars, and practitioners alike, understanding the transformative potential of AI isn’t just advantageous—it’s essential to staying competitive in an increasingly data-driven world.This editorial delves into recent scholarly advancements in LLM applications within business contexts, analyzing studies that explore AI’s potential across various domains, from decision support to creative industries. By introducing a structured framework, this editorial highlights key insights and contributions from recent studies, assessing their value to academia and industry. The following comparative analysis sheds light on how these innovations shape our understanding of AI’s role in business while pointing to future research directions.Puyt and Madsen's (2024) study stands out as a foundational exploration of LLM accuracy, assessing ChatGPT-4's ability to recount the history of the SWOT analysis-a vital business strategy tool. Their findings reveal that, while ChatGPT-4 effectively conveys general concepts, it struggles with detailed historical information, often producing inaccuracies or "hallucinations." This gap underscores the need for LLMs to be trained with verified academic data, particularly for strategic business applications that demand precision. This study not only contributes to the literature by proposing methods to evaluate AI accuracy in historical contexts but also highlights the importance of rigorous information vetting in industry settings where reliability is crucial.In contrast, Raikov et al. (2024) explore a hybrid intelligence model that combines LLM capabilities with explainable AI (XAI) principles to enhance human-machine collaboration. Their approach emphasizes cognitive semantics, improving transparency and decision-making efficiency. The hybrid model's real-time adaptability addresses the needs of complex, regulated industries such as finance and healthcare, where trust in AI decisions is paramount. Academically, this study provides a valuable addition to XAI literature by demonstrating how LLMs can bridge the gap between AI autonomy and human oversight, making it a model for future human-AI interactions in complex business environments.Another significant study by Mariotti and colleagues (2024) examines the integration of LLMs with enterprise knowledge graphs to enhance data-driven decision-making. By enabling organizations to leverage knowledge graphs for more accurate and scalable data retrieval, this research provides a robust framework for businesses seeking efficient knowledge management systems. The academic contribution here lies in advancing the dialogue between LLMs and knowledge graphs, emphasizing ethical data handling and quality standards essential for industry applications. For enterprises, the study offers practical solutions to achieve streamlined data management, balancing automation with privacy and security. 2024) take a different approach, investigating LLMs' role in creative industries, specifically within fashion design. They introduce a hybrid intelligence model that supports creative processes, allowing AI to complement rather than replace human ingenuity. While LLMs in this field demonstrate potential in automating repetitive design tasks and enhancing customer personalization, the study reveals limitations in AI's ability to handle spatial and stylistic nuances. This study's academic contribution lies in promoting human-AI co-creation, inspiring further research into AI applications across diverse creative sectors, including media and marketing.Collectively, these studies not only illuminate LLMs' transformative potential in business but also highlight critical ethical and operational considerations. Ensuring accuracy, transparency, and data privacy are vital to responsibly integrating AI into business workflows. Future research should focus on enhancing LLM accuracy, refining hybrid intelligence models, and exploring creative AI applications, all while maintaining ethical standards. As LLMs evolve, interdisciplinary collaborations will be essential to harness their full potential, making AI an ethical, effective, and innovative force in the business world.

As consumers increasingly rely on AI for purchase guidance, large language models (LLMs) have become key mediators of buying decisions, processing marketing content before generating recommendations. Understanding how these AI-based decision support systems respond to persuasive techniques becomes critical for consumers and system designers. This study maps LLM response patterns to persuasive techniques across product types. Using a controlled experiment with 13,500 observations across three LLMs (GPT-4.1 Mini, GPT-5 Mini, Kimi K2-0905), the research tested hedonic versus utilitarian products under four persuasion conditions based on Cialdini’s principles. Results reveal differential persuasion sensitivity by product type: social proof increased the rate at which LLMs expressed high recommendation certainty for concert tickets from 9.6% to 65.6%, while authority appeals reduced certainty for utilitarian products. This asymmetric pattern persisted across all models and partially mirrors human consumer psychology, suggesting LLMs internalized decision frameworks from training data. The study contributes to IS research by extending the hedonic-utilitarian framework to AI-mediated decision support and connecting LLM persuasion susceptibility to debates on technology trust. These findings have practical implications for how persuasive content shapes AI-mediated consumer decision support.

