Understanding ironic utterances: A comprehensive examination of ChatGPT-4o

How Can <i>IJDS</i> Authors, Reviewers, and Editors Use (and Misuse) Generative AI?

Evaluating Artificial Intelligence Chatbots in Oral and Maxillofacial Surgery Board Exams: Performance and Potential

Advances in neural networks, deep learning, and artificial intelligence (AI) have progressed recently. Previous deep learning AI has been structured around domain-specific areas that are trained on dataset-specific areas of interest that yield high accuracy and precision. A new AI model using large language models (LLM) and nonspecific domain areas, ChatGPT (OpenAI), has gained attention. Although AI has demonstrated proficiency in managing vast amounts of data, implementation of that knowledge remains a challenge. (1) What percentage of Orthopaedic In-Training Examination questions can a generative, pretrained transformer chatbot (ChatGPT) answer correctly? (2) How does that percentage compare with results achieved by orthopaedic residents of different levels, and if scoring lower than the 10th percentile relative to 5th-year residents is likely to correspond to a failing American Board of Orthopaedic Surgery score, is this LLM likely to pass the orthopaedic surgery written boards? (3) Does increasing question taxonomy affect the LLM's ability to select the correct answer choices? This study randomly selected 400 of 3840 publicly available questions based on the Orthopaedic In-Training Examination and compared the mean score with that of residents who took the test over a 5-year period. Questions with figures, diagrams, or charts were excluded, including five questions the LLM could not provide an answer for, resulting in 207 questions administered with raw score recorded. The LLM's answer results were compared with the Orthopaedic In-Training Examination ranking of orthopaedic surgery residents. Based on the findings of an earlier study, a pass-fail cutoff was set at the 10th percentile. Questions answered were then categorized based on the Buckwalter taxonomy of recall, which deals with increasingly complex levels of interpretation and application of knowledge; comparison was made of the LLM's performance across taxonomic levels and was analyzed using a chi-square test. ChatGPT selected the correct answer 47% (97 of 207) of the time, and 53% (110 of 207) of the time it answered incorrectly. Based on prior Orthopaedic In-Training Examination testing, the LLM scored in the 40th percentile for postgraduate year (PGY) 1s, the eighth percentile for PGY2s, and the first percentile for PGY3s, PGY4s, and PGY5s; based on the latter finding (and using a predefined cutoff of the 10th percentile of PGY5s as the threshold for a passing score), it seems unlikely that the LLM would pass the written board examination. The LLM's performance decreased as question taxonomy level increased (it answered 54% [54 of 101] of Tax 1 questions correctly, 51% [18 of 35] of Tax 2 questions correctly, and 34% [24 of 71] of Tax 3 questions correctly; p = 0.034). Although this general-domain LLM has a low likelihood of passing the orthopaedic surgery board examination, testing performance and knowledge are comparable to that of a first-year orthopaedic surgery resident. The LLM's ability to provide accurate answers declines with increasing question taxonomy and complexity, indicating a deficiency in implementing knowledge. Current AI appears to perform better at knowledge and interpretation-based inquires, and based on this study and other areas of opportunity, it may become an additional tool for orthopaedic learning and education.

BACKGROUND Large language models (LLMs) provide new opportunities to advance the intelligent development of Traditional Chinese medicine (TCM). Syndrome differentiation thinking is an essential part of TCM, and equipping LLMs with this capability represents a crucial step toward more effective clinical applications of TCM. However, given the complexity of TCM syndrome differentiation thinking, acquiring this ability is a considerable challenge for the model. OBJECTIVE This study aims to evaluate LLMs' syndrome differentiation thinking ability and design a method to enhance their performance in this area effectively. METHODS We decompose the process of TCM syndrome differentiation thinking into three core tasks: pathogenesis inference, syndrome inference, and diagnostic suggestion. To evaluate the performance of LLMs in these tasks, we constructed a high-quality evaluation dataset, providing a reliable foundation for the quantitative assessment of their capabilities. Furthermore, we developed a methodology for generating instruction data based on the idea of an "open-book exam", customized three data templates, and dynamically retrieved task-relevant professional knowledge, inserted into predefined positions within the templates. This approach effectively generates high-quality instruction data that aligns with the unique characteristics of TCM syndrome differentiation thinking. Leveraging this instruction data, we fine-tuned the base model, enhancing the syndrome differentiation thinking ability of the LLMs. RESULTS We collected 200 medical cases for the evaluation dataset and standardized them into three types of task questions. We tested general and TCM LLMs, comparing their performance with our proposed solution. The results demonstrate that our method significantly enhances LLMs' syndrome differentiation thinking ability. Our model achieved 85.7% and 81.2% accuracy in Tasks 1 and 2, respectively, surpassing the best-performing TCM and general LLMs by 26.3% and 15.8%. In Task 3, our model scored 84.3, indicating that the model is very similar to the advice given by experts. CONCLUSIONS Existing general LLMs and TCM LLMs still have significant limitations in the core task of syndrome differentiation thinking. Our research shows that fine-tuning LLMs by designing professional instruction templates and generating high-quality instruction data can significantly improve their performance in core tasks. The optimized LLMs show a high degree of similarity in reasoning results with the opinions of domain experts, indicating that they can simulate syndrome differentiation thinking to a certain extent. This has important theoretical and practical significance for in-depth interpretation of the complexity of the clinical diagnosis and treatment process of TCM.

New large language models (LLMs) like ChatGPT have the potential to change qualitative research by contributing to every stage of the research process from generating interview questions to structuring research publications. However, it is far from clear whether such ‘assistance’ will enable or deskill and eventually displace the qualitative researcher. This paper sets out to explore the implications for qualitative research of the recently emerged capabilities of LLMs; how they have acquired their seemingly ‘human-like’ capabilities to ‘converse’ with us humans, and in what ways these capabilities are deceptive or misleading. Building on a comparison of the different ‘trainings’ of humans and LLMs, the paper first traces the seemingly human-like qualities of the LLM to the human proclivity to project communicative intent into or onto LLMs’ purely imitative capacity to predict the structure of human communication. It then goes on to detail the ways in which such human-like communication is deceptive and misleading in relation to the absolute ‘certainty’ with which LLMs ‘converse’, their intrinsic tendencies to ‘hallucination’ and ‘sycophancy’, the narrow conception of ‘artificial intelligence’, LLMs’ complete lack of ethical sensibility or capacity for responsibility, and finally the feared danger of an ‘emergence’ of ‘human-competitive’ or ‘superhuman’ LLM capabilities. The paper concludes by noting the potential dangers of the widespread use of LLMs as ‘mediators’ of human self-understanding and culture. A postscript offers a brief reflection on what only humans can do as qualitative researchers.

Evaluating the Performance of Large Language Models in Hematopoietic Stem Cell Transplantation Decision Making

As the health care industry increasingly embraces large language models (LLMs), understanding the consequence of this integration becomes crucial for maximizing benefits while mitigating potential pitfalls. This paper explores the evolving relationship among clinician trust in LLMs, the transition of data sources from predominantly human-generated to artificial intelligence (AI)–generated content, and the subsequent impact on the performance of LLMs and clinician competence. One of the primary concerns identified in this paper is the LLMs’ self-referential learning loops, where AI-generated content feeds into the learning algorithms, threatening the diversity of the data pool, potentially entrenching biases, and reducing the efficacy of LLMs. While theoretical at this stage, this feedback loop poses a significant challenge as the integration of LLMs in health care deepens, emphasizing the need for proactive dialogue and strategic measures to ensure the safe and effective use of LLM technology. Another key takeaway from our investigation is the role of user expertise and the necessity for a discerning approach to trusting and validating LLM outputs. The paper highlights how expert users, particularly clinicians, can leverage LLMs to enhance productivity by off-loading routine tasks while maintaining a critical oversight to identify and correct potential inaccuracies in AI-generated content. This balance of trust and skepticism is vital for ensuring that LLMs augment rather than undermine the quality of patient care. We also discuss the risks associated with the deskilling of health care professionals. Frequent reliance on LLMs for critical tasks could result in a decline in health care providers’ diagnostic and thinking skills, particularly affecting the training and development of future professionals. The legal and ethical considerations surrounding the deployment of LLMs in health care are also examined. We discuss the medicolegal challenges, including liability in cases of erroneous diagnoses or treatment advice generated by LLMs. The paper references recent legislative efforts, such as The Algorithmic Accountability Act of 2023, as crucial steps toward establishing a framework for the ethical and responsible use of AI-based technologies in health care. In conclusion, this paper advocates for a strategic approach to integrating LLMs into health care. By emphasizing the importance of maintaining clinician expertise, fostering critical engagement with LLM outputs, and navigating the legal and ethical landscape, we can ensure that LLMs serve as valuable tools in enhancing patient care and supporting health care professionals. This approach addresses the immediate challenges posed by integrating LLMs and sets a foundation for their maintainable and responsible use in the future.

Artificial intelligence (AI) large language models (LLMs) now produce human-like general text and images. LLMs' ability to generate persuasive scientific essays that undergo evaluation under traditional peer review has not been systematically studied. To measure perceptions of quality and the nature of authorship, we conducted a competitive essay contest in 2024 with both human and AI participants. Human authors and 4 distinct LLMs generated essays on controversial topics in stroke care and outcomes research. A panel of Stroke Editorial Board members (mostly vascular neurologists), blinded to author identity and with varying levels of AI expertise, rated the essays for quality, persuasiveness, best in topic, and author type. Among 34 submissions (22 human and 12 LLM) scored by 38 reviewers, human and AI essays received mostly similar ratings, though AI essays were rated higher for composition quality. Author type was accurately identified only 50% of the time, with prior LLM experience associated with improved accuracy. In multivariable analyses adjusted for author attributes and essay quality, only persuasiveness was independently associated with odds of a reviewer assigning AI as author type (adjusted odds ratio, 1.53 [95% CI, 1.09-2.16]; P=0.01). In conclusion, a group of experienced editorial board members struggled to distinguish human versus AI authorship, with a bias against best in topic for essays judged to be AI generated. Scientific journals may benefit from educating reviewers on the types and uses of AI in scientific writing and developing thoughtful policies on the appropriate use of AI in authoring manuscripts.

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

BACKGROUND. The American College of Radiology (ACR) Incidental Findings Committee (IFC) algorithm provides guidance for pancreatic cystic lesion (PCL) management. Its implementation using plain-text large language model (LLM) solutions is challenging given that key components include multimodal data (e.g., figures and tables). OBJECTIVE. The purpose of the study is to evaluate a multimodal LLM approach incorporating knowledge retrieval using flowchart embedding for forming follow-up recommendations for PCL management. METHODS. This retrospective study included patients who underwent abdominal CT or MRI from September 1, 2023, to September 1, 2024, and whose report mentioned a PCL. The reports' Findings sections were inputted to a multimodal LLM (GPT-4o). For task 1 (198 patients: mean age, 69.0 ± 13.0 [SD] years; 110 women, 88 men), the LLM assessed PCL features (presence of PCL, PCL size and location, presence of main pancreatic duct communication, presence of worrisome features or high-risk stigmata) and formed a follow-up recommendation using three knowledge retrieval methods (default knowledge, plain-text retrieval-augmented generation [RAG] from the ACR IFC algorithm PDF document, and flowchart embedding using the LLM's image-to-text conversion for in-context integration of the document's flowcharts and tables). For task 2 (85 patients: mean initial age, 69.2 ± 10.8 years; 48 women, 37 men), an additional relevant prior report was inputted; the LLM assessed for interval PCL change and provided an adjusted follow-up schedule accounting for prior imaging using flowchart embedding. Three radiologists assessed LLM accuracy in task 1 for PCL findings in consensus and follow-up recommendations independently; one radiologist assessed accuracy in task 2. RESULTS. For task 1, the LLM with flowchart embedding had accuracy for PCL features of 98.0-99.0%. The accuracy of the LLM follow-up recommendations based on default knowledge, plain-text RAG, and flowchart embedding for radiologist 1 was 42.4%, 23.7%, and 89.9% (p < .001), respectively; radiologist 2 was 39.9%, 24.2%, and 91.9% (p < .001); and radiologist 3 was 40.9%, 25.3%, and 91.9% (p < .001). For task 2, the LLM using flowchart embedding showed an accuracy for interval PCL change of 96.5% and for adjusted follow-up schedules of 81.2%. CONCLUSION. Multimodal flowchart embedding aided the LLM's automated provision of follow-up recommendations adherent to a clinical guidance document. CLINICAL IMPACT. The framework could be extended to other incidental findings through the use of other clinical guidance documents as the model input.

Machine learning promises versatile help in the creation of systematic reviews (SRs). Recently, further developments in the form of large language models (LLMs) and their application in SR conduct attracted attention. We aimed at providing an overview of LLM applications in SR conduct in health research. We systematically searched MEDLINE, Web of Science, IEEEXplore, ACM Digital Library, Europe PMC (preprints), Google Scholar, and conducted an additional hand search (last search: February 26, 2024). We included scientific articles in English or German, published from April 2021 onwards, building upon the results of a mapping review that has not yet identified LLM applications to support SRs. Two reviewers independently screened studies for eligibility; after piloting, 1 reviewer extracted data, checked by another. Our database search yielded 8054 hits, and we identified 33 articles from our hand search. We finally included 37 articles on LLM support. LLM approaches covered 10 of 13 defined SR steps, most frequently literature search (n = 15, 41%), study selection (n = 14, 38%), and data extraction (n = 11, 30%). The mostly recurring LLM was Generative Pretrained Transformer (GPT) (n = 33, 89%). Validation studies were predominant (n = 21, 57%). In half of the studies, authors evaluated LLM use as promising (n = 20, 54%), one-quarter as neutral (n = 9, 24%) and one-fifth as nonpromising (n = 8, 22%). Although LLMs show promise in supporting SR creation, fully established or validated applications are often lacking. The rapid increase in research on LLMs for evidence synthesis production highlights their growing relevance. Systematic reviews are a crucial tool in health research where experts carefully collect and analyze all available evidence on a specific research question. Creating these reviews is typically time- and resource-intensive, often taking months or even years to complete, as researchers must thoroughly search, evaluate, and synthesize an immense number of scientific studies. For the present article, we conducted a review to understand how new artificial intelligence (AI) tools, specifically large language models (LLMs) like Generative Pretrained Transformer (GPT), can be used to help create systematic reviews in health research. We searched multiple scientific databases and finally found 37 relevant articles. We found that LLMs have been tested to help with various parts of the systematic review process, particularly in 3 main areas: searching scientific literature (41% of studies), selecting relevant studies (38%), and extracting important information from these studies (30%). GPT was the most commonly used LLM, appearing in 89% of the studies. Most of the research (57%) focused on testing whether these AI tools actually work as intended in this context of systematic review production. The results were mixed: about half of the studies found LLMs promising, a quarter were neutral, and one-fifth found them not promising. While LLMs show potential for making the systematic review process more efficient, there is still a lack of fully tested and validated applications. However, the increasing number of studies in this field suggests that these AI tools are becoming increasingly important in creating systematic reviews.

Getting AI Right: Introductory Notes on AI &amp; Society

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.

A large language model (LLM) provides new opportunities to advance the intelligent development of traditional Chinese medicine (TCM). Syndrome differentiation thinking is an essential part of TCM and equipping LLMs with this capability represents a crucial step toward more effective clinical applications of TCM. However, given the complexity of TCM syndrome differentiation thinking, acquiring this ability is a considerable challenge for the model. This study aims to evaluate the ability of LLMs for syndrome differentiation thinking and design a method to effectively enhance their performance in this area. We decomposed the process of syndrome differentiation thinking in TCM into three core tasks: pathogenesis inference, syndrome inference, and diagnostic suggestion. To evaluate the performance of LLMs in these tasks, we constructed a high-quality evaluation dataset, forming a reliable foundation for quantitative assessment of their capabilities. Furthermore, we developed a methodology for generating instruction data based on the idea of an "open-book exam," customized three data templates, and dynamically retrieved task-relevant professional knowledge that was inserted into predefined positions within the templates. This approach effectively generates high-quality instruction data that aligns with the unique characteristics of TCM syndrome differentiation thinking. Leveraging this instruction data, we fine-tuned the base model, enhancing the syndrome differentiation thinking ability of the LLMs. We collected 200 medical cases for the evaluation dataset and standardized them into three types of task questions. We tested general and TCM-specific LLMs, comparing their performance with our proposed solution. The findings demonstrated that our method significantly enhanced LLMs' syndrome differentiation thinking. Our model achieved 85.7% in Task 1 and 81.2% accuracy in Task 2, surpassing the best-performing TCM and general LLMs by 26.3% and 15.8%, respectively. In Task 3, our model achieved a similarity score of 84.3, indicating that the model was remarkably similar to advice given by experts. Existing general LLMs and TCM-specific LLMs continue to have significant limitations in the core task of syndrome differentiation thinking. Our research shows that fine-tuning LLMs by designing professional instruction templates and generating high-quality instruction data can significantly improve their performance on core tasks. The optimized LLMs show a high degree of similarity in reasoning results, consistent with the opinions of domain experts, indicating that they can simulate syndrome differentiation thinking to a certain extent. These findings have important theoretical and practical significance for in-depth interpretation of the complexity of the clinical diagnosis and treatment process of TCM.

A critical challenge in modelling Heterogeneous-Agent Teams is training agents to collaborate with teammates whose policies are inaccessible or non-stationary, such as humans. Traditional approaches rely on expensive human-in-the-loop data, which limits scalability. We propose using Large Language Models (LLMs) as policy-agnostic human proxies to generate synthetic data that mimics human decision-making. To evaluate this, we conduct three experiments in a grid-world capture game inspired by Stag Hunt, a game theory paradigm that balances risk and reward. In Experiment 1, we compare decisions from 30 human participants and 2 expert judges with outputs from LLaMA 3.1 and Mixtral 8x22B models. LLMs, prompted with game-state observations and reward structures, align more closely with experts than participants, demonstrating consistency in applying underlying decision criteria. Experiment 2 modifies prompts to induce risk-sensitive strategies (e.g. “be risk averse”). LLM outputs mirror human participants’ variability, shifting between risk-averse and risk-seeking behaviours. Finally, Experiment 3 tests LLMs in a dynamic grid-world where the LLM agents generate movement actions. LLMs produce trajectories resembling human participants’ paths. While LLMs cannot yet fully replicate human adaptability, their prompt-guided diversity offers a scalable foundation for simulating policy-agnostic teammates.