ChatGPT in action: Harnessing artificial intelligence potential and addressing ethical challenges in medicine, education, and scientific research

Application of transformer architectures in generative video modeling for neurosurgical education

PURPOSE

This article explores the potential impact of OpenAI's Sora, a generative video modeling technology, on neurosurgical training. It evaluates how such technology could revolutionize the field by providing realistic surgical simulations, thereby enhancing the learning experience and proficiency in complex procedures for neurosurgical trainees.

METHODS

The study examines the incorporation of this technology into neurosurgical education by leveraging transformer architecture and processing of video and image data. It involves compiling a neurosurgical procedure dataset for model training, aiming to create accurate, high-fidelity simulations.

RESULTS

Our findings indicate significant potential applications in neurosurgical training, including immersive simulations for skill development and exposure to diverse surgical scenarios. The technology also promises to transform assessment and feedback, introducing a standardized, objective way to measure and improve trainee competencies.

CONCLUSION

Integrating generative video modeling technology into neurosurgical education marks a progressive step toward enhancing training methodologies. Despite challenges in technical, ethical, and practical domains, continuous development and evaluation could lead to substantial advancements in surgical education, preparing neurosurgeons more effectively for their demanding roles.

ChatGPT Answers the 110-Question Laboratory Enzymology Student Exam: Pass or Fail?

Introduction Chatbots like ChatGPT have attracted a lot of interest lately due to their ability to generate human-like responses. Their reliability and accuracy are still questionable, and they are the topic of many studies in different fields. Therefore, the aim of this study was to examine the knowledge of two versions of chatbots regarding laboratory enzymology and to compare it with the average knowledge of students for the purpose of considering the use of ChatGPT in providing answers in this field. Material and methods An exam with 110 questions covering four topics was answered by students and ChatGPT-3.5 and ChatGPT-4.0. The accuracy of the answers of 52 students and ChatGPT was evaluated. The accuracy of answers between students and artificial intelligence was compared, and the percentage of passing the exam was 60%. All responses were reviewed by two authors with full interrater agreement. Results Total scores for students, ChatGPT-3.5, and ChatGPT-4.0 were 85.46%, 52.73%, and 74.55% (p < 0.05), whereby ChatGPT-4.0 achieved better results compared to the other chatbot. ChatGPT-3.5 and ChatGPT-4.0 achieved the best results on questions about enzymes in metabolism. The lowest scores for both chatbots were observed in the laboratory analysis of enzymes. Conclusion ChatGPT showed average results in the Laboratory Enzymology exam and scored lower than students. This proved that chatbots could be a potential tool for learning and eventual implementation in higher and/or medical education with extensive optimization but still cannot replace a human.

[What is the potential of ChatGPT for qualified patient information? : Attempt of a structured analysis on the basis of a survey regarding complementary and alternative medicine (CAM) in rheumatology]

INTRODUCTION

The chatbot ChatGPT represents a milestone in the interaction between humans and large databases that are accessible via the internet. It facilitates the answering of complex questions by enabling a communication in everyday language. Therefore, it is a potential source of information for those who are affected by rheumatic diseases. The aim of our investigation was to find out whether ChatGPT (version 3.5) is capable of giving qualified answers regarding the application of specific methods of complementary and alternative medicine (CAM) in three rheumatic diseases: rheumatoid arthritis (RA), systemic lupus erythematosus (SLE) and granulomatosis with polyangiitis (GPA). In addition, it was investigated how the answers of the chatbot were influenced by the wording of the question.

METHODS

The questioning of ChatGPT was performed in three parts. Part A consisted of an open question regarding the best way of treatment of the respective disease. In part B, the questions were directed towards possible indications for the application of CAM in general in one of the three disorders. In part C, the chatbot was asked for specific recommendations regarding one of three CAM methods: homeopathy, ayurvedic medicine and herbal medicine. Questions in parts B and C were expressed in two modifications: firstly, it was asked whether the specific CAM was applicable at all in certain rheumatic diseases. The second question asked which procedure of the respective CAM method worked best in the specific disease. The validity of the answers was checked by using the ChatGPT reliability score, a Likert scale ranging from 1 (lowest validity) to 7 (highest validity).

RESULTS

The answers to the open questions of part A had the highest validity. In parts B and C, ChatGPT suggested a variety of CAM applications that lacked scientific evidence. The validity of the answers depended on the wording of the questions. If the question suggested the inclination to apply a certain CAM, the answers often lacked the information of missing evidence and were graded with lower score values.

CONCLUSION

The answers of ChatGPT (version 3.5) regarding the applicability of CAM in selected rheumatic diseases are not convincingly based on scientific evidence. In addition, the wording of the questions affects the validity of the information. Currently, an uncritical application of ChatGPT as an instrument for patient information cannot be recommended.