Large language models (LLMs) are advanced artificial intelligence (AI) tools capable of generating human-like text and are increasingly used in education, clinical care, and research. Little is known about their use within oral and maxillofacial surgery (OMFS) training. This study investigates LLM usage trends, perceived value, and educational integration among OMFS residents in the United States. A national, anonymous cross-sectional survey was distributed to OMFS residents via program directors. It gathered demographic data, LLM usage patterns, applications, perceived limitations, and attitudes toward incorporating LLMs into formal education. Eighty-one residents responded, 79.0% (64/81) reported having used an LLM, and of that group, 96.9% (62/64) use ChatGPT. 51.9% (42/81) of respondents used LLMs at least monthly in residency; however, 97.5% (79/81) reported having received no formal LLM education during residency. Residents used LLMs for clinical decision support, board preparation, research, and career planning. Free-text responses revealed a wide spectrum of views. Some advocated for curricular integration and patient education applications, while others questioned the need for formal instruction. Some respondents supported integrating LLMs into curriculums and patient education while others questioned the need for formal instruction. LLMs are used frequently by OMFS residents for a variety of purposes. As AI and LLMs become embedded in healthcare, understanding how OMFS residents interact with LLMs is vital. These findings may guide curriculum development, fostering responsible and effective use of LLMs in surgical training and practice.

Large language models (LLMs) are currently abundant and diverse, yet clinicians lack clarity on top performers, with uncertainty about general LLMs' expertise in musculoskeletal rehabilitation. This study aims to investigate the potential and correctness of LLMs in clinical application, and to evaluate whether LLMs could assist primary rehabilitation therapists to prepare for rehabilitation examination. 8 primary doctors and therapists tested 10 LLMs in the first test, 5 senior doctors and therapists assessed answers in the second test, and 5 primary therapists acted as examinees in the third test. We assessed the quality of case analysis based on six different dimensions, including Case Understanding, Clinical Reasoning, Primary Diagnosis, Differential Diagnosis, Treatment Plan Accuracy and Safety, and Guidelines & Consensus. In the first test, only ERNIE Bot X1 Turbo and Doubao 1.5 pro had accuracy rates of over 90%, and Chinese LLMs had significantly fewer incorrect questions than English LLMs (9.6% vs. 14.8%, P < 0.001). In the second test, Doubao 1.5 pro achieved relatively high scores in both cases, and LLMs gained high scores in "Case understanding", "Clinical Reasoning" and "Diagnosis". In the third test, primary therapists achieving a mean accuracy rate of 76.9%, and Doubao 1.5 pro improved its accuracy rates to 85.8%. Doubao 1.5 pro possessed competent ability and application prospects, and was assessed as the best LLM for answering musculoskeletal rehabilitation questions. We also demonstrated that the response quality of local-language LLMs was significantly better than that of English LLMs in answering localized language questions.

This paper examines the potential of integrating Large Language Models (LLMs) with knowledge bases to imrpove the accuracy and reliability of their responses. The advantages of such a combination are evaluated, particularly in reducing the risk of hallucinations – the phenomenon where models generate erroneous or fabricated information. Various methodologies for combining LLMs with knowledge bases are analyzed, along with their respective advantages and limitations. The prospects and challenges of implementing this technology in diverse fields—such as information retrieval, decision support, and automated content creation—are discussed. The paper presents an overview of the current state of research in this domain and delineates directions for future investigation.The integration of LLMs with knowledge bases represents a significant advancement in artificial intelligence, aiming one of the key concerns regarding LLMs—their tendency to generate inaccurate or fabricated information, commonly referred to as hallucinations. This approach leverages the vast language understanding and generation capabilities of LLMs while grounding their outputs in structured and verified information from knowledge bases. The synergy between these two technologies has the potential to significantly enhance the reliability and factual accuracy of AI-generated responses across a wide range of applications. The methodologies for combining LLMs with knowledge bases differ in their implementation and effectiveness. Some approaches involve pre-training LLMs on curated knowledge bases, while others reference knowledge bases externally during the inference process. Each method presents its own set of advantages and challenges, such as balancing computational efficiency against accuracy and maintaining the fluency of LLM outputs while adhering strictly to factual information. The application of this integrated technology extends beyond mere information retrieval, showing promise in complex decision support systems, automated content creation for specialized domains, and contributing to the advancement of explainable AI by providing traceable sources for generated information. As research in this area progresses, it is expected to open new avenues for developing more trustworthy and capable AI systems across various industries and academic disciplines.

To evaluate the impact of constraining proprietary and open large language models (LLMs) to structured outputs in processing radiology request forms (RRF). We evaluated five LLMs-two proprietary (GPT-5-Thinking, Gemini 2.5 Pro) and three open (Qwen3-235B-A22B-Thinking, gpt-oss-120b, medgemma-27b-it)-on 100 RRFs (50 computed tomography, 50 magnetic resonance imaging). Each model processed cases with and without constraints to structured outputs. Endpoints included accuracy for modality, anatomical region, contrast phase, urgency, "all correct" (all four categories correct), and "indication improved" (clarity of rewritten text). Outputs were evaluated against a reference standard defined by board-certified radiologists and compared with two radiology residents (first-year and third-year). Accuracies with 95% confidence intervals were calculated. Constraining to structured outputs had model-dependent effects: it improved Gemini 2.5 Pro (all correct: from 53.0% [43.3-62.5] to 66.0% [56.3-74.5]) but reduced GPT-5-Thinking accuracy (from 76.0% [66.8-83.3] to 53.0% [43.3-62.5]), with minimal influence on open models. Both proprietary LLMs outperformed the best open models (up to 41.0% [31.9-50.8]). All LLMs exceeded the unassisted first-year residents' performance (19.0% [12.5-27.8]). LLM assistance improved first-year residents' accuracy to 65.0% [55.3-73.6], approaching the third-year residents' performance (80.0% [71.1-86.7]), who performed comparably to the best LLMs. Across models, performance was highest for modality and anatomical region, and lowest for urgency. Indication reformulation was judged clearer in > 90% of cases across all models without hallucinations. Constraining to structured outputs exerted model-specific effects. Proprietary LLMs achieved the highest accuracy in RRF-based protocol selection and improved first-year resident performance to an experienced-resident level. LLMs may serve as valuable decision-support tools for radiology workflow. Constraining LLMs to structured outputs produced divergent, model-specific effects in radiology protocol selection-improving Gemini 2.5 Pro, reducing GPT-5-Thinking, and minimally affecting open models-highlighting the need for model-specific prompting strategies before adopting LLMs in radiology decision support. Structured output constraints affect LLM performance differently. Gemini 2.5 Pro benefits from structured prompting, while GPT-5-Thinking declines. Open-weight models show minimal impact from output constraining. Proprietary models outperform open models in radiology protocol selection.

Large language models illuminate a progressive pathway to artificial intelligent healthcare assistant

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

The integration of artificial intelligence technologies, such as large language models (LLMs), in health care holds potential for improved efficiency and decision support. However, ethical concerns must be addressed before widespread adoption. This article focuses on the ethical principles surrounding the use of Generative Pretrained Transformer-4 and its conversational model, ChatGPT, in healthcare settings. One concern is potential inaccuracies in generated content. LLMs can produce believable yet incorrect information, risking errors in medical records. Opacity of training data exacerbates this, hindering accuracy assessment. To mitigate, LLMs should train on precise, validated medical data sets. Model bias is another critical concern because LLMs may perpetuate biases from their training, leading to medically inaccurate and discriminatory responses. Sampling, programming, and compliance biases contribute necessitating careful consideration to avoid perpetuating harmful stereotypes. Privacy is paramount in health care, using public LLMs raises risks. Strict data-sharing agreements and Health Insurance Portability and Accountability Act (HIPAA)-compliant training protocols are necessary to protect patient privacy. Although artificial intelligence technologies offer promising opportunities in health care, careful consideration of ethical principles is crucial. Addressing concerns of inaccuracy, bias, and privacy will ensure responsible and patient-centered implementation, benefiting both healthcare professionals and patients.

Large Language Models (LLMs) are increasingly used in industrial monitoring and decision support, yet they remain prone to process-control hallucinations—diagnoses and explanations that sound plausible but conflict with physical constraints, sensor data, or plant dynamics. This paper investigates hallucination as a failure of abductive reasoning, where missing premises, weak mechanistic support, or counter-evidence lead an LLM to propose incorrect causal narratives for faults such as pump restriction, valve stiction, fouling, or reactor runaway. We develop a neuro-symbolic framework in which Abductive Logic Programming (ALP) evaluates the coherence of model-generated explanations, counter-abduction generates rival hypotheses that test whether the explanation can be defeated, and Discourse-weighted ALP (D-ALP) incorporates nucleus–satellite structure from operator notes and alarm logs to weight competing explanations. Using our 500-scenario Process-Control Hallucination Dataset, we assess LLM reasoning across mechanistic, evidential, and contrastive dimensions. Results show that abductive and counter-abductive operators substantially reduce explanation-level hallucinations and improve alignment with physical process behavior, particularly in “easy-but-wrong’’ cases where a superficially attractive explanation contradicts historian trends or counter-evidence. These findings demonstrate that abductive reasoning provides a practical and verifiable foundation for improving LLM reliability in safety-critical process-control environments.

Electronic health records (EHRs) with clinical decision support tools are now ubiquitous in healthcare organizations. Clinical foundation models (CFMs) pretrained on large-scale, heterogeneous structured EHR data have emerged as a powerful approach to improve predictive performance and generalizability. Meanwhile, large language models (LLMs) pretrained on broad data sources are being applied to an expanding range of healthcare tasks. However, it remains unclear whether generalist LLMs can match specialized CFMs for disease risk prediction using structured clinical data. We compared CFMs (Med-BERT, CLMBR) against fine-tuned generalist LLMs (Mistral, LLaMA-2/3/3.1), a clinical LLM (Me-LLaMA), and LLM-generated embeddings paired with simple classifiers (using DeepSeek, Qwen3, and GPT-OSS) on two disease risk prediction tasks: heart failure risk among diabetic patients (DHF) and pancreatic cancer diagnosis (PaCa). Evaluations spanned multi-site EHR data, claims data, and an open-source single-institution benchmark (EHRSHOT). Performance was assessed using the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). On larger EHR and claims cohorts (>30,000 patients), fine-tuned CFMs outperformed fine-tuned LLMs by a small but statistically significant margin (<1% AUROC). The clinical LLM performed comparably to generalist LLMs despite being smaller. On the open-source PaCa cohort (3,810 patients, 199 cases), LLMs achieved slightly higher AUROCs that were not statistically significant (LLaMA-3.1-70B 86.1% vs. Med-BERT 85.3%, p=0.27), but CFMs achieved significantly higher AUPRC (Med-BERT 55.9% vs. LLaMA-3.1-70B 41.1%, p=0.001). Notably, LLM-generated trajectory embeddings paired with logistic regression or a simple MLP, without any LLM fine-tuning, achieved the best overall performance, with AUROC exceeding 90% (Qwen3) and AUPRC reaching 66% (GPT-OSS 20B). LLM-generated embeddings with lightweight classifiers outperformed both fine-tuned CFMs and fine-tuned LLMs on AUROC and AUPRC. While these results demonstrate the potential of generalist models to match or surpass specialized CFMs, their substantially greater computational cost and variable AUPRC performance in the fine-tuning setting warrant caution. We provide a reproducible evaluation framework and codebase to support continued benchmarking.

<sec> <title>BACKGROUND</title> Substance use disorder (SUD) affects more than 48 million people in the US, but access to care is often constrained by factors such as stigma, financial barriers, and limited service availability. While digital health interventions have begun to mitigate these challenges, large language models (LLMs) such as GPT introduce new possibilities for scalable SUD recovery support. However, empirical evidence regarding LLM use in SUD contexts re-mains fragmented and unevenly evaluated. A scoping review is needed to synthesize cur-rent approaches, surface methodological and safety gaps, and inform future research. </sec> <sec> <title>OBJECTIVE</title> This scoping review synthesizes current evidence on the use of LLMs in SUD and addiction contexts. It examines how LLM-based tools are being applied across the full spectrum of care, identifies common tasks and data sources, evaluates risks and safety concerns, and maps early evidence of effectiveness and gaps that inform future research. </sec> <sec> <title>METHODS</title> We conducted a scoping review in accordance with PRISMA-ScR guidelines. We searched medical and technical databases for studies that reported empirical findings on LLMs in drug-related SUD contexts. Of 675 records identified, 492 were screened at the title and abstract level, 60 underwent full-text review, and 12 studies met inclusion criteria. </sec> <sec> <title>RESULTS</title> All included studies were published in 2024 or 2025, indicating a very recent and rapidly evolving evidence base. Most studies used public social media or online forums (8 of 12), while 3 studies used clinical data such as EHR, and 1 study used multimodal data that combined sensors, images, and chat logs. GPT-family models were the most frequently used (10 of 12), often evaluated alongside Llama or Mistral models. Across studies, LLM applications clustered into four task categories: text classification and extraction, response generation, conversational decision support, and predictive modeling using LLM-derived features. Classification and extraction tasks were most common and often showed im-proved performance compared with traditional machine learning or rule-based baselines. While general-purpose LLMs were prone to generating stigmatizing, biased, or clinically inappropriate content in SUD-related scenarios, it was also reported that targeted prompt engineering substantially reduced stigmatizing language and improved perceived ade-quacy and appropriateness of responses. However, safety evaluations remained limited in scope and depth. </sec> <sec> <title>CONCLUSIONS</title> LLM-based tools show emerging potential to support SUD-related research and care, particularly scalable classification and information extraction, conversational screening, and feature derivation from unstructured text or speech. Yet current work is predominantly preclinical, relying on public datasets and simulated prompts, with limited evidence on safety, clinical effectiveness, or real-world implementation in recovery settings. Responsible use in SUD contexts will require a safety-first development approach that integrates human-in-the-loop validation, systematic prompt engineering, and design constraints such as curated response libraries. It will also require efforts to strengthen LLM literacy among clinicians, peer specialists, and people in recovery. </sec>