Application of Large Language Models in Embodied Artificial Intelligence

This article presents a risk analysis of large language models (LLMs), a type of "generative" artificial intelligence (AI) system that produces text, commonly in response to textual inputs from human users. The article is specifically focused on the risk of LLMs causing an extreme catastrophe in which they do something akin to taking over the world and killing everyone. The possibility of LLM takeover catastrophe has been a major point of public discussion since the recent release of remarkably capable LLMs such as ChatGPT and GPT-4. This arguably marks the first time when actual AI systems (and not hypothetical future systems) have sparked concern about takeover catastrophe. The article's analysis compares (A) characteristics of AI systems that may be needed for takeover, as identified in prior theoretical literature on AI takeover risk, with (B) characteristics observed in current LLMs. This comparison reveals that the capabilities of current LLMs appear to fall well short of what may be needed for takeover catastrophe. Future LLMs may be similarly incapable due to fundamental limitations of deep learning algorithms. However, divided expert opinion on deep learning and surprise capabilities found in current LLMs suggests some risk of takeover catastrophe from future LLMs. LLM governance should monitor for changes in takeover characteristics and be prepared to proceed more aggressively if warning signs emerge. Unless and until such signs emerge, more aggressive governance measures may be unwarranted.

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.

Artificial intelligence (AI) systems powered by large language models have become increasingly prevalent in modern society, enabling a wide range of applications through natural language interaction. As AI agents proliferate in our daily lives, their generic and uniform expressiveness presents a significant limitation to their appeal and adoption. Personality expression represents a key prerequisite for creating more human-like and distinctive AI systems. We show that AI models can express deterministic and consistent personalities when instructed using established psychological frameworks, with varying degrees of accuracy depending on model capabilities. We find that more advanced models like GPT-4o and o1 demonstrate the highest accuracy in expressing specified personalities across both Big Five and Myers-Briggs assessments, and further analysis suggests that personality expression emerges from a combination of intelligence and reasoning capabilities. Our results reveal that personality expression operates through holistic reasoning rather than question-by-question optimization, with response-scale metrics showing higher variance than test-scale metrics. Furthermore, we find that model fine-tuning affects communication style independently of personality expression accuracy. These findings establish a foundation for creating AI agents with diverse and consistent personalities, which could significantly enhance human-AI interaction across applications from education to healthcare, while additionally enabling a broader range of more unique AI agents. The ability to quantitatively assess and implement personality expression in AI systems opens new avenues for research into more relatable, trustworthy, and ethically designed AI.

Purpose This paper aims to evaluate how effectively large language models (LLMs) represent and generate minority cultural knowledge, specifically Taiwanese Hakka culture. To address this, the study proposes a structured and replicable evaluation framework integrating Bloom’s taxonomy and retrieval-augmented generation (RAG). The research is guided by the following questions: (1) How do LLMs perform across different cognitive domains when processing Hakka cultural content? (2) To what extent does the integration of RAG enhance the accuracy and contextual appropriateness of LLM outputs? And (3) How do different LLM architectures compare in their ability to recall, analyse and creatively synthesize culturally grounded information? Design/methodology/approach This study proposes a cognitive benchmarking framework to evaluate how LLMs process and apply culturally specific knowledge. The framework integrates Bloom’s taxonomy with RAG to assess model performance across six hierarchical cognitive domains: remembering, understanding, applying, analysing, evaluating and creating. Using a curated Taiwanese Hakka digital cultural archive as the primary testbed, the evaluation measures LLM-generated responses’ semantic accuracy and cultural relevance. Findings The evaluation results indicate that LLMs augmented with RAG exhibit marked improvements over baseline models in the cognitive domains of remembering, understanding and analysing. These enhancements are particularly evident in tasks requiring factual accuracy, contextual relevance and semantic precision, underscoring RAG’s effectiveness in addressing the knowledge sparsity typically observed in underrepresented cultural data sets. However, a notable limitation persists across all models including those equipped with RAG in the domain of creating. This suggests that while retrieval mechanisms bolster the reproduction and comprehension of cultural knowledge, they do not yet sufficiently support culturally nuanced generative synthesis. Originality/value This study introduces a novel evaluation framework integrating cognitive domain benchmarks with RAG-enhanced LLMs to assess cultural knowledge processing. The research advances culturally grounded artificial intelligence (AI) systems and digital archival quality by empirically demonstrating RAG’s impact on improving factual accuracy in lower and mid-level tasks. The findings affirm the strategic value of retrieval integration for enhancing representational fidelity in cultural AI applications, while also highlighting the need for future research into hybrid architectures that combine external grounding with culturally adaptive generation strategies.