Prospects and perils of ChatGPT in diabetes

ChatGPT, a popular large language model developed by OpenAI, has the potential to transform the management of diabetes mellitus. It is a conversational artificial intelligence model trained on extensive datasets, although not specifically health-related. The development and core components of ChatGPT include neural networks and machine learning. Since the current model is not yet developed on diabetes-related datasets, it has limitations such as the risk of inaccuracies and the need for human supervision. Nevertheless, it has the potential to aid in patient engagement, medical education, and clinical decision support. In diabetes management, it can contribute to patient education, personalized dietary guidelines, and providing emotional support. Specifically, it is being tested in clinical scenarios such as assessment of obesity, screening for diabetic retinopathy, and provision of guidelines for the management of diabetic ketoacidosis. Ethical and legal considerations are essential before ChatGPT can be integrated into healthcare. Potential concerns relate to data privacy, accuracy of responses, and maintenance of the patient-doctor relationship. Ultimately, while ChatGPT and large language models hold immense potential to revolutionize diabetes care, one needs to weigh their limitations, ethical implications, and the need for human supervision. The integration promises a future of proactive, personalized, and patient-centric care in diabetes management.

Adoption of K-means clustering algorithm in smart city security analysis and mythical experience analysis of urban image

OBJECTIVE

An information security evaluation model based on the K-Means Clustering (KMC) +  Decision Tree (DT) algorithm is constructed, aiming to assess its value in evaluating smart city (SC) security. Additionally, the impact of SCs on individuals' mythical experiences is investigated.

METHODS

An information security analysis model based on the combination of KMC and DT algorithms is established. A total of 38 SCs are selected as the research objects for practical analysis. The practical feasibility of the model is assessed using the receiver operating characteristic (ROC) curve, and its performance is compared with that of the Naive Bayes (NB), Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM) classification methods. Lastly, a questionnaire survey is conducted to obtain and analyze individuals' mythical experiences in SCs.

RESULTS

(1) The area under the ROC curve is significantly higher than 0.9 (0.921 vs. 0.9). (2) Compared to the NB and LR algorithms, the security analysis model based on the combination of KMC and DT algorithms demonstrated higher true positive rate (TPR), accuracy, recall, F-Score, AUC-ROC, and AUC-PR. Additionally, the performance metrics of RF, SVM, and GBM are similar to those of the KMC+DT model. (3) When the attributes are the same, the difference in smart risk levels is small, while when the attributes are different, the difference in risk levels is significant. (4) The support rates for various types of new folk activities are as follows: offline shopping festivals (17.6%), New Year's Eve celebrations (16.7%), Tibet tourism (15.6%), spiritual practices (16.2%), green leisure (16.0%), and suburban/rural tourism (15.8%). (5) High-risk cities (Grade A) showed stronger support for modern activities such as offline shopping festivals and green leisure, while low-risk cities (Grades C and D) tended to favor traditional cultural activities.

CONCLUSION

The algorithm model constructed in this work is capable of effectively evaluating the information security risks of SCs and has practical value. A good city image and mythological experience are driving the development of cities.

Chat Generative Pre-Trained Transformer (ChatGPT) in Oral and Maxillofacial Surgery: A Narrative Review on Its Research Applications and Limitations

Objectives: This review aimed to evaluate the role of ChatGPT in original research articles within the field of oral and maxillofacial surgery (OMS), focusing on its applications, limitations, and future directions. Methods: A literature search was conducted in PubMed using predefined search terms and Boolean operators to identify original research articles utilizing ChatGPT published up to October 2024. The selection process involved screening studies based on their relevance to OMS and ChatGPT applications, with 26 articles meeting the final inclusion criteria. Results: ChatGPT has been applied in various OMS-related domains, including clinical decision support in real and virtual scenarios, patient and practitioner education, scientific writing and referencing, and its ability to answer licensing exam questions. As a clinical decision support tool, ChatGPT demonstrated moderate accuracy (approximately 70-80%). It showed moderate to high accuracy (up to 90%) in providing patient guidance and information. However, its reliability remains inconsistent across different applications, necessitating further evaluation. Conclusions: While ChatGPT presents potential benefits in OMS, particularly in supporting clinical decisions and improving access to medical information, it should not be regarded as a substitute for clinicians and must be used as an adjunct tool. Further validation studies and technological refinements are required to enhance its reliability and effectiveness in clinical and research settings.

Navigating the future of pediatric cardiovascular surgery: Insights and innovation powered by Chat Generative Pre-Trained Transformer (ChatGPT)

INTRODUCTION

Interdisciplinary consultations are essential to decision-making for patients with congenital heart disease. The integration of artificial intelligence (AI) and natural language processing into medical practice is rapidly accelerating, opening new avenues to diagnosis and treatment. The main objective of this study was to consult the AI-trained model Chat Generative Pre-Trained Transformer (ChatGPT) regarding cases discussed during a cardiovascular surgery conference (CSC) at a single tertiary center and compare the ChatGPT suggestions with CSC expert consensus results.

METHODS

In total, 37 cases discussed at a single CSC were retrospectively identified. Clinical information comprised deidentified data from the last electrocardiogram, echocardiogram, intensive care unit progress note (or cardiology clinic note if outpatient), as well as a patient summary. The diagnosis was removed from the summary and possible treatment options were deleted from all notes. ChatGPT (version 4.0) was asked to summarize the case, identify diagnoses, and recommend surgical procedures and timing of surgery. The responses of ChatGPT were compared with the results of the CSC.

RESULTS

Of the 37 cases uploaded to ChatGPT, 45.9% (n = 17) were considered to be less complex cases, with only 1 treatment option, and 54.1% (n = 20) were considered more complex, with several treatment options. ChatGPT correctly provided a detailed and systematically written summary for each case within 10 to 15 seconds. ChatGPT correctly identified diagnoses for approximately 94.5% (n = 35) cases. The surgical intervention plan matched the group decision for approximately 40.5% (n = 15) cases; however, it differed in 27% cases. In 23 of 37 cases, timing of surgery was the same between CSC group and ChatGPT. Overall, the match between ChatGPT responses and CSC decisions for diagnosis was 94.5%, surgical intervention was 40.5%, and timing of surgery was 62.2%. However, within complex cases, we have 25% agreement for surgical intervention and 67% for timing of surgery.

CONCLUSIONS

ChatGPT can be used as an augmentative tool for surgical conferences to systematically summarize large amounts of patient data from electronic health records and clinical notes in seconds. In addition, our study points out the potential of ChatGPT as an AI-based decision support tool in surgery, particularly for less-complex cases. The discrepancy, particularly in complex cases, emphasizes on the need for caution when using ChatGPT in decision-making for the complex cases in pediatric cardiovascular surgery. There is little doubt that the public will soon use this comparative tool.

Evaluating AI performance in nephrology triage and subspecialty referrals

Artificial intelligence (AI) has shown promise in revolutionizing medical triage, particularly in the context of the rising prevalence of kidney-related conditions with the aging global population. This study evaluates the utility of ChatGPT, a large language model, in triaging nephrology cases through simulated real-world scenarios. Two nephrologists created 100 patient cases that encompassed various aspects of nephrology. ChatGPT's performance in determining the appropriateness of nephrology consultations and identifying suitable nephrology subspecialties was assessed. The results demonstrated high accuracy; ChatGPT correctly determined the need for nephrology in 99-100% of cases, and it accurately identified the most suitable nephrology subspecialty triage in 96-99% of cases across two evaluation rounds. The agreement between the two rounds was 97%. While ChatGPT showed promise in improving medical triage efficiency and accuracy, the study also identified areas for refinement. This included the need for better integration of multidisciplinary care for patients with complex, intersecting medical conditions. This study's findings highlight the potential of AI in enhancing decision-making processes in clinical workflow, and it can inform the development of AI-assisted triage systems tailored to institution-specific practices including multidisciplinary approaches.

Knowledge, attitude, and perceptions of MENA researchers towards the use of ChatGPT in research: A cross-sectional study

Background

Artificial intelligence (AI) technologies are increasingly recognized for their potential to revolutionize research practices. However, there is a gap in understanding the perspectives of MENA researchers on ChatGPT. This study explores the knowledge, attitudes, and perceptions of ChatGPT utilization in research.

Methods

A cross-sectional survey was conducted among 369 MENA researchers. Participants provided demographic information and responded to questions about their knowledge of AI, their experience with ChatGPT, their attitudes toward technology, and their perceptions of the potential roles and benefits of ChatGPT in research.

Results

The results indicate a moderate level of knowledge about ChatGPT, with a total score of 58.3 ± 19.6. Attitudes towards its use were generally positive, with a total score of 68.1 ± 8.1 expressing enthusiasm for integrating ChatGPT into their research workflow. About 56 % of the sample reported using ChatGPT for various applications. In addition, 27.6 % expressed their intention to use it in their research, while 17.3 % have already started using it in their research. However, perceptions varied, with concerns about accuracy, bias, and ethical implications highlighted. The results showed significant differences in knowledge scores based on gender (p < 0.001), working country (p < 0.05), and work field (p < 0.01). Regarding attitude scores, there were significant differences based on the highest qualification and the employment field (p < 0.05). These findings underscore the need for targeted training programs and ethical guidelines to support the effective use of ChatGPT in research.

Conclusion

MENA researchers demonstrate significant awareness and interest in integrating ChatGPT into their research workflow. Addressing concerns about reliability and ethical implications is essential for advancing scientific innovation in the MENA region.

Human or Machine? A Comparative Analysis of Artificial Intelligence-Generated Writing Detection in Personal Statements

INTRODUCTION

This study examines the ability of human readers, recurrence quantification analysis (RQA), and an online artificial intelligence (AI) detection tool (GPTZero) to distinguish between AI-generated and human-written personal statements in physical therapist education program applications.

REVIEW OF LITERATURE

The emergence of large language models such as ChatGPT and Google Gemini has raised concerns about the authenticity of personal statements. Previous studies have reported varying degrees of success in detecting AI-generated text.

SUBJECTS

Data were collected from 50 randomly selected nonmatriculated individuals who applied to the Mayo Clinic School of Health Sciences Doctor of Physical Therapy Program during the 2021-2022 application cycle.

METHODS

Fifty personal statements from applicants were pooled with 50 Google Gemini-generated statements, then analyzed by 2 individuals, RQA, and GPTZero. RQA provided quantitative measures of lexical sophistication, whereas GPTZero used advanced machine learning algorithms to quantify AI-specific text characteristics.

RESULTS

Human raters demonstrated high agreement (κ = 0.92) and accuracy (97% and 99%). RQA parameters, particularly recurrence and max line, differentiated human- from AI-generated statements (areas under receiver operating characteristic [ROC] curve = 0.768 and 0.859, respectively). GPTZero parameters including simplicity, perplexity, and readability also differentiated human- from AI-generated statements (areas under ROC curve > 0.875).

DISCUSSION AND CONCLUSION

The study reveals that human raters, RQA, and GPTZero offer varying levels of accuracy in differentiating human-written from AI-generated personal statements. The findings could have important implications in academic admissions processes, where distinguishing between human- and AI-generated submissions is becoming increasingly important. Future research should explore integrating these methods to enhance the robustness and reliability of personal statement content evaluation across various domains. Three strategies for managing AI's role in applications-for applicants, governing organizations, and academic institutions-are provided to promote integrity and accountability in admission processes.

Artificial Intelligence Advancements in Cardiomyopathies: Implications for Diagnosis and Management of Arrhythmogenic Cardiomyopathy

PURPOSE OF REVIEW

This review aims to explore the emerging potential of artificial intelligence (AI) in refining risk prediction, clinical diagnosis, and treatment stratification for cardiomyopathies, with a specific emphasis on arrhythmogenic cardiomyopathy (ACM).

RECENT FINDINGS

Recent developments highlight the capacity of AI to construct sophisticated models that accurately distinguish affected from non-affected cardiomyopathy patients. These AI-driven approaches not only offer precision in risk prediction and diagnostics but also enable early identification of individuals at high risk of developing cardiomyopathy, even before symptoms occur. These models have the potential to utilise diverse clinical input datasets such as electrocardiogram recordings, cardiac imaging, and other multi-modal genetic and omics datasets. Despite their current underrepresentation in literature, ACM diagnosis and risk prediction are expected to greatly benefit from AI computational capabilities, as has been the case for other cardiomyopathies. As AI-based models improve, larger and more complicated datasets can be combined. These more complex integrated datasets with larger sample sizes will contribute to further pathophysiological insights, better disease recognition, risk prediction, and improved patient outcomes.

Using Large Language Models to Extract Core Injury Information From Emergency Department Notes

BACKGROUND

Injuries pose a significant global health challenge due to their high incidence and mortality rates. Although injury surveillance is essential for prevention, it is resource-intensive. This study aimed to develop and validate locally deployable large language models (LLMs) to extract core injury-related information from Emergency Department (ED) clinical notes.

METHODS

We conducted a diagnostic study using retrospectively collected data from January 2014 to December 2020 from two urban academic tertiary hospitals. One served as the derivation cohort and the other as the external test cohort. Adult patients presenting to the ED with injury-related complaints were included. Primary outcomes included classification accuracies for information extraction tasks related to injury mechanism, place of occurrence, activity, intent, and severity. We fine-tuned a single generalizable Llama-2 model and five distinct Bidirectional Encoder Representations from Transformers (BERT) models for each task to extract information from initial ED physician notes. The Llama-2 model was able to perform different tasks by modifying the instruction prompt. Data recorded in injury registries provided the gold standard labels. Model performance was assessed using accuracy and macro-average F1 scores.

RESULTS

The derivation and external test cohorts comprised 36,346 and 32,232 patients, respectively. In the derivation cohort's test set, the Llama-2 model achieved accuracies (95% confidence intervals) of 0.899 (0.889-0.909) for injury mechanism, 0.774 (0.760-0.789) for place of occurrence, 0.679 (0.665-0.694) for activity, 0.972 (0.967-0.977) for intent, and 0.935 (0.926-0.943) for severity. The Llama-2 model outperformed the BERT models in accuracy and macro-average F1 scores across all tasks in both cohorts. Imposing constraints on the Llama-2 model to avoid uncertain predictions further improved its accuracy.

CONCLUSION

Locally deployable LLMs, trained to extract core injury-related information from free-text ED clinical notes, demonstrated good performance. Generative LLMs can serve as versatile solutions for various injury-related information extraction tasks.

Beyond the Scalpel: Assessing ChatGPT's potential as an auxiliary intelligent virtual assistant in oral surgery

AI has revolutionized the way we interact with technology. Noteworthy advances in AI algorithms and large language models (LLM) have led to the development of natural generative language (NGL) systems such as ChatGPT. Although these LLM can simulate human conversations and generate content in real time, they face challenges related to the topicality and accuracy of the information they generate. This study aimed to assess whether ChatGPT-4 could provide accurate and reliable answers to general dentists in the field of oral surgery, and thus explore its potential as an intelligent virtual assistant in clinical decision making in oral surgery. Thirty questions related to oral surgery were posed to ChatGPT4, each question repeated 30 times. Subsequently, a total of 900 responses were obtained. Two surgeons graded the answers according to the guidelines of the Spanish Society of Oral Surgery, using a three-point Likert scale (correct, partially correct/incomplete, and incorrect). Disagreements were arbitrated by an experienced oral surgeon, who provided the final grade Accuracy was found to be 71.7%, and consistency of the experts' grading across iterations, ranged from moderate to almost perfect. ChatGPT-4, with its potential capabilities, will inevitably be integrated into dental disciplines, including oral surgery. In the future, it could be considered as an auxiliary intelligent virtual assistant, though it would never replace oral surgery experts. Proper training and verified information by experts will remain vital to the implementation of the technology. More comprehensive research is needed to ensure the safe and successful application of AI in oral surgery.

Artificial Intelligence Diagnosing of Oral Lichen Planus: A Comparative Study

Early diagnosis of oral lichen planus (OLP) is challenging, which traditionally is dependent on clinical experience and subjective interpretation. Artificial intelligence (AI) technology has been widely applied in objective and rapid diagnoses. In this study, we aim to investigate the potential of AI diagnosis in OLP and evaluate its effectiveness in improving diagnostic accuracy and accelerating clinical decision making. A total of 128 confirmed OLP patients were included, and lesion images from various anatomical sites were collected. The diagnosis was performed using AI platforms, including ChatGPT-4O, ChatGPT (Diagram-Date extension), and Claude Opus, for AI directly identification and AI pre-training identification. After OLP feature training, the diagnostic accuracy of the AI platforms significantly improved, with the overall recognition rates of ChatGPT-4O, ChatGPT (Diagram-Date extension), and Claude Opus increasing from 59%, 68%, and 15% to 77%, 80%, and 50%, respectively. Additionally, the pre-training recognition rates for buccal mucosa reached 94%, 93%, and 56%, respectively. However, the AI platforms performed less effectively when recognizing lesions in less common sites and complex cases; for instance, the pre-training recognition rates for the gums were only 60%, 60%, and 20%, demonstrating significant limitations. The study highlights the strengths and limitations of different AI technologies and provides a reference for future AI applications in oral medicine.

Assessing ChatGPT's cybersecurity implications in Saudi Arabian healthcare and education sectors: A comparative study

STUDY PURPOSE

This study aims to critically evaluate ChatGPT's impact on cybersecurity in healthcare and education sectors.

METHODS

This study employed a cross-sectional survey design, collecting data from healthcare and educational professionals in Saudi Arabia through a structured questionnaire, with 205 healthcare workers' and 214 educators. The survey assessed perceptions of ChatGPT's impact on cybersecurity opportunities and challenges, with data analyzed using descriptive statistics and ANOVA to explore differences across professional roles.

RESULTS

Healthcare professionals viewed artificial intelligence (AI) more favorably (mean scores 4.24 and 4.14) than those in education, who showed moderate enthusiasm (mean scores 2.55 to 3.54). Concerns over data privacy and the cost of securing AI were significant, with high mean scores of 3.59 indicating widespread apprehension.

CONCLUSION

A balanced approach to ChatGPT's integration that carefully considers ethical implications, data privacy, and the technology's dual-use potential is required.

The global evolution and impact of systems biology and artificial intelligence in stem cell research and therapeutics development: a scoping review

Advanced bioinformatics analysis, such as systems biology (SysBio) and artificial intelligence (AI) approaches, including machine learning (ML) and deep learning (DL), is increasingly present in stem cell (SC) research. An approximate timeline on these developments and their global impact is still lacking. We conducted a scoping review on the contribution of SysBio and AI analysis to SC research and therapy development based on literature published in PubMed between 2000 and 2024. We identified an 8 to 10-fold increase in research output related to all 3 search terms between 2000 and 2021, with a 10-fold increase in AI-related production since 2010. Use of SysBio and AI still predominates in preclinical basic research with increasing use in clinically oriented translational medicine since 2010. SysBio- and AI-related research was found all over the globe, with SysBio output led by the (US, n = 1487), (UK, n = 1094), Germany (n = 355), The Netherlands (n = 339), Russia (n = 215), and France (n = 149), while for AI-related research the US (n = 853) and UK (n = 258) take a strong lead, followed by Switzerland (n = 69), The Netherlands (n = 37), and Germany (n = 19). The US and UK are most active in SCs publications related to AI/ML and AI/DL. The prominent use of SysBio in ESC research was recently overtaken by prominent use of AI in iPSC and MSC research. This study reveals the global evolution and growing intersection among AI, SysBio, and SC research over the past 2 decades, with substantial growth in all 3 fields and exponential increases in AI-related research in the past decade.

Large language models in medicine: A review of current clinical trials across healthcare applications

This review analyzes current clinical trials investigating large language models' (LLMs) applications in healthcare. We identified 27 trials (5 published and 22 ongoing) across 4 main clinical applications: patient care, data handling, decision support, and research assistance. Our analysis reveals diverse LLM uses, from clinical documentation to medical decision-making. Published trials show promise but highlight accuracy concerns. Ongoing studies explore novel applications like patient education and informed consent. Most trials occur in the United States of America and China. We discuss the challenges of evaluating rapidly evolving LLMs through clinical trials and identify gaps in current research. This review aims to inform future studies and guide the integration of LLMs into clinical practice.

Artificial intelligence in endodontics: Data preparation, clinical applications, ethical considerations, limitations, and future directions

Artificial intelligence (AI) is emerging as a transformative technology in healthcare, including endodontics. A gap in knowledge exists in understanding AI's applications and limitations among endodontic experts. This comprehensive review aims to (A) elaborate on technical and ethical aspects of using data to implement AI models in endodontics; (B) elaborate on evaluation metrics; (C) review the current applications of AI in endodontics; and (D) review the limitations and barriers to real-world implementation of AI in the field of endodontics and its future potentials/directions. The article shows that AI techniques have been applied in endodontics for critical tasks such as detection of radiolucent lesions, analysis of root canal morphology, prediction of treatment outcome and post-operative pain and more. Deep learning models like convolutional neural networks demonstrate high accuracy in these applications. However, challenges remain regarding model interpretability, generalizability, and adoption into clinical practice. When thoughtfully implemented, AI has great potential to aid with diagnostics, treatment planning, clinical interventions, and education in the field of endodontics. However, concerted efforts are still needed to address limitations and to facilitate integration into clinical workflows.

A thorough examination of ChatGPT-3.5 potential applications in medical writing: A preliminary study

Effective communication of scientific knowledge plays a crucial role in the advancement of medical research and health care. Technological advancements have introduced large language models such as Chat Generative Pre-Trained Transformer (ChatGPT), powered by artificial intelligence (AI), which has already shown promise in revolutionizing medical writing. This study aimed to conduct a detailed evaluation of ChatGPT-3.5's role in enhancing various aspects of medical writing. From May 10 to 12, 2023, the authors engaged in a series of interactions with ChatGPT-3.5 to evaluate its effectiveness in various tasks, particularly its application to medical writing, including vocabulary enhancement, text rewriting for plagiarism prevention, hypothesis generation, keyword generation, title generation, article summarization, simplification of medical jargon, transforming text from informal to scientific and data interpretation. The exploration of ChatGPT's functionalities in medical writing revealed its potential in enhancing various aspects of the writing process, demonstrating its efficiency in improving vocabulary usage, suggesting alternative phrasing, and providing grammar enhancements. While the results indicate the effectiveness of ChatGPT (version 3.5), the presence of certain imperfections highlights the current indispensability of human intervention to refine and validate outputs, ensuring accuracy and relevance in medical settings. The integration of AI into medical writing shows significant potential for improving clarity, efficiency, and reliability. This evaluation highlights both the benefits and limitations of using ChatGPT-3.5, emphasizing its ability to enhance vocabulary, prevent plagiarism, generate hypotheses, suggest keywords, summarize articles, simplify medical jargon, and transform informal text into an academic format. However, AI tools should not replace human expertise. It is crucial for medical professionals to ensure thorough human review and validation to maintain the accuracy and relevance of the content in case they eventually use AI as a supplementary resource in medical writing. Accepting this mutually symbiotic partnership holds the promise of improving medical research and patient outcomes, and it sets the stage for the fusion of AI and human knowledge to produce a novel approach to medical assessment. Thus, while AI can streamline certain tasks, experienced medical writers and researchers must perform final reviews to uphold high standards in medical communications.

The Impact of Artificial Intelligence-Assisted Learning on Nursing Students' Ethical Decision-making and Clinical Reasoning in Pediatric Care: A Quasi-Experimental Study

The integration of artificial intelligence such as ChatGPT into educational frameworks marks a pivotal transformation in teaching. This quasi-experimental study, conducted in September 2023, aimed to evaluate the effects of artificial intelligence-assisted learning on nursing students' ethical decision-making and clinical reasoning. A total of 99 nursing students enrolled in a pediatric nursing course were randomly divided into two groups: an experimental group that utilized ChatGPT and a control group that used traditional textbooks. The Mann-Whitney U test was employed to assess differences between the groups in two primary outcomes: ( a ) ethical standards, focusing on the understanding and applying ethical principles, and ( b ) nursing processes, emphasizing critical thinking skills and integrating evidence-based knowledge. The control group outperformed the experimental group in ethical standards and demonstrated better clinical reasoning in nursing processes. Reflective essays revealed that the experimental group reported lower reliability but higher time efficiency. Despite artificial intelligence's ability to offer diverse perspectives, the findings highlight that educators must supplement artificial intelligence technology with strategies that enhance critical thinking, careful data selection, and source verification. This study suggests a hybrid educational approach combining artificial intelligence with traditional learning methods to bolster nursing students' decision-making processes and clinical reasoning skills.

Current Status of ChatGPT Use in Medical Education: Potentials, Challenges, and Strategies

ChatGPT, a generative pretrained transformer, has garnered global attention and sparked discussions since its introduction on November 30, 2022. However, it has generated controversy within the realms of medical education and scientific research. This paper examines the potential applications, limitations, and strategies for using ChatGPT. ChatGPT offers personalized learning support to medical students through its robust natural language generation capabilities, enabling it to furnish answers. Moreover, it has demonstrated significant use in simulating clinical scenarios, facilitating teaching and learning processes, and revitalizing medical education. Nonetheless, numerous challenges accompany these advancements. In the context of education, it is of paramount importance to prevent excessive reliance on ChatGPT and combat academic plagiarism. Likewise, in the field of medicine, it is vital to guarantee the timeliness, accuracy, and reliability of content generated by ChatGPT. Concurrently, ethical challenges and concerns regarding information security arise. In light of these challenges, this paper proposes targeted strategies for addressing them. First, the risk of overreliance on ChatGPT and academic plagiarism must be mitigated through ideological education, fostering comprehensive competencies, and implementing diverse evaluation criteria. The integration of contemporary pedagogical methodologies in conjunction with the use of ChatGPT serves to enhance the overall quality of medical education. To enhance the professionalism and reliability of the generated content, it is recommended to implement measures to optimize ChatGPT's training data professionally and enhance the transparency of the generation process. This ensures that the generated content is aligned with the most recent standards of medical practice. Moreover, the enhancement of value alignment and the establishment of pertinent legislation or codes of practice address ethical concerns, including those pertaining to algorithmic discrimination, the allocation of medical responsibility, privacy, and security. In conclusion, while ChatGPT presents significant potential in medical education, it also encounters various challenges. Through comprehensive research and the implementation of suitable strategies, it is anticipated that ChatGPT's positive impact on medical education will be harnessed, laying the groundwork for advancing the discipline and fostering the development of high-caliber medical professionals.

AI and Ethics: A Systematic Review of the Ethical Considerations of Large Language Model Use in Surgery Research

INTRODUCTION

As large language models receive greater attention in medical research, the investigation of ethical considerations is warranted. This review aims to explore surgery literature to identify ethical concerns surrounding these artificial intelligence models and evaluate how autonomy, beneficence, nonmaleficence, and justice are represented within these ethical discussions to provide insights in order to guide further research and practice.

METHODS

A systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Five electronic databases were searched in October 2023. Eligible studies included surgery-related articles that focused on large language models and contained adequate ethical discussion. Study details, including specialty and ethical concerns, were collected.

RESULTS

The literature search yielded 1179 articles, with 53 meeting the inclusion criteria. Plastic surgery, orthopedic surgery, and neurosurgery were the most represented surgical specialties. Autonomy was the most explicitly cited ethical principle. The most frequently discussed ethical concern was accuracy (n = 45, 84.9%), followed by bias, patient confidentiality, and responsibility.

CONCLUSION

The ethical implications of using large language models in surgery are complex and evolving. The integration of these models into surgery necessitates continuous ethical discourse to ensure responsible and ethical use, balancing technological advancement with human dignity and safety.

Using ChatGPT in Psychiatry to Design Script Concordance Tests in Undergraduate Medical Education: Mixed Methods Study

Background

Undergraduate medical studies represent a wide range of learning opportunities served in the form of various teaching-learning modalities for medical learners. A clinical scenario is frequently used as a modality, followed by multiple-choice and open-ended questions among other learning and teaching methods. As such, script concordance tests (SCTs) can be used to promote a higher level of clinical reasoning. Recent technological developments have made generative artificial intelligence (AI)-based systems such as ChatGPT (OpenAI) available to assist clinician-educators in creating instructional materials.

Objective

The main objective of this project is to explore how SCTs generated by ChatGPT compared to SCTs produced by clinical experts on 3 major elements: the scenario (stem), clinical questions, and expert opinion.

Methods

This mixed method study evaluated 3 ChatGPT-generated SCTs with 3 expert-created SCTs using a predefined framework. Clinician-educators as well as resident doctors in psychiatry involved in undergraduate medical education in Quebec, Canada, evaluated via a web-based survey the 6 SCTs on 3 criteria: the scenario, clinical questions, and expert opinion. They were also asked to describe the strengths and weaknesses of the SCTs.

Results

A total of 102 respondents assessed the SCTs. There were no significant distinctions between the 2 types of SCTs concerning the scenario (P=.84), clinical questions (P=.99), and expert opinion (P=.07), as interpretated by the respondents. Indeed, respondents struggled to differentiate between ChatGPT- and expert-generated SCTs. ChatGPT showcased promise in expediting SCT design, aligning well with Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria, albeit with a tendency toward caricatured scenarios and simplistic content.

Conclusions

This study is the first to concentrate on the design of SCTs supported by AI in a period where medicine is changing swiftly and where technologies generated from AI are expanding much faster. This study suggests that ChatGPT can be a valuable tool in creating educational materials, and further validation is essential to ensure educational efficacy and accuracy.

ChatGPT in finance: Applications, challenges, and solutions

The emergence of ChatGPT, a generative artificial intelligence tool, has sparked a revolution in the finance industry, enabling individuals to interact with technology in natural language. However, the use of ChatGPT in finance presents a profound array of ethical considerations that demand careful scrutiny to ensure its responsible and ethical use. After a concise exploration of ChatGPT's applications in finance, this policy article delves into the ethical challenges arising from the use of ChatGPT in finance, including outcomes contaminated with biases, incorporation of fake information in the financial decisions, concerns surrounding privacy and security, lack of transparency and accountability in the decision-making processes and financial services, human job displacement, and the intricate web of legal complexities. Our article asserts that financial institutions employing ChatGPT must proactively devise strategies to confront these burgeoning challenges, mitigating their adverse effects on both individuals and society as a whole. Additionally, we propose relevant policies to tackle these ethical quandaries head-on. In essence, this article illuminates the imperative need for a meticulous ethical framework, facilitating an informed and responsible use of ChatGPT in the realm of finance, safeguarding the welfare of individuals and society. While our work significantly contributes to the research and practice of finance, we also identify future research avenues.

Chain of Thought Utilization in Large Language Models and Application in Nephrology

Chain-of-thought prompting enhances the abilities of large language models (LLMs) significantly. It not only makes these models more specific and context-aware but also impacts the wider field of artificial intelligence (AI). This approach broadens the usability of AI, increases its efficiency, and aligns it more closely with human thinking and decision-making processes. As we improve this method, it is set to become a key element in the future of AI, adding more purpose, precision, and ethical consideration to these technologies. In medicine, the chain-of-thought prompting is especially beneficial. Its capacity to handle complex information, its logical and sequential reasoning, and its suitability for ethically and context-sensitive situations make it an invaluable tool for healthcare professionals. Its role in enhancing medical care and research is expected to grow as we further develop and use this technique. Chain-of-thought prompting bridges the gap between AI's traditionally obscure decision-making process and the clear, accountable standards required in healthcare. It does this by emulating a reasoning style familiar to medical professionals, fitting well into their existing practices and ethical codes. While solving AI transparency is a complex challenge, the chain-of-thought approach is a significant step toward making AI more comprehensible and trustworthy in medicine. This review focuses on understanding the workings of LLMs, particularly how chain-of-thought prompting can be adapted for nephrology's unique requirements. It also aims to thoroughly examine the ethical aspects, clarity, and future possibilities, offering an in-depth view of the exciting convergence of these areas.

Harnessing the benefits of ChatGPT for radiography education: A discussion paper

OBJECTIVE

Radiography education is pivotal in training skilled radiographers for diagnostic imaging and therapeutic applications. With technological advancements, interest in innovative educational tools to enhance traditional teaching methods is growing. This discussion paper explores the possibility of the integration of ChatGPT, a cutting-edge conversational AI language model, into radiography education.

KEY FINDINGS

We report that ChatGPT offers interactive learning opportunities that can facilitate learning. It also provides self-paced learning, revision platforms, and supports educators in scenario creation, assessment development, group collaboration, and professional and research activities. Despite these benefits, it is important to carefully consider issues related to academic integrity and privacy, along with the opportunities and challenges presented by this new technology in radiography education.

CONCLUSION

This paper highlights some of the prospects and limitations of the potential applications of ChatGPT in radiography education, underscoring the benefits for both students and educators. However, its implementation must be considered thoughtfully and ethically, taking into account its strengths and limitations.

IMPLICATIONS FOR PRACTICE

Integrating ChatGPT in radiography education has the potential to improve radiography education by improving digital literacy and graduate outcomes of students while streamlining the preparation process for educators. However, ethical implementation is vital for optimal outcomes.

ChatGPT as an aid for pathological diagnosis of cancer

Diagnostic workup of cancer patients is highly reliant on the science of pathology using cytopathology, histopathology, and other ancillary techniques like immunohistochemistry and molecular cytogenetics. Data processing and learning by means of artificial intelligence (AI) has become a spearhead for the advancement of medicine, with pathology and laboratory medicine being no exceptions. ChatGPT, an artificial intelligence (AI)-based chatbot, that was recently launched by OpenAI, is currently a talk of the town, and its role in cancer diagnosis is also being explored meticulously. Pathology workflow by integration of digital slides, implementation of advanced algorithms, and computer-aided diagnostic techniques extend the frontiers of the pathologist's view beyond a microscopic slide and enables effective integration, assimilation, and utilization of knowledge that is beyond human limits and boundaries. Despite of it's numerous advantages in the pathological diagnosis of cancer, it comes with several challenges like integration of digital slides with input language parameters, problems of bias, and legal issues which have to be addressed and worked up soon so that we as a pathologists diagnosing malignancies are on the same band wagon and don't miss the train.

Ethical Dilemmas in Using AI for Academic Writing and an Example Framework for Peer Review in Nephrology Academia: A Narrative Review

The emergence of artificial intelligence (AI) has greatly propelled progress across various sectors including the field of nephrology academia. However, this advancement has also given rise to ethical challenges, notably in scholarly writing. AI's capacity to automate labor-intensive tasks like literature reviews and data analysis has created opportunities for unethical practices, with scholars incorporating AI-generated text into their manuscripts, potentially undermining academic integrity. This situation gives rise to a range of ethical dilemmas that not only question the authenticity of contemporary academic endeavors but also challenge the credibility of the peer-review process and the integrity of editorial oversight. Instances of this misconduct are highlighted, spanning from lesser-known journals to reputable ones, and even infiltrating graduate theses and grant applications. This subtle AI intrusion hints at a systemic vulnerability within the academic publishing domain, exacerbated by the publish-or-perish mentality. The solutions aimed at mitigating the unethical employment of AI in academia include the adoption of sophisticated AI-driven plagiarism detection systems, a robust augmentation of the peer-review process with an "AI scrutiny" phase, comprehensive training for academics on ethical AI usage, and the promotion of a culture of transparency that acknowledges AI's role in research. This review underscores the pressing need for collaborative efforts among academic nephrology institutions to foster an environment of ethical AI application, thus preserving the esteemed academic integrity in the face of rapid technological advancements. It also makes a plea for rigorous research to assess the extent of AI's involvement in the academic literature, evaluate the effectiveness of AI-enhanced plagiarism detection tools, and understand the long-term consequences of AI utilization on academic integrity. An example framework has been proposed to outline a comprehensive approach to integrating AI into Nephrology academic writing and peer review. Using proactive initiatives and rigorous evaluations, a harmonious environment that harnesses AI's capabilities while upholding stringent academic standards can be envisioned.

The Role of ChatGPT in the Advancement of Diagnosis, Management, and Prognosis of Cardiovascular and Cerebrovascular Disease

Cardiovascular and cerebrovascular disease incidence has risen mainly due to poor control of preventable risk factors and still constitutes a significant financial and health burden worldwide. ChatGPT is an artificial intelligence language-based model developed by OpenAI. Due to the model's unique cognitive capabilities beyond data processing and the production of high-quality text, there has been a surge of research interest concerning its role in the scientific community and contemporary clinical practice. To fully exploit ChatGPT's potential benefits and reduce its possible misuse, extreme caution must be taken to ensure its implications ethically and equitably. In this narrative review, we explore the language model's possible applications and limitations while emphasizing its potential value for diagnosing, managing, and prognosis of cardiovascular and cerebrovascular disease.