Artificial Intelligence Assistance Research Articles

Addressing pollution challenges for enterprises under diverse extreme climate conditions: artificial intelligence-driven experience and policy support of top Chinese enterprises.

This study investigates the experiences of leading Chinese companies in environmental conservation under varying extreme climate conditions, focusing on the role of artificial intelligence (AI) and governmental assistance. A survey was conducted involving 200 participants to assess recognition and endorsement of AI's role in environmental protection and to explore the adoption of AI technologies by firms for enhancing environmental management practices. The survey revealed widespread recognition of Tencent's green initiatives and strong support for AI's role in environmental protection. Many firms are considering adopting AI technologies to optimize energy management, deploy intelligent HVAC systems, and improve the operations of data centers and smart lighting systems. The findings highlight a strong belief in AI's potential to advance environmental protection efforts, with a call for increased governmental support to foster this development. The study underscores the importance of a partnership between businesses and governments to leverage AI for environmental sustainability, contributing significantly to conservation efforts.

Developing interpretable models and techniques for explainable AI in decision-making

The rapid proliferation of artificial intelligence (AI) technologies across various industries and decision-making processes has undeniably transformed the way of approaching complex problems and tasks. AI systems have proven their prowess in areas such as healthcare, finance, and autonomous systems, revolutionizing how decisions are made. Nevertheless, this proliferation of AI has raised critical concerns regarding the transparency, accountability, and fairness of these systems, as many of the state-of-the-art AI models often resemble complex black boxes. These intricate models, particularly deep learning neural networks, harbor non-linear relationships that are difficult for human users to decipher, thereby raising concerns about bias, fairness, and overall trustworthiness in AI-driven decisions. The urgency of this issue is underscored by the realization that AI should not merely be accurate; it should also be interpretable. Explainable AI (XAI) has emerged as a vital field of research, emphasizing the development of models and techniques that render AI systems comprehensible and transparent in their decision-making processes. This paper investigates into the relevance and significance of XAI across various domains, including healthcare, finance, and autonomous systems, where the ability to understand the rationale behind AI decisions is paramount. In healthcare, where AI assists in diagnosis and treatment, the interpretability of AI models is crucial for clinicians to make informed decisions. In finance, applications like credit scoring and investment analysis demand transparent AI to ensure fairness and accountability. In the realm of autonomous systems, transparency is indispensable to guarantee safety and compliance with regulations. Moreover, government agencies in areas such as law enforcement and social services require interpretable AI to maintain ethical standards and accountability. This paper also highlights the diverse array of research efforts in the XAI domain, spanning from model-specific interpretability methods to more general approaches aimed at unveiling complex AI models. Interpretable models like decision trees and rule-based systems have gained attention for their inherent transparency, while integrating explanation layers into deep neural networks strives to balance accuracy with interpretability. The study emphasizes the significance of this burgeoning field in bridging the gap between AI's advanced capabilities and human users' need for comprehensible AI systems. It seeks to contribute to this field by exploring the design, development, and practical applications of interpretable AI models and techniques, with the ultimate goal of enhancing the trust and understanding of AI-driven decisions.

The impact of artificial intelligence on online assessment: A preliminary review

The purpose of this study is to examine the impact of artificial intelligence (AI) on online assessment in the context of opportunities and threats based on the literature. To this end, 19 articles related to the AI tool ChatGPT and online assessment were analysed through rapid literature review. In the content analysis, the themes of “AI's assistance role”, “automatic grading and feedback”, “improving assessment” and “time benefit” were obtained in the opportunities category, while the themes of “academic integrity concern”, “reliability issues” and “adaptability issues” were obtained in the threats category. The impact of AI on online assessment was explained within the scope of these themes. The results revealed that the most emphasis was placed on "improving assessment" themes in the opportunities category, and "academic integrity concern" themes in the threats category. At the end of this preliminary review, it was revealed that more studies investigating the integration of AI to online assessment are needed and all educational institutions, especially distance education institutions, should take measures to ensure the ethical use of AI.

Artificial intelligence-assisted diagnosis of congenital heart disease and associated pulmonary arterial hypertension from chest radiographs: A multi-reader multi-case study

