Artificial Intelligence Platform Research Articles

Assessment of Artificial Intelligence Platforms With Regard to Medical Microbiology Knowledge: An Analysis of ChatGPT and Gemini.

The performance of two artificial intelligence (AI) platforms, ChatGPT 3.5 (OpenAI, California, United States) and Gemini (Google AI, California, United States) was assessed by answering 200 questions of microbiology drawn from validated sources. The questions were selected from topics such as General Microbiology, Immunology, and Microbiology Applied to Infectious Diseases. The study was conducted from December 2023 to March 2024, and the responses of the different AI platforms were compared with an answer key. Statistical analysis was performed to assess accuracy. ChatGPT 3.5 and Gemini had comparable accuracy with correct response scores of 71% and 70.5%, respectively. Their performance varied across different sections. Gemini performed better in General Microbiology and Immunology, and ChatGPT 3.5 had a better score in the Applied Microbiology section. The study's findings highlight that AI platforms such as ChatGPT and Gemini can be utilized in microbiology and medical education.The evolution and continuous updating of AI platforms are required to improve their performance.

Supporting L2 Writing Using QuillBot

As Artificial Intelligence (AI) grows in accessibility and popularity, educators are faced with the challenge to adopt this new form of information disbursement. AI platforms could benefit language learning by providing individual writing support in addition to classroom teacher instructional support. AI platforms could pose risks to student fidelity with AI produced work product. This paper will discuss and exemplify the use of QuillBot, an AI writing platform, that could be used as a teaching and learning tool for second language (L2) writing instruction. By using this technology to plan meaningful and student tailored lessons, educators can continue to provide an interactive learning space for multilingual learners (MLs) in the language classroom.

Time division multiplexing based multi-spectral semantic camera for LiDAR applications

The recent progress in the development of measurement systems for autonomous recognition had a substantial impact on emerging technology in numerous fields, especially robotics and automotive applications. In particular, time-of-flight (TOF) based light detection and ranging (LiDAR) systems enable to map the surrounding environmental information over long distances and with high accuracy. The combination of advanced LiDAR with an artificial intelligence platform allows enhanced object recognition and classification, which however still suffers from limitations of inaccuracy and misidentification. Recently, multi-spectral LiDAR systems have been employed to increase the object recognition performance by additionally providing material information in the short-wave infrared (SWIR) range where the reflection spectrum characteristics are typically very sensitive to material properties. However, previous multi-spectral LiDAR systems utilized band-pass filters or complex dispersive optical systems and even required multiple photodetectors, adding complexity and cost. In this work, we propose a time-division-multiplexing (TDM) based multi-spectral LiDAR system for semantic object inference by the simultaneous acquisition of spatial and spectral information. By utilizing the TDM method, we enable the simultaneous acquisition of spatial and spectral information as well as a TOF based distance map with minimized optical loss using only a single photodetector. Our LiDAR system utilizes nanosecond pulses of five different wavelengths in the SWIR range to acquire sufficient material information in addition to 3D spatial information. To demonstrate the recognition performance, we map the multi-spectral image from a human hand, a mannequin hand, a fabric gloved hand, a nitrile gloved hand, and a printed human hand onto an RGB-color encoded image, which clearly visualizes spectral differences as RGB color depending on the material while having a similar shape. Additionally, the classification performance of the multi-spectral image is demonstrated with a convolution neural network (CNN) model using the full multi-spectral data set. Our work presents a compact novel spectroscopic LiDAR system, which provides increased recognition performance and thus a great potential to improve safety and reliability in autonomous driving.

Indonesia-Malaysia Batik Pattern Collaboration Creation with Artificial Intelligence Platform

The problem with making batik in Indonesia and Malaysia so far is that it is still very manual and has not taken much advantage of technological advances. The development of Artificial Intelligence (AI) technology is currently very advanced, continues to innovate, and has even touched various aspects of human life. Limitations in ideas, human resources, knowledge, tools, and internet networks are the dominant conditions that cause problems in Indonesian and Malaysian batik creations. This study aims to use Artificial Intelligence to describe the combination of Indonesian and Malaysian batik patterns. This paper uses a descriptive-qualitative method with an experimental approach in the form of trials for the creation of batik patterns using the Imagine Artificial Intelligence platform; each trial is based on three basic patterns of batik, namely Sawat and Gurdha (Indonesia) and Orchid and Tiger (Malaysia). The results of this paper generated eight totally new Indonesian-Malaysian batik patterns. Artificial Intelligence (AI) was very helpful for the development of batik patterns. Through the creation of new batik patterns with the support of Artificial Intelligence (AI), it certainly creates new opportunities for the creation and business of batik to become more advanced. It is hoped that this paper will succeed in solving the problem of limited ideas in creating Indonesian-Malaysian batik motifs. Keywords: Artificial Intelligence, batik, creation, Indonesia-Malaysia

Assessment of Lung Health Status by Analyzing Cough Sound Using Swaasa Artificial Intelligence Technology

ABSTRACT Introduction: The crucial function of the respiratory system is the facilitation of blood gas exchange process. Spirometry is the diagnostic tool to detect and differentiate obstructive and restrictive respiratory diseases that impair this vital function. The need for a clinical setup, technical expertise, and patient compliance are certain limitations for utilizing spirometry in remote areas. Swaasa® artificial intelligence (AI) platform, which has a Class B manufacturing license from India’s Central Drugs Standard Control Organization, is a validated “software as a medical device,” which aids in screening and diagnosis of respiratory diseases, unbounded by location, equipment and technical expertise. Objectives: The objectives of the study were to determine the “lung health index” and the “pattern of lung health” conditions among nursing students. Materials and Methods: A cross-sectional study was conducted utilizing the Swaasa® AI platform among students of a nursing college in Central Karnataka in June 2023. Based on audiometric analysis of cough sounds, parameters such as underlying respiratory condition, cough count values, and respiratory symptoms experienced, ‘Swaasa’ AI platform derives pattern of respiratory condition (normal/obstructive/restrictive/mixed), ‘Lung Health Index,’ and presence or absence of lung health risk. Data collected in the Swaasa app were downloaded and analyzed using SPSS.v. 20. Results: Lung health risk was present in 58.2% of participants. Abnormal lung pattern was noted in 21.3% of participants (obstructive: 14.8%, restrictive: 2.5%, and mixed pattern: 4.1%). Conclusion: The Swaasa AI platform was utilized in this study to self-test lung health in real time without the need for a clinical setting. A high lung health index was found among 18% of participants. Studies adopting such cough sound analysis involving a larger population from wider geographic areas have to be conducted to detect and monitor respiratory diseases to increase its usage among the medical community in everyday clinical practice and also in remote areas.

Validation of the Quality Analysis of Medical Artificial Intelligence (QAMAI) tool: a new tool to assess the quality of health information provided by AI platforms.

The widespread diffusion of Artificial Intelligence (AI) platforms is revolutionizing how health-related information is disseminated, thereby highlighting the need for tools to evaluate the quality of such information. This study aimed to propose and validate the Quality Assessment of Medical Artificial Intelligence (QAMAI), a tool specifically designed to assess the quality of health information provided by AI platforms. The QAMAI tool has been developed by a panel of experts following guidelines for the development of new questionnaires. A total of 30 responses from ChatGPT4, addressing patient queries, theoretical questions, and clinical head and neck surgery scenarios were assessed by 27 reviewers from 25 academic centers worldwide. Construct validity, internal consistency, inter-rater and test-retest reliability were assessed to validate the tool. The validation was conducted on the basis of 792 assessments for the 30 responses given by ChatGPT4. The results of the exploratory factor analysis revealed a unidimensional structure of the QAMAI with a single factor comprising all the items that explained 51.1% of the variance with factor loadings ranging from 0.449 to 0.856. Overall internal consistency was high (Cronbach's alpha = 0.837). The Interclass Correlation Coefficient was 0.983 (95% CI 0.973-0.991; F (29,542) = 68.3; p < 0.001), indicating excellent reliability. Test-retest reliability analysis revealed a moderate-to-strong correlation with a Pearson's coefficient of 0.876 (95% CI 0.859-0.891; p < 0.001). The QAMAI tool demonstrated significant reliability and validity in assessing the quality of health information provided by AI platforms. Such a tool might become particularly important/useful for physicians as patients increasingly seek medical information on AI platforms.

Harnessing large language models for coding, teaching and inclusion to empower research in ecology and evolution

Abstract Large language models (LLMs) are a type of artificial intelligence (AI) that can perform various natural language processing tasks. The adoption of LLMs has become increasingly prominent in scientific writing and analyses because of the availability of free applications such as ChatGPT. This increased use of LLMs not only raises concerns about academic integrity but also presents opportunities for the research community. Here we focus on the opportunities for using LLMs for coding in ecology and evolution. We discuss how LLMs can be used to generate, explain, comment, translate, debug, optimise and test code. We also highlight the importance of writing effective prompts and carefully evaluating the outputs of LLMs. In addition, we draft a possible road map for using such models inclusively and with integrity. LLMs can accelerate the coding process, especially for unfamiliar tasks, and free up time for higher level tasks and creative thinking while increasing efficiency and creative output. LLMs also enhance inclusion by accommodating individuals without coding skills, with limited access to education in coding, or for whom English is not their primary written or spoken language. However, code generated by LLMs is of variable quality and has issues related to mathematics, logic, non‐reproducibility and intellectual property; it can also include mistakes and approximations, especially in novel methods. We highlight the benefits of using LLMs to teach and learn coding, and advocate for guiding students in the appropriate use of AI tools for coding. Despite the ability to assign many coding tasks to LLMs, we also reaffirm the continued importance of teaching coding skills for interpreting LLM‐generated code and to develop critical thinking skills. As editors of MEE, we support—to a limited extent—the transparent, accountable and acknowledged use of LLMs and other AI tools in publications. If LLMs or comparable AI tools (excluding commonly used aids like spell‐checkers, Grammarly and Writefull) are used to produce the work described in a manuscript, there must be a clear statement to that effect in its Methods section, and the corresponding or senior author must take responsibility for any code (or text) generated by the AI platform.

ChatGPT: May Help Inform Patients in Dental Implantology.

Patients may have high expectations regarding dental implants based on the source of their information, which can lead to challenges in clinical communication. This study aims to evaluate the quality of responses provided by Chat Generative Pre-trained Transformer (ChatGPT, OpenAI, USA), an artificial intelligence program, to patient questions in the field of dental implantology. This study was prospectively designed as a crosssectional study. Frequently asked questions by patients about general information on dental implantology (Part 1) and dental implant brands (Part 2) were posed to the ChatGPT program. Responses were independently assessed by oral and maxillofacial surgeons (Group 1: n=10), periodontologists (Group 2: n=10), prosthodontists (Group 3: n=10), and general dentists (Group 4: n=10) using the Global Quality Scale (GSQ). There were a total of 60 questions, with 30 questions in each part. Participants in the study were evenly distributed by gender (50% female, 50% male) with a mean age of 32.6±4.07 years. The mean years of experience were 8.2±3.12 years. There were no significant differences in mean age, gender, and years of experience among the groups (p>0.05). The overall mean GSQ score was 3.87±0.29. Part 1 had a mean score of 3.9±0.35, and Part 2 had a mean score of 3.85±0.29, with no statistically significant difference (p>0.05). The artificial intelligence platform may contribute to the additional education of patients in the field of dental implantology and aid in understanding treatment procedures. However, it is concerning that ChatGPT may exhibit bias regarding dental implant brands, which could impact patient guidance.

Synchronous Diagnosis of Diabetic Retinopathy by a Handheld Retinal Camera, Artificial Intelligence, and Simultaneous Specialist Confirmation

PurposeDiabetic retinopathy (DR) is a leading cause of preventable blindness, particularly in underserved regions where access to ophthalmic care is limited. This study presents a proof of concept for utilizing a portable handheld retinal camera with an embedded artificial intelligence (AI) platform, complemented by a synchronous remote confirmation by retina specialists, for DR screening in an underserved rural area. DesignRetrospective cohort study Subjects1,115 individuals with diabetes. MethodsA retrospective analysis of a screening initiative conducted in four municipalities in Northeastern Brazil, targeting the diabetic population. A portable handheld retinal camera captured macula-centered and disc-centered images, which were analyzed by the AI system. Immediate push notifications were sent out to retina specialists upon the detection of significant abnormalities, enabling synchronous verification and confirmation, with on-site patient feedback within minutes. Referral criteria were established, and all referred patients underwent a complete ophthalmic work-up and subsequent treatment. Main outcome measuresProof-of-concept implementation success. ResultsOut of 2,052 invited individuals, 1,115 participated, with a mean age of 60.93 years and diabetes duration of 7.52 years, of whom 66.03% were women. The screening covered 2,222 eyes, revealing various retinal conditions. Referable eyes for DR were 11.84%, with an additional 13% for other conditions (diagnoses included various stages of DR, media opacity, nevus, drusen, enlarged cup-to-disc ratio, pigmentary changes, and other). AI performance for overall detection of referable cases (both DR and other conditions) was: sensitivity- 84.23% (95% CI- 82.63-85.84), specificity- 80.79% (95% CI- 79.05- 82.53). When we assessed whether AI matched any clinical diagnosis, be it referable or not, sensitivity was 85.67% (95% CI- 84.12-87.22), specificity was 98.86 (95% CI- 98.39-99.33), and AUC was 0.92 (95% CI- 0.91-0.94). ConclusionsThe integration of a portable device, AI analysis, and synchronous medical validation has the potential to play a crucial role in preventing blindness from DR, especially in socially unequal scenarios.

Looking for resource density in the platform supply network

In a platform supply network, a platform owner uses a digital platform to link other firms such as suppliers, customers, logistics firms and other service firms for resource combination to manage supply chains in a comprehensive manner. Resources from different providers are combined on the platform to create value for suppliers. The literature on the Service-Dominant Logic (SDL) has implied that resource density (defined as the optimal combination of resources) is key to value co-creation. Resource density is enhanced when the number and variety of resources and/or their combinability at the opportune time and place are boosted. Yet the process by which the platform owner, complementors (independent companies providing complementary services) and suppliers interact to make this happen is still unclear. This research aims to explore how these three kinds of actors interact to build a resource density situation that guarantees satisfactory value creation in a platform supply network. Based on a qualitative study of a Business-to-Business (B2B) e-commerce digital platform, we identify three mechanisms through which these companies achieve resource density. The findings demonstrate how the mechanisms for achieving resource density manifest in the platform supply network context. We reveal that resource density can be achieved at three different levels, and that artificial intelligence and digital platforms play key roles in optimizing resource density.

Evaluating Chat Generative Pre-trained Transformer Responses to Common Pediatric In-toeing Questions.

Chat generative pre-trained transformer (ChatGPT) has garnered attention in health care for its potential to reshape patient interactions. As patients increasingly rely on artificial intelligence platforms, concerns about information accuracy arise. In-toeing, a common lower extremity variation, often leads to pediatric orthopaedic referrals despite observation being the primary treatment. Our study aims to assess ChatGPT's responses to pediatric in-toeing questions, contributing to discussions on health care innovation and technology in patient education. We compiled a list of 34 common in-toeing questions from the "Frequently Asked Questions" sections of 9 health care-affiliated websites, identifying 25 as the most encountered. On January 17, 2024, we queried ChatGPT 3.5 in separate sessions and recorded the responses. These 25 questions were posed again on January 21, 2024, to assess its reproducibility. Two pediatric orthopaedic surgeons evaluated responses using a scale of "excellent (no clarification)" to "unsatisfactory (substantial clarification)." Average ratings were used when evaluators' grades were within one level of each other. In discordant cases, the senior author provided a decisive rating. We found 46% of ChatGPT responses were "excellent" and 44% "satisfactory (minimal clarification)." In addition, 8% of cases were "satisfactory (moderate clarification)" and 2% were "unsatisfactory." Questions had appropriate readability, with an average Flesch-Kincaid Grade Level of 4.9 (±2.1). However, ChatGPT's responses were at a collegiate level, averaging 12.7 (±1.4). No significant differences in ratings were observed between question topics. Furthermore, ChatGPT exhibited moderate consistency after repeated queries, evidenced by a Spearman rho coefficient of 0.55 (P = 0.005). The chatbot appropriately described in-toeing as normal or spontaneously resolving in 62% of responses and consistently recommended evaluation by a health care provider in 100%. The chatbot presented a serviceable, though not perfect, representation of the diagnosis and management of pediatric in-toeing while demonstrating a moderate level of reproducibility in its responses. ChatGPT's utility could be enhanced by improving readability and consistency and incorporating evidence-based guidelines. Level IV-diagnostic.