Health care artificial intelligence (AI) systems are increasingly integrated into clinical workflows, yet remain vulnerable to data-poisoning attacks. A small number of manipulated training samples can compromise AI models used for diagnosis, documentation, and resource allocation. Existing privacy regulations, including the Health Insurance Portability and Accountability Act and the General Data Protection Regulation, may inadvertently complicate anomaly detection and cross-institutional auditing, thereby limiting visibility into adversarial activity. This study provides a comprehensive threat analysis of data poisoning vulnerabilities across major health care AI architectures. The goals are to (1) identify attack surfaces in clinical AI systems, (2) evaluate the feasibility and detectability of poisoning attacks analytically modeled in prior security research, and (3) propose a multilayered defense framework appropriate for health care settings. We synthesized empirical findings from 41 key security studies published between 2019 and 2025 and integrated them into an analytical threat-modeling framework specific to health care. We constructed 8 hypothetical yet technically grounded attack scenarios across 4 categories: (1) architecture-specific attacks on convolutional neural networks, large language models, and reinforcement learning agents (scenario A); (2) infrastructure exploitation in federated learning and clinical documentation pipelines (scenario B); (3) poisoning of critical resource allocation systems (scenario C); and (4) supply chain attacks affecting commercial foundation models (scenario D). Scenarios were aligned with realistic insider-access threat models and current clinical deployment practices. Multiple empirical studies demonstrate that attackers with access to as few as 100-500 poisoned samples can compromise health care AI systems, with attack success rates typically ≥60%. Critically, attack success depends on the absolute number of poisoned samples rather than their proportion of the training corpus, a finding that fundamentally challenges assumptions that larger datasets provide inherent protection. We estimate that detection delays commonly range from 6 to 12 months and may extend to years in distributed or privacy-constrained environments. Analytical scenarios highlight that (1) routine insider access creates numerous injection points across health care data infrastructure, (2) federated learning amplifies risks by obscuring attribution, and (3) supply chain compromises can simultaneously affect dozens to hundreds of institutions. Privacy regulations further complicate cross-patient correlation and model audit processes, substantially delaying the detection of subtle poisoning campaigns. Health care AI systems face significant security challenges that current regulatory frameworks and validation practices do not adequately address. We propose a multilayered defense strategy that combines ensemble disagreement monitoring, adversarial testing, privacy-preserving yet auditable mechanisms, and strengthened governance requirements. Ensuring patient safety may require a shift from opaque, high-performance models toward more interpretable and constraint-driven architectures with verifiable robustness guarantees.

The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) is enabling a new class of intelligent applications. Specifically, Large Language Models (LLMs) are emerging as powerful tools not only for natural language understanding but also for enhancing IoT security. However, the integration of these computationally intensive models into resource-constrained IoT environments presents significant challenges. This paper provides an in-depth examination of how LLMs can be adapted to secure IoT ecosystems. We identify key application areas, discuss major challenges, and propose optimization strategies for resource-limited settings. Our primary contribution is a novel collaborative embeddings offloading mechanism for IoT intrusion detection named SEED (Semantic Embeddings for Efficient Detection). This system leverages a lightweight, fine-tuned BERT model, chosen for its proven contextual and semantic understanding of sequences, to generate rich network embeddings at the edge. A compact neural network deployed on the end-device then queries these embeddings to assess network flow normality. This architecture alleviates the computational burden of running a full transformer on the device while capitalizing on its analytical performance. Our optimized BERT model is reduced by approximately 90% from its original size, now representing approximately 41 MB, suitable for the Edge. The resulting compact neural network is a mere 137 KB, appropriate for the IoT devices. This system achieves 99.9% detection accuracy with an average inference time of under 70 ms on a standard CPU. Finally, the paper discusses the ethical implications of LLM-IoT integration and evaluates the resilience of LLMs in dynamic and adversarial environments.

As artificial intelligence (AI) systems increasingly enter trust‐dependent domains, questions arise about whether their behaviour reflects genuine trustworthiness or merely the illusion of it. This study examined how humans and large language models (LLMs) establish and adjust trust in dynamic social interactions using a 50‐round trust game. Across 100 human participants and three leading LLMs—ChatGPT‐3.5, ChatGPT‐4o and DeepSeek‐V3—we compared trust trajectories, responsiveness to partner behaviour and reactions to unexpected outcomes. Human participants adjusted trust in line with partner trustworthiness and exhibited symmetrical responses to unexpected gains and violations. In contrast, LLMs showed fixed, model‐specific behaviour with little to no adaptation based on interaction history. Despite their cooperative appearance, AI agents lacked mechanisms for social learning and trust calibration. These findings highlight a fundamental disconnect between perceived and actual AI behaviour and underscore the need for cautious interpretation of AI trust signals in socially sensitive contexts.

The variability in responses generated by Large Language Models (LLMs) like OpenAI’s GPT-4 poses challenges in ensuring consistent accuracy on medical knowledge assessments, such as the United States Medical Licensing Exam (USMLE). This study introduces a novel multi-agent framework—referred to as a "Council of AIs"—to enhance LLM performance through collaborative decision-making. The Council consists of multiple GPT-4 instances that iteratively discuss and reach consensus on answers facilitated by a designated "Facilitator AI." This methodology was applied to 325 USMLE questions across Step 1, Step 2 Clinical Knowledge (CK), and Step 3 exams. The Council achieved consensus responses that were correct 97%, 93%, and 94% of the time for Step 1, Step 2CK, and Step 3, respectively, outperforming single-instance GPT-4 models. In cases where there wasn’t an initial unanimous response, the Council of AI deliberations achieved a consensus that was the correct answer 83% of the time. For questions that required deliberation, the Council corrected over half (53%) of responses that majority vote had gotten incorrect. At the end of deliberation, the Council often corrected majority responses that were initially incorrect: the odds of a majority voting response changing from incorrect to correct were 5 (95% CI: 1.1, 22.8) times higher than the odds of changing from correct to incorrect after discussion. We additionally characterized the semantic entropy of the response space for each question and found that deliberations impact entropy of the response space and steadily decrease it, consistently reaching an entropy of zero in all instances. This study showed that in a Council model response variability—often viewed as a limitation—could be leveraged as a strength, enabling adaptive reasoning and collaborative refinement of answers. These findings suggest new paradigms for AI implementation and reveal diversity of responses as a strength in collective decision-making even in medical question scenarios where there is a single correct response.Author SummaryIn our study, we explored how collaboration among multiple artificial intelligence (AI) systems could improve accuracy on medical licensing exams. While individual AI models like GPT-4 often produce varying answers to the same question—a challenge known as "response variability"—we designed a "Council of AIs" to turn this variability into a strength. The Council consists of several AI models working together, discussing their answers through an iterative process until they reach consensus.When tested on 325 medical exam questions, the Council achieved 97%, 93%, and 94% accuracy on the Step 1, Step 2CK, and Step 3, respectively. This improvement was most notable when answers required debate: in cases where initial responses disagreed, the collaborative process corrected errors 83% of the time. Our findings suggest that collective decision-making— even among AIs—can enhance accuracy and AI collaboration can potentially lead to more trustworthy tools for healthcare, where accuracy is critical. By demonstrating that diverse AI perspectives can refine answers, we challenge the notion that consistency alone defines a "good" AI. Instead, embracing variability through teamwork might unlock new possibilities for AI in medicine and beyond. This approach could inspire future systems where AIs and humans collaborate (e.g. on Councils with both humans and AIs), combining strengths to solve complex problems. While technical challenges remain, our work highlights a promising path toward more robust, adaptable AI solutions.

BackgroundThe exponential growth of medical knowledge presents a paradox for modern medical education. While access to information is immediate, applying it in a clinically meaningful way remains a challenge. Large language models (LLMs), such as ChatGPT, are widely used for information retrieval, yet their role in dynamic, high-pressure clinical learning remains poorly understood.ObjectiveThis study aims to evaluate whether unstructured access to an LLM improves decision-making, teamwork, and confidence in trauma education for medical students.MethodsThis randomized controlled pilot study involved 41 final-year medical students participating in a trauma simulation session. Students self-selected into teams of 4 to 6 and were randomized to either an LLM-assisted group (ChatGPT-4o mini) or a control group without LLM access. All teams completed 18 video-based trauma scenarios requiring time-sensitive clinical decisions. Prompting was unrestricted. Confidence and trauma exposure were assessed using pre- or postquestionnaires. Facilitators rated teamwork (1-5), decision accuracy, and response times. Knowledge retention was measured 4 weeks later via an online quiz.ResultsConfidence in trauma management improved in both groups (P<.001), with larger gains in the non-LLM group (P=.02). LLM support did not enhance the decision accuracy or speed and was associated with longer response times in some complex cases. Teams without LLMs demonstrated more active discussion and scored higher in teamwork ratings (median 5.0 [IQR 5.0-5.0] vs median 3.5 [IQR 3.0-4.5]; P=.08). Students primarily used the LLM for fact-checking but reported vague or overly general responses. Knowledge retention was high across both groups and did not differ significantly (P=.33).ConclusionsWhile students appreciated the inclusion of artificial intelligence (AI), unstructured LLM use did not improve performance and may have disrupted the group reasoning. The use of non-English prompting likely contributed to lower AI performance, underscoring the importance of language alignment in LLM applications. This pilot study highlights the need for structured AI integration and targeted instruction in AI literacy. Simulation-based trauma education proved effective and well received, but optimizing the educational value of LLMs will require thoughtful curricular design. Further studies with more students are needed to define best practices for LLM use in clinical education.

With the rapid evolution of artificial intelligence (AI), its potential implications for higher education have become a focal point of interest. This study delves into the capabilities of AI in physics education and offers actionable AI policy recommendations. Using openAI’s flagship gpt-3.5-turbo large language model (LLM), we assessed its ability to answer 1337 physics exam questions spanning general certificate of secondary education (GCSE), A-Level, and introductory university curricula. We employed various AI prompting techniques: Zero Shot, in context learning, and confirmatory checking, which merges chain of thought reasoning with reflection. The proficiency of gpt-3.5-turbo varied across academic levels: it scored an average of 83.4% on GCSE, 63.8% on A-Level, and 37.4% on university-level questions, with an overall average of 59.9% using the most effective prompting technique. In a separate test, the LLM’s accuracy on 5000 mathematical operations was found to be 45.2%. When evaluated as a marking tool, the LLM’s concordance with human markers averaged at 50.8%, with notable inaccuracies in marking straightforward questions, like multiple-choice. Given these results, our recommendations underscore caution: while current LLMs can consistently perform well on physics questions at earlier educational stages, their efficacy diminishes with advanced content and complex calculations. LLM outputs often showcase novel methods not in the syllabus, excessive verbosity, and miscalculations in basic arithmetic. This suggests that at university, there’s no substantial threat from LLMs for non-invigilated physics questions. However, given the LLMs’ considerable proficiency in writing physics essays and coding abilities, non-invigilated examinations of these skills in physics are highly vulnerable to automated completion by LLMs. This vulnerability also extends to pysics questions pitched at lower academic levels. It is thus recommended that educators be transparent about LLM capabilities with their students, while emphasizing caution against overreliance on their output due to its tendency to sound plausible but be incorrect.

This research presents an in-depth hardware compatibility evaluation of Meta's Llama 3.2 model on ten varied tasks—from long-context summarization and code generation to multi-turn dialogue and structured output generation—to discern performance trends and computational bottlenecks. Outputs show that hardware requirements differ widely by task type: long-context processing (1,915 input tokens) maximizes memory bandwidth (34% utilization), whereas code generation (659 tokens) and creative storytelling (2,321 tokens) maximize Central Processing Unit (CPU) parallelism (355% utilization on 4 cores). Multi-turn dialog shows growing latency (45 → 58 seconds) and token output increase (115→846 tokens) due to the compounding overhead of context retention, highlighting inefficiency in recurrent attention mechanisms. Repetitive work (1,000 "A"s) shows excellent token throughput (373 tokens/47 seconds), in contrast with mathematical calculations (square root of 123,456,789 × π), which overwhelm CPUs out of proportion (382% utilization) with trivial outputs, indicating Large Language Models (LLM) weakness in numeric accuracy. Ordered outputs (JSON) and translation work further reveal formatting-centric CPU overhead (355–382%), with tax policy questions (1,110 input tokens) uncovering context-parsing latency (53 seconds to process 148 tokens), highlighting semantic extraction inefficiencies from high-density texts. These results develop LLM work in three fronts: (1) Hardware Optimization, promoting task-specific settings (code/math with multi-core CPUs, plenty of RAM for context-intensive processes); (2) Model Architecture, calling for enhanced context handling and arithmetic blocks; and (3) Deployment Strategy, emphasizing resource allocation in keeping with use case operationalization (e.g., memory over legal/text analysis versus clock speed over creative applications). By correlating task types with hardware profiles, this research offers a framework for LLM scalability optimization, inference cost reduction, and informing future research into energy-efficient designs. The research highlights the need for interdisciplinary collaboration between Artificial Intelligence (AI) researchers and systems engineers to close the gap between algorithmic innovation and hardware capabilities, enabling sustainable large-scale LLM adoption.

Large language model (LLM) artificial intelligence (AI) systems have shown promise in diagnostic reasoning, but their utility in management reasoning with no clear right answers is unknown. To determine whether LLM assistance improves physician performance on open-ended management reasoning tasks compared to conventional resources. Prospective, randomized controlled trial conducted from 30 November 2023 to 21 April 2024. Multi-institutional study from Stanford University, Beth Israel Deaconess Medical Center, and the University of Virginia involving physicians from across the United States. 92 practicing attending physicians and residents with training in internal medicine, family medicine, or emergency medicine. Five expert-developed clinical case vignettes were presented with multiple open-ended management questions and scoring rubrics created through a Delphi process. Physicians were randomized to use either GPT-4 via ChatGPT Plus in addition to conventional resources (e.g., UpToDate, Google), or conventional resources alone. The primary outcome was difference in total score between groups on expert-developed scoring rubrics. Secondary outcomes included domain-specific scores and time spent per case. Physicians using the LLM scored higher compared to those using conventional resources (mean difference 6.5 %, 95% CI 2.7-10.2, p<0.001). Significant improvements were seen in management decisions (6.1%, 95% CI 2.5-9.7, p=0.001), diagnostic decisions (12.1%, 95% CI 3.1-21.0, p=0.009), and case-specific (6.2%, 95% CI 2.4-9.9, p=0.002) domains. GPT-4 users spent more time per case (mean difference 119.3 seconds, 95% CI 17.4-221.2, p=0.02). There was no significant difference between GPT-4-augmented physicians and GPT-4 alone (-0.9%, 95% CI -9.0 to 7.2, p=0.8). LLM assistance improved physician management reasoning compared to conventional resources, with particular gains in contextual and patient-specific decision-making. These findings indicate that LLMs can augment management decision-making in complex cases. ClinicalTrials.gov Identifier: NCT06208423; https://classic.clinicaltrials.gov/ct2/show/NCT06208423.

It has been forecasted that a quarter of the world’s energy usage will be supplied from Offshore Wind (OSW) by 2050 (Smith 2023). Given that up to one third of Levelised Cost of Energy (LCOE) arises from Operations and Maintenance (O&amp;M), the motive for cost reduction is enormous. In typical OSW farms hundreds of alarms occur within a single day, making manual O&amp;M planning without automated systems costly and difficult. Increased pressure to ensure safety and high reliability in progressively harsher environments motivates the exploration of Artificial Intelligence (AI) and Machine Learning (ML) systems as aids to the task. We recently introduced a specialised conversational agent trained to interpret alarm sequences from Supervisory Control and Data Acquisition (SCADA) and recommend comprehensible repair actions (Walker et al. 2023). Building on recent advancements on Large Language Models (LLMs), we expand on this earlier work, fine tuning LLAMA (Touvron 2018), using available maintenance records from EDF Energy. An issue presented by LLMs is the risk of responses containing unsafe actions, or irrelevant hallucinated procedures. This paper proposes a novel framework for safety monitoring of OSW, combining previous work with additional safety layers. Generated responses of this agent are being filtered to prevent raw responses endangering personnel and the environment. The algorithm represents such responses in embedding space to quantify dissimilarity to predefined unsafe concepts using the Empirical Cumulative Distribution Function (ECDF). A second layer identifies hallucination in responses by exploiting probability distributions to analyse against stochastically generated sentences. Combining these layers, the approach finetunes individual safety thresholds based on categorised concepts, providing a unique safety filter. The proposed framework has potential to utilise the O&amp;M planning for OSW farms using state-of-the-art LLMs as well as equipping them with safety monitoring that can increase technology acceptance within the industry.

The high performance of generative artificial intelligence (AI) and large language models (LLM) in examination contexts has triggered an intense debate about their applications, effects and risks. What legal aspects need to be considered when using LLM in teaching and assessment? What possibilities do language models offer? Statutes and laws are used to assess the use of LLM: - University statutes, state higher education laws, licensing regulations for doctors - Copyright Act (UrhG) - General Data Protection Regulation (DGPR) - AI Regulation (EU AI Act) LLM and AI offer opportunities but require clear university frameworks. These should define legitimate uses and areas where use is prohibited. Cheating and plagiarism violate good scientific practice and copyright laws. Cheating is difficult to detect. Plagiarism by AI is possible. Users of the products are responsible. LLM are effective tools for generating exam questions. Nevertheless, careful review is necessary as even apparently high-quality products may contain errors. However, the risk of copyright infringement with AI-generated exam questions is low, as copyright law allows up to 15% of protected works to be used for teaching and exams. The grading of exam content is subject to higher education laws and regulations and the GDPR. Exclusively computer-based assessment without human review is not permitted. For high-risk applications in education, the EU's AI Regulation will apply in the future. When dealing with LLM in assessments, evaluation criteria for existing assessments can be adapted, as can assessment programmes, e.g. to reduce the motivation to cheat. LLM can also become the subject of the examination themselves. Teachers should undergo further training in AI and consider LLM as an addition.

Background/Objectives: Artificial intelligence (AI), particularly large language models (LLMs), has demonstrated versatility in various applications but faces challenges in specialized domains like neurology. This study evaluates a specialized LLM's capability and trustworthiness in complex neurological diagnosis, comparing its performance to neurologists in simulated clinical settings. Methods: We deployed GPT-4 Turbo (OpenAI, San Francisco, CA, US) through Neura (Sciense, New York, NY, US), an AI infrastructure with a dual-database architecture integrating "long-term memory" and "short-term memory" components on a curated neurological corpus. Five representative clinical scenarios were presented to 13 neurologists and the AI system. Participants formulated differential diagnoses based on initial presentations, followed by definitive diagnoses after receiving conclusive clinical information. Two senior academic neurologists blindly evaluated all responses, while an independent investigator assessed the verifiability of AI-generated information. Results: AI achieved a significantly higher normalized score (86.17%) compared to neurologists (55.11%, p < 0.001). For differential diagnosis questions, AI scored 85% versus 46.15% for neurologists, and for final diagnosis, 88.24% versus 70.93%. AI obtained 15 maximum scores in its 20 evaluations and responded in under 30 s compared to neurologists' average of 9 min. All AI-provided references were classified as relevant with no hallucinatory content detected. Conclusions: A specialized LLM demonstrated superior diagnostic performance compared to practicing neurologists across complex clinical challenges. This indicates that appropriately harnessed LLMs with curated knowledge bases can achieve domain-specific relevance in complex clinical disciplines, suggesting potential for AI as a time-efficient asset in clinical practice.