ObjectivesTo explore the possibility of automatic diagnosis of congenital heart disease (CHD) and pulmonary arterial hypertension associated with CHD (PAH-CHD) from chest radiographs using artificial intelligence (AI) technology and to evaluate whether AI assistance could improve clinical diagnostic accuracy. Materials and MethodsA total of 3255 frontal preoperative chest radiographs (1174 CHD of any type and 2081 non-CHD) were retrospectively obtained. In this study, we adopted ResNet18 pretrained with the ImageNet database to establish diagnostic models. Radiologists diagnosed CHD/PAH-CHD from 330/165 chest radiographs twice: the first time, 50% of the images were accompanied by AI-based classification; after a month, the remaining 50% were accompanied by AI-based classification. Diagnostic results were compared between the radiologists and AI models, and between radiologists with and without AI assistance. ResultsThe AI model achieved an average area under the receiver operating characteristic curve (AUC) of 0.948 (sensitivity: 0.970, specificity: 0.982) for CHD diagnoses and an AUC of 0.778 (sensitivity: 0.632, specificity: 0.925) for identifying PAH-CHD. In the 330 balanced (165 CHD and 165 non-CHD) testing set, AI achieved higher AUCs than all 5 radiologists in the identification of CHD (0.670–0.858) and PAH-CHD (0.610–0.688). With AI assistance, the mean ± standard error AUC of radiologists was significantly improved for CHD (ΔAUC + 0.096, 95 % CI: 0.001–0.190; P = 0.048) and PAH-CHD (ΔAUC + 0.066, 95 % CI: 0.010–0.122; P = 0.031) diagnosis. ConclusionChest radiograph-based AI models can detect CHD and PAH-CHD automatically. AI assistance improved radiologists' diagnostic accuracy, which may facilitate a timely initial diagnosis of CHD and PAH-CHD.

The precursors of AI adoption in business: Towards an efficient decision-making and functional performance

Artificial intelligence (AI) is a highly effective solution for enhancing decision-making efficiency and optimising the functional performance of organisations. However, there have been limited attempts to assess the consequences of implementing AI systems on the quality and efficiency of decision-making. This study proposes and empirically examines an extended model covering all aspects that would shape the successful adoption of AI by decision-makers while investigating how the successful adoption of AI enhances the efficiency of the decision-making process. This study also intends to test the validity of the integrated AI acceptance-avoidance model (IAAAM) proposed by Cao et al. (2021) using the Middle East context (i.e. Saudi Arabia). The extended model of the current study was based on the IAAAM and IS professional distinctiveness (ISPD). Two quantitative studies were conducted to achieve the research objectives. The first study was conducted to validate the IAAAM using a purposive sample of employees (non-adopters of AI applications). The second study tested the proposed model using a purposive sample of employees (actual adopters). The structural equation modelling (SEM) results of the first study (non-adopters) supported the validity of the IAAAM in Saudi Arabia. Factors (performance expectancy (PE), facilitating conditions (FC), personal well-being concern (PWC), perceived threat (PT), and attitudes (ATT)) had a significant impact on either ATT or the intention to use AI. The SEM results of actual adopters supported the impact of PE, EE, FC, PWC, and ATT on either ATT or the adoption of AI (AoAI). As an external factor, the ISPD was the most significant predictor of AoAI. The AoAI was confirmed to strongly predict decision-making efficiency, which, in turn, contributes to functional performance. This study enriches the current understanding of the main factors that contribute to the successful implementation of AI systems, offering an in-depth understanding of both AI adopters and non-adopters. It identifies factors important to non-users to enhance future adoption, whereas current AI users focus on improving decision-making quality with the AI assistance.

Designing Human-Centered AI to Prevent Medication Dispensing Errors: Focus Group Study With Pharmacists.

Medication errors, including dispensing errors, represent a substantial worldwide health risk with significant implications in terms of morbidity, mortality, and financial costs. Although pharmacists use methods like barcode scanning and double-checking for dispensing verification, these measures exhibit limitations. The application of artificial intelligence (AI) in pharmacy verification emerges as a potential solution, offering precision, rapid data analysis, and the ability to recognize medications through computer vision. For AI to be embraced, it must be designed with the end user in mind, fostering trust, clear communication, and seamless collaboration between AI and pharmacists. This study aimed to gather pharmacists' feedback in a focus group setting to help inform the initial design of the user interface and iterative designs of the AI prototype. A multidisciplinary research team engaged pharmacists in a 3-stage process to develop a human-centered AI system for medication dispensing verification. To design the AI model, we used a Bayesian neural network that predicts the dispensed pills' National Drug Code (NDC). Discussion scripts regarding how to design the system and feedback in focus groups were collected through audio recordings and professionally transcribed, followed by a content analysis guided by the Systems Engineering Initiative for Patient Safety and Human-Machine Teaming theoretical frameworks. A total of 8 pharmacists participated in 3 rounds of focus groups to identify current challenges in medication dispensing verification, brainstorm solutions, and provide feedback on our AI prototype. Participants considered several teaming scenarios, generally favoring a hybrid teaming model where the AI assists in the verification process and a pharmacist intervenes based on medication risk level and the AI's confidence level. Pharmacists highlighted the need for improving the interpretability of AI systems, such as adding stepwise checkmarks, probability scores, and details about drugs the AI model frequently confuses with the target drug. Pharmacists emphasized the need for simplicity and accessibility. They favored displaying only essential information to prevent overwhelming users with excessive data. Specific design features, such as juxtaposing pill images with their packaging for quick comparisons, were requested. Pharmacists preferred accept, reject, or unsure options. The final prototype interface included (1) checkmarks to compare pill characteristics between the AI-predicted NDC and the prescription's expected NDC, (2) a histogram showing predicted probabilities for the AI-identified NDC, (3) an image of an AI-provided "confused" pill, and (4) an NDC match status (ie, match, unmatched, or unsure). In partnership with pharmacists, we developed a human-centered AI prototype designed to enhance AI interpretability and foster trust. This initiative emphasized human-machine collaboration and positioned AI as an augmentative tool rather than a replacement. This study highlights the process of designing a human-centered AI for dispensing verification, emphasizing its interpretability, confidence visualization, and collaborative human-machine teaming styles.