Emerging Challenges in Privacy Protection with Advancements in Artificial Intelligence

The proliferation of Artificial Intelligence (AI) in various sectors raises significant privacy concerns, demanding a nuanced understanding and strategic approach to privacy protection. This article delves into the intricate challenges of safeguarding privacy in the age of AI, exploring the dynamic interplay between technological advancements and privacy norms. With AI's capacity for extensive data collection and analysis, privacy risks escalate, highlighting the necessity for transparent and ethical data practices. Through examining case studies and regulatory responses, the article underscores the critical role of decentralized AI platforms and robust legal frameworks in ensuring privacy. It advocates for a collaborative effort among stakeholders to balance AI's benefits against privacy rights, aiming for a future where AI technologies are developed and deployed responsibly, with a steadfast commitment to upholding individual privacy and dignity.

A Social Self-Awareness Agent with Embodied Reasoning

In the realm of multi-agent systems, establishing social self-awareness in agents stands as a critical challenge within the broader scope of artificial general intelligence research. Addressing this challenge, in this paper, we draw upon Tomasello’s social self-awareness theory as a foundational framework, and employ Non-Axiomatic Reasoning System (NARS), an artificial general intelligence model, to simulate analogical and recursive reasoning methods used by socially embodied agents. In this paper, we assume that an agent can possess sensorimotor capabilities and physical common sense, it explores the process of constructing a “core self” through embodied reasoning. On this basis, we explore deeper into the construction process of social self-awareness. As a result, we clarify how agents generate common knowledge about themselves through recursive reasoning regarding the mental states of others and communication dynamics. Following this initial step, metaphors are generated utilizing analogical reasoning, intertwining public knowledge with these metaphors to facilitate mutual comprehension between humans and machines. This paper delves into the simulation of perspective representation and recursive reasoning within social embodied agents, employing NARS– an overarching artificial intelligence platform. The experimental results substantiate the viability of constructing a social self-agent within a machine framework grounded in embodied reasoning.

Foreword

As technology advances, there are growing arguments about artificial intelligence, which is still controversial, with both welcome and pessimistic views regarding its role in the future of human life. What was revealed about the development of artificial intelligence is considered very simple in the eyes of specialists and observers, especially in areas that have not been explored, and this constitutes a source of fear for many. The vast leaps witnessed in recent years in the field of artificial intelligence have become a source of anxiety for most people, especially those working in institutions and industrial facilities, even though this anxiety stems from losing their jobs and replacing them with robots. This concern came from statements that the next two decades will witness the introduction of vast quantities of robots to the market. OpenAI was initially established in 2015 by Sam Altman, Elon Musk, and others to advance digital intelligence. On November 30, 2022, OpenAI released ChatGPT (Generative Pre-trained Transformer. ChatGPT uses a large language model to provide users with quick and reliable answers and give enough information about most of the topics. Furthermore, ChatGPT allows users to have more in-depth conversations. The concern about the impact role of ChatGPT on educational and social institutions has led to some reactions from academic institutions, and school districts were hasty and took action to block artificial intelligence platforms from schools, which they quickly retracted in the aftermath. On the other hand, some scholarly academic journals, including our Arab World English Journal, have announced guidelines for article authors. Authors must disclose the use of generative AI and AI-assisted technologies in the writing process. While we are editing the final draft of this volume (April 2024), OpenAI CEO Sam Altman announced in a statement that OpenAI has opened its first office in Asia, Japan, and is providing a GPT-4 custom for the Japanese language; it is three times faster its predecessor. OpenAI aims to expand ChatGPT’s global presence. After releasing ChatGPT, which is backed by Microsoft, OpenAI has emerged as a leading company in the history of computing. In this special issue, we addressed the impact of ChatGPT and AI-assisted technologies, an essential topic that will constitute the main features of education and publications in the twenty-first century. The issue contains 21 articles from eight countries (China, Kuwait, Iraq, Oman, Malaysia, Morocco, Saudi Arabia, and Ukraine). It covers a broad spectrum of related topics: Academic writing, the pedagogical potential of infinigoChatIC, Grammar Teaching and Learning in EFL Classes, English Language Proficiency in EFL Classrooms, Grammar Teaching and Learning in EFL Classes, EFL Essay Writing, Distance Learning Environment, Evaluation of the Translation, Improve the Research Writing Process, Role of Artificial Intelligence in Education, Language Learners’ Critical Thinking, Naruto Fan Fiction Writing, and the Impact of ChatGPT on Future Academics. We seize this opportunity to extend our heartfelt thanks to the authors who have shared their research with our Arab World English journal. We anticipate that the articles in this volume will attract a diverse readership and deepen our understanding of ChatGPT’s role in learning and teaching and its associated challenges. We also hope these papers will provide a thought-provoking perspective for faculty members and students to consider ChatGPT’s impact on writing and related assignments.

Mengubah Gambar Sketsa Menjadi Visual yang Menakjubkan : Membangun Dunia Animasi dengan Kecerdasan Buatan

The rapid development of technology in the current era has brought changes to the industry of animation. The utilization of artificial intelligence technology in the field of animation helps accelerate the creative process of creating animated films, one of which is the search for visual concepts to be used. The search for visual concepts can now be made using only sketch images through the assistance of artificial intelligence. The research method employed is exploration, and this study will explore the utilization of Control Trait on the artificial intelligence platform used, namely Playground AI. The sketch images used consist of 10 images, comprising 5 character sketch images and 5 environmental sketch images. The results of the research reveal that the recommended Control Trait values for sketch images fall within the range of 0.4 – 0.6. The conclusion of this study is that black and white sketch images can be quickly transformed into stunning visuals with the assistance of artificial intelligence. The generated images are sufficiently good to be used as visual concepts for an animated film.

Identification of the Constitutive and Friction Models Parameters via a Multi-Objective Surrogate-Assisted Algorithm for the Modeling of Machining—Application to Arbitrary Lagrangian Eulerian Orthogonal Cutting of Ti6Al4V

Abstract The evolution of high-performance computing facilitates the simulation of manufacturing processes. The prediction accuracy of a numerical model of the cutting process is closely associated with the selection of constitutive and friction models. The reliability and the accuracy of these models highly depend on the value of the parameters involved in the definition of the cutting process. Direct of inverse methods are used to determine these model parameters. However, these identification procedures often neglect the link between the parameters of the material and the friction models. This article introduces a novel approach to inversely identify the best parameters value for both models at the same time and by taking into account multiple cutting conditions in the optimization routine. An artificial intelligence (AI) framework that combines the finite element modeling with an adaptive Bayesian multi-objective evolutionary algorithm (AB-MOEA) is developed, where the objective is to minimize the deviation between the experimental and the numerical results. The arbitrary Lagrangian–Eulerian (ALE) formulation and the Ti6Al4V alloy are selected to demonstrate its applicability. The investigation shows that the developed AI platform can identify the best parameters values with low computational time and resources. The identified parameters values predicted the cutting and feed forces within a deviation of less than 4% from the experiments for all the cutting conditions considered in this work.

Periorbital Rejuvenation: Artificial Intelligence Highlights the Importance of Brow Lift

Abstract Goals/Purpose Well described facial changes occur over time, which collectively result in an increased perceived age of an individual. These changes affect everything including the bony framework, facial fat pads, soft tissue, skin quality, and facial muscles. Aesthetic surgery provides an opportunity to reverse the appearance of facial aging. With the recent increase in mask utilization following the COVID-19 pandemic, an increased focus on the upper face and the impact of age-related changes in the periorbital region has been observed. Blepharoplasty and brow lifts aim to restore a youthful, rested, and attractive appearance by addressing the periorbital region. Blepharoplasty is a commonly performed procedure and has consistently ranked the third most common aesthetic procedure performed by plastic surgeons since 2018. However, following the pandemic we saw a 28% increase in blepharoplasties performed, now totaling 1.4 million in 2022. Over the same time period, the number of brow lifts performed has increased by 60%, now accounting for 352,324 surgeries performed in 2022. Despite this increase, recent surveys demonstrate that nearly 30% of plastic surgeons feel that neuromodulators have completely replaced operative brow lifting procedures. Additionally, a recently published study demonstrated that 62% of patients presenting for blepharoplasty have unfavorable preoperative brow aesthetics. However, only 36% of patients underwent brow lifts.Those with preoperative unfavorable brows who underwent a brow lift procedure were found to have significantly higher postoperative aesthetic scores than those who did not. Despite the increase in popularity of periorbital procedures, the literature continues to lack objective evaluation and outcome measurements related to perceived facial aging and the impact of individual procedures. The goal of this study is to quantify the impact of different periorbital rejuvenation surgeries on perceived age through the utilization of artificial intelligence (AI) in the form of convolutional neural network algorithms. Methods/Technique A retrospective review of patients who underwent periorbital rejuvenation surgery (upper blepharoplasty, lower blepharoplasty, and brow lift) at a single cosmetic practice between 2018-2023 was performed. Charts were reviewed for demographic information, periocular history, prior surgeries, surgical technique utilized, and complications/revisions. Exclusion criteria included: simultaneous procedures, incomplete patient records, poor quality or missing photographs, periocular medical conditions with cosmetic impact, and facial aesthetic procedures performed in the postoperative period. Pre and post operative frontal photographs of each patient were analyzed using four facial analysis AI platforms (Face++, Betaface, Facelytics, and Everypixel). Each platform generated an age estimate. A collective pre-op and post-op age estimation was then calculated for each patient by averaging the 4 platforms responses. To evaluate accuracy of the AI age estimation, a percent error was calculated by dividing the difference between the true age and estimated preoperative age by the true preoperative age. To account for natural aging between surgery and post-op photographs due to follow-up time, a corrective difference was calculated and subtracted from the mean postoperative estimated age. Statistical analysis included student’s t-test for univariate analysis and linear regression for multivariate analysis. All statistical analyses were performed on Stata version 14.0 (Stata Corporation, College Station, Texas, USA). Results/Complications A total of 153 patients were included for analysis, of which 120 underwent upper blepharoplasty, 66 underwent lower blepharoplasty, and 35 underwent a brow lift procedure. The study population consisted of 130 females and 23 males, 95% were Caucasian, and the average preoperative age was 58.1 (± 9.2 years). Post-operative photographs were taken at 3.8 months (± 5.4 months). Across all AI platforms, the mean age estimation percent error was 10%, with the tendency for AI to underestimate compared to true age. Univariate analysis revealed an overall age reduction following periorbital rejuvenation surgery of 1.03 years (p&amp;lt;0.001), with a standard error of 0.257. When controlling for all other procedures via linear regression, patients who underwent brow lifts demonstrated a statistically significant age reduction, with the mean AI perceived age reduction of 1.72 years (p=0.007). No significant age reduction was identified for patients undergoing lower blepharoplasty (regardless of technique), ptosis repair, or fat pad resection. Conclusion The results of this study highlight the rejuvenation power of a brow lift, with an average age reduction of nearly 2 years. In our patient population, brow lifting procedures conveyed the most age reduction of periorbital surgery. Recently published literature indicates that brow lifts are under performed and can serve as a potential avenue for further facial rejuvenation. Therefore, when planning periorbital rejuvenation, a thorough preoperative evaluation of brow position and shape should be performed and additional consideration should be given to operative brow lifting procedures.

Evaluating the Quality of Postpartum Hemorrhage Nursing Care Plans Generated by Artificial Intelligence Models.

With the rapidly advancing technological landscape of health care, evaluating the potential use of artificial intelligence (AI) models to prepare nursing care plans is of great importance. The purpose of this study was to evaluate the quality of nursing care plans created by AI for the management of postpartum hemorrhage (PPH). This cross-sectional exploratory study involved creating a scenario for an imaginary patient with PPH. Information was put into 3 AI platforms (GPT-4, LaMDA, Med-PaLM) on consecutive days without prior conversation. Care plans were evaluated using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) scale. Med-PaLM exhibited superior quality in developing the care plan compared with LaMDA ( Z = 4.354; P = .000) and GPT-4 ( Z = 3.126; P = .029). Our findings suggest that despite the strong performance of Med-PaLM, AI, in its current state, is unsuitable for use with real patients.

339 Gender Concordance and Publication Productivity Within Neurosurgical Focus: A Ten-Year Review

INTRODUCTION: The “glass ceiling” phenomenon describes how female representation in leadership positions has remained stagnant despite an increase in the number of trainees. Mentorship from female role models has been hypothesized to influence academic productivity. To our knowledge, trends in gender representation among editorial boards of neurosurgical journals remain undescribed. Neurosurgical Focus publishes monthly issues which are peer-reviewed by an invited group of topic editors based on their expertise in the field. METHODS: Publicly available data were collected from the Journal of Neurosurgery: Focus website from January 2013 to December 2022. Themes were dichotomized into technical or non-technical themes. Female gender concordant publications were defined as publications having females in both first and senior authorship. Gender was assessed using pronouns based on authors’ university profile and an artificial intelligence platform in case of ambiguity. Odds ratios (ORs) were calculated using Chi-squared and Fisher’s exact test. RESULTS: The number of female editors and gender concordant authors increased over time (P &lt; 0.05). Women accounted for =50% of the editorial board on non-technical themes relevant to education and gender diversity. Having a female senior author was associated with higher publication productivity for original research and review articles among female authors (OR = 13.73, 95% CI = 1.74-394.31, P &lt; 0.05). The odds of having female gender concordant publications were higher with a female editor (OR = 4.05, 95% CI= 1.38-12.92, P &lt; 0.01). CONCLUSIONS: Mentorship and representation among editorial boards play a crucial role in enhancing academic productivity among women. This paper provides evidence that Neurosurgical Focus increased female gender concordant publications by increasing female editors over time because of their efforts to increase female representation. Initiatives to increase female representation and mentorship will help overcome the glass ceiling phenomenon.

ChatGPT in Urogynecology Research: Novel or Not?

ChatGPT (Chat Generative Pre-trained Transformer) is an artificial intelligence (AI) chatbot that provides human-like responses to text prompts. Little is known regarding ChatGPT's ability to identify original research ideas in urogynecology. Our objective was to evaluate the accuracy of ChatGPT in generating novel systematic review (SR) and research ideas in urogynecology. In this cross-sectional study, we asked ChatGPT to generate 10 novel SR ideas that have not yet been published for each of the following 5 topics: (1) urogynecology, (2) tension-free vaginal tape slings, (3) pessaries for pelvic organ prolapse, (4) beta-3 agonist use for overactive bladder, and (5) sexual function with genitourinary syndrome of menopause. Research idea novelty was assessed by cross-referencing PubMed and Scopus to determine if any prior publications existed. ChatGPT proposed 50 total SR ideas, including 10 for each prompt. Overall, ChatGPT showed 54% overall accuracy in developing novel SR ideas. Nonnovel SR ideas had a median of 19 (interquartile range, 8-35) published SRs on the suggested topic. When stratified by prompt type, 50% of general and 40-70% of specific urogynecology proposed SR ideas were found to be novel. There were no publications of any type identified for 7 of the 50 suggested ideas. ChatGPT may be helpful for identifying novel research ideas in urogynecology, but its accuracy is limited. It is essential for those using ChatGPT to review existing literature to ensure originality and credibility. As AI transforms health care, we encourage all urogynecologists to familiarize themselves with popular AI platforms.