Explainable and evidence-linked recommendations for spine surgery via a retrieval-augmented LLM agent.

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.

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.

Large-language models (LLMs) have demonstrated increasing potential in healthcare, including applications in clinical information retrieval, medical reasoning, and decision support. Autoimmune liver diseases (AILDs), including autoimmune hepatitis, primary biliary cholangitis, primary sclerosing cholangitis, and IgG4-related liver disease, are rare and heterogeneous conditions that often present with nonspecific features and require specialized expertise for accurate diagnosis and management. In resource-limited settings, limited access to hepatology subspecialists may contribute to delayed diagnoses and suboptimal care. Although reasoning-oriented LLMs are designed to support structured clinical inference, their performance on AILD-related tasks has not been systematically evaluated. To evaluate and compare the accuracy, safety, readability, comprehensiveness, and diagnostic performance of six large language models for addressing AILD-related clinical questions and real-world cases. We developed 26 clinically relevant questions spanning the key domains of AILDs, including pathogenesis, risk factors, clinical presentation, diagnosis, treatment, and prognosis. Six publicly available LLMs (o1-preview, Claude-3.5-Sonnet, GPT-4o, GPT-4o-mini, GPT-3.5-Turbo, and LLaMA-3.1-405B) generated responses that were independently evaluated by 11 board-certified hepatologists with more than 10years of specialty experience. Accuracy, safety, readability, comprehensiveness, and clinical helpfulness were assessed by using predefined rating scales. Diagnostic performance was further evaluated in three models (o1-preview, GPT-4o, and GPT-3.5-Turbo) using 21 confirmed real-world AILD cases, with accuracy determined by comparison with gold-standard clinical diagnoses. Among the six models, the o1-preview demonstrated the highest overall performance. It achieved the greatest proportion of accurate responses (78.3%), the highest mean safety score, and the most favorable readability profile, despite generating the longest responses. Comprehensiveness and helpfulness ratings were also highest for o1-preview. In diagnostic testing, the o1-preview achieved an overall accuracy of 81.0%, outperforming GPT-4o and GPT-3.5-Turbo, particularly in single-entity AILD diagnoses. All models demonstrated reduced diagnostic accuracy for autoimmune hepatitis-primary biliary cholangitis overlap syndrome. Reasoning-based LLMs, particularly o1-preview, demonstrate potential value in supporting clinical information delivery and diagnostic reasoning for AILDs, especially in resource-limited settings. However, LLM outputs should serve as adjunctive support rather than substitutes for specialist judgment. Further domain-specific refinement and multicenter validation are necessary to ensure safe and effective integration into hepatology and clinical pharmacy practices.

e13630 Background: As many as 60% of prior authorization requests are denied, yet coverage approval occurs for more than 60% of appeals for some therapies. Appeal processes encumber providers and increase burnout, but large language models (LLMs) may aid providers by drafting appeal letters. We evaluated LLM performance at this task for radiotherapy denials. Methods: Three commercially accessible LLMs were evaluated: generative pre-trained transformer 3.5 (GPT3.5), GPT4, and GPT4+web with internet search capacity (OpenAI, Inc., San Francisco, CA). A fourth LLM, GPT3.5-FT, was developed by fine-tuning GPT3.5 in a HIPAA-complaint local environment. The fine-tuning training data comprised 53 insurance denial appeal letters prepared by radiation oncologists and paired prompts describing the clinical history and appeal intent. Training data were enriched in appeal letters for proton radiotherapy, stereotactic body radiotherapy, and image-guided radiotherapy for myriad clinical scenarios. Twenty prompts, each requesting a letter for a simulated patient history, were programmatically presented to the LLMs. Three radiation oncologists, who were blinded to the LLM source, scored letter outputs across four domains: language syntax and semantics, clinical detail inclusion, clinical reasoning validity, and overall readiness for insurer submission. Additionally, one radiation oncologist scored the authenticity and relevance of literature sources cited in output letters, which were requested by several test prompts. Interobserver agreement between radiation oncologist scores was determined by Cohen’s kappa coefficient. Scores were compared between LLMs with non-parametric statistical tests. Results: Agreement between radiation oncologists’ scores was moderate-to-excellent across all domains (median κ = 0.68, minimum κ = 0.41). GPT3.5, GPT4, and GPT4+web drafted letters that, by mode average, were semantically and syntactically clear, included all provided clinical history without confabulation, clinically reasoned with few necessary revisions, and overall were submissible to an insurer with minor revisions. GPT4 and GPT4+web clinically reasoned better than GPT3.5 (p values &lt; 0.001). In contrast, GPT3.5-FT performance was inferior to other LLMs across all domains (p values &lt; 0.001). LLMs were poor at identifying, citing, and summarizing relevant literature unless provided in the prompt. Conclusions: LLMs can draft insurance appeal letters for radiotherapy services that require few revisions yet are poor at referencing relevant literature. Contrary to our hypothesis, fine-tuning with data from our department compromised LLM performance.

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 &lt;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 (LLMs) have showed strong performance on standardized medical examinations, yet their comparative clinical relevance against human clinicians remains limited. This study benchmarked the performance of DeepSeek-R1 and ChatGPT 4.0 against cardiovascular clinicians from different hospital levels in China. We conducted a cross-sectional, vignette-based assessment consisting of 100 standardized cardiovascular multiple-choice questions covering four competency domains: clinical reasoning (CR), frontier updates (FU), basic memory (BM), and emergency decision (ED). Thirty clinicians from six hospitals (three primary and three tertiary) were compared with two LLMs. Each question was executed five times per model, and run-to-run consistency was evaluated. Mean differences (LLM − clinician) with 95% confidence intervals (CIs) were estimated using nonparametric bootstrap resampling (10,000 iterations). Clinicians achieved a mean total score of 69.7 ± 7.9, whereas DeepSeek-R1 and ChatGPT-4.0 scored 97 and 95, respectively. The mean total score differences were + 27.3 points (95% CI 24.4–30.1) for DeepSeek-R1 and + 25.3 points (22.4–28.1) for ChatGPT 4.0. Both models outperformed clinicians in CR, FU, BM, and ED. Run-to-run agreement was high (DeepSeek-R1 κ = 0.73; ChatGPT 4.0 κ = 0.76). LLMs substantially outperformed clinicians in knowledge- and decision-based tasks while approaching clinician-level performance in CR. These findings suggest that LLMs may complement clinical expertise and enhance diagnostic consistency across hospital levels.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-32602-w.

Large language models present new questions for decision support

Background/Objective: Hypertension is the most prevalent chronic disease in primary care and a leading cause of cardiovascular morbidity and mortality. Despite existing guidelines, therapeutic inertia and suboptimal control persist. Large language models (LLMs) offer a potential valuable addition to augment clinical decision-making, yet their reliability for guideline-driven tasks remains unverified. This study evaluated the accuracy and safety of hypertension management recommendations generated by three LLMs compared to expert responses. Methods: Fifty-one clinical vignettes representing 17 core hypertension management concepts were constructed by hypertension experts. Each case was submitted to three LLMs (GPT-4, Gemini, MedLM) and a hypertension expert also wrote the “gold standard” answers. Three blinded expert reviewers rated each response on a 4-point accuracy scale, a binary safety (safe/unsafe) scale, and attempted to identify the source (LLM vs. expert) providing the response. Ratings were analyzed using mean scores, percentages of accurate and safe responses, and inter-rater agreement. Results: GPT-4 had the highest accuracy (83%) and safety (86%) scores among LLMs but remained inferior to expert responses (92% accuracy, 93% safety). Gemini and MedLM performed significantly worse (accuracy: 64% and 35%; safety: 73% and 39%, respectively). GPT-4 generated the most guideline-concordant responses (46%) among the three LLMs (Gemini 35%, MedLM 14%), but remains lower than experts’ responses (68%). Evaluators misidentified LLM responses as expert-written in 10 to 25% of cases, particularly with GPT-4. Inter-rater reliability for accuracy ratings was highest for expert-generated responses (ICC 0.81), with progressively lower agreement for GPT-4 (0.76), Gemini (0.70), and MedLM (0.68). A similar pattern was observed for safety and source discrimination ratings. The agreement was strongest for safety assessments and weakest for source discrimination. Conclusion: Among three tested LLMs, GPT-4 demonstrated closer agreement to expert decisions thereby showing greater potential for supporting hypertension management. However, current LLMs’ versions frequently produce inaccurate or unsafe recommendations and remain inferior to expert judgment. Human-in-the-loop supervision remains essential when deploying LLMs for clinical decision-making.

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.

Purpose:To evaluate the performance of Large Language Models (LLMs) on optometry examination questions and compare their accuracy and readability with Bachelor of Optometry students.Methods:A cross-sectional comparative study was conducted using the publicly available, free versions of five LLM models from four platforms (ChatGPT 3.5, ChatGPT 4o, Gemini, CoPilot, and DeepSeek) and a group of 15 third- and fourth-year optometry students. Two sets of multiple-choice questions (20 theoretical and 20 clinical) were administered to both the students and the LLMs. Theoretical questions covered core optometric knowledge, while clinical questions simulated real-life patient scenarios. Responses were graded by senior ophthalmologists for accuracy, and readability was assessed via readable.com using four indices, including Flesch–Kincaid Grade Level, Flesch Reading Ease Score, Coleman Liau Score, and Simple Measure of Gobbledygook (SMOG) Index.Results:The overall scores of the optometry students (28.13 ± 3.33) were comparable to those of the LLMs (29 ± 4.41). In theoretical questions, LLMs (15.40 ± 1.82) performed at par with the students (14.07 ± 2.21), with DeepSeek and CoPilot outperforming students (scoring 17 each). However, in clinical questions, the students performed better, highlighting the limitations of LLMs in context-specific reasoning. Pairwise comparisons of the readability analysis revealed that Gemini and DeepSeek provided significantly most readable explanations, while ChatGPT 3.5 produced the most complex responses. Across models, readability varied for Flesch–Kincaid grade level (P = 0.0213), Flesch Reading Ease Score (P = 0.0014), and SMOG (P = 0.0412), with a nonsignificant trend for Coleman Liau Score (P = 0.0529).Conclusion:LLMs show reasonable accuracy, matching students in theoretical performance but underperforming in clinical reasoning. Gemini and DeepSeek offer superior readability, highlighting their promise as educational tools. Future research should focus on integrating LLMs into curricula while balancing them with hands-on clinical education.

A paper from Goh etal. found that a large language model (LLM) working alone outperformed American clinicians assisted by the same LLM in diagnostic reasoning tests (Goh E, Gallo R, Hom J, Strong E, Weng J, Kerman H, etal. Large language model influence on diagnostic reasoning: a randomized clinical trial. JAMA Netw Open 2024;7:e2440969). We aimed to replicate this experiment in a UK setting and explore how interactions with the LLM might explain the observed gaps in performance. This was a within-subjects study of UK physicians. 22 participants answered structured questions on four clinical vignettes. For 2 cases physicians had access to an LLM via a custom-built web-application. Results were analysed using a mixed-effects model accounting for case difficulty and the variability of clinicians at baseline. Qualitative analysis involved coding of participant-LLM interaction logs and evaluating the rates of LLM use per question. Physicians with LLM assistance scored significantly lower than the LLM alone (mean difference 21.3 percentage points, p<0.001). Access to the LLM was associated with improved physician performance compared to using conventional resources (74.3 vs. 65.7 %, p=0.001). There was significant heterogeneity in the degree of LLM-assisted improvement (SD 12.8 %). Qualitative analysis revealed that only 30 % of case questions were directly posed to the LLM, which suggests that under-utilisation of the LLM contributed to the observed performancegap. While access to an LLM can improve diagnostic accuracy, realising the full potential of human-AI collaboration may require a focus on training clinicians to integrate these tools into their cognitive workflows and on designing systems that make these integrations the default rather than an optionalextra.

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.

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.

Harnessing artificial intelligence in bariatric surgery: comparative analysis of ChatGPT-4, Bing, and Bard in generating clinician-level bariatric surgery recommendations

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