Impact of Artificial Intelligence on Mammography Interpretation by Breast Radiologists, Non-Breast Radiologists, and Senior Residents

Background: Artificial intelligence (AI) is recognized to have tremendous potential to revolutionize breast cancer management through mammography. However, the extent of its impact on radiologists with different levels of experience remains largely unexplored. Therefore, this study aimed to comprehensively show how AI could assist radiologists of varying expertise including breast and non-breast radiologists, as well as senior residents, in performing mammogram interpretation.Methods: This retrospective study analyzed eligible mammograms from Cipto Mangunkusumo Hospital between January 2017 and March 2021. Mammographic readings were conducted independently by two breast radiologists, two from other subspecialties, and three senior residents, all blinded to clinical information. AI standalone performance, as well as radiologists with and without AI assistance, was measured. Results: The results showed that a total of 886 eligible mammograms were analyzed. AI standalone performance, assessed using ROC curve analysis, yielded an AUC of 0.946 (95% CI, 0.925–0.967) with sensitivity and specificity of 90.1% and 93.6%, respectively. AI assistance significantly improved the sensitivity and specificity of all radiologists, regardless of experience level, with a median increase of 19.4% (IQR, 10.4–33.5%) and 12.1% (IQR, 5.2–16.2%), respectively. Moreover, there was a trend toward a higher increase with AI assistance in dense compared to fatty breasts.Conclusions: AI proved to be a highly effective diagnostic supplement for radiologists across varying experience levels, specifically in non-breast radiologists, offering the potential to add even greater value in cases of dense breast tissue. The results were derived from a national referral tertiary hospital that generally received many breast cancer cases referred from other hospitals for further treatment. Therefore, further studies incorporating different levels of hospitals were needed.

High-Performance Hydrogel Sensors Enabled Multimodal and Accurate Human-Machine Interaction System for Active Rehabilitation.

Human-machine interaction (HMI) technology shows an important application prospect in rehabilitation medicine, but it is greatly limited by the unsatisfactory recognition accuracy and wearing comfort. Here, this work develops a fully flexible, conformable, and functionalized multimodal HMI interface consisting of hydrogel-based sensors and a self-designed flexible printed circuit board. Thanks to the component regulation and structural design of the hydrogel, both electromyogram (EMG) and forcemyography (FMG) signals can be collected accurately and stably, so that they are later decoded with the assistance of artificial intelligence (AI). Compared with traditional multichannel EMG signals, the multimodal human-machine interaction method based on the combination of EMG and FMG signals significantly improves the efficiency of human-machine interaction by increasing the information entropy of the interaction signals. The decoding accuracy of the interaction signals from only two channels for different gestures reaches 91.28%. The resulting AI-powered active rehabilitation system can control a pneumatic robotic glove to assist stroke patients in completing movements according to the recognized human motion intention. Moreover, this HMI interface is further generalized and applied to other remote sensing platforms, such as manipulators, intelligent cars, and drones, paving the way for the design of future intelligent robot systems.

Unveiling the factors shaping consumer acceptance of AI assistant services in the hotel industry: a behavioral reasoning perspective

Technological advancements in automation, artificial intelligence (AI), and robotics have completely changed the service industries, including the hospitality sector. This study aims to investigate the fundamental elements that impact the acceptance (or rejection) of artificial intelligence assistants' services. This research employed semi-structured interviews to gather insights from both individuals who use AI services in hotels and those who do not. Based on Behavioral Reasoning Theory, this study is empirically tested by interviews with intended respondents. This study examines the "reasons for" and "reasons against" using AI assistant services in the hotel. Technology anxiety, privacy concern, prior experience, and task complexity are "reasons against," while performance expectancy, effort expectancy, social influence, facilitating conditions, humanness, social presence, perceived interaction enjoyment, cuteness, and trust are "reasons for." This study found that "reasons against" negatively affect attitude and adoption intention, while "reasons for" positively affect them. Behavioral Reasoning Theory is used for the first time to evaluate Bangladeshi hotel consumers' AI service assistant adoption intentions. As a new technology, AI assistants provide a better understanding of user usage. Furthermore, this study offers essential facts regarding the utilization of technology in the hospitality sector.

How Can the Clinical Aptitude of AI Assistants Be Assayed?

Large language models (LLMs) are exhibiting remarkable performance in clinical contexts, with exemplar results ranging from expert-level attainment in medical examination questions to superior accuracy and relevance when responding to patient queries compared to real doctors replying to queries on social media. The deployment of LLMs in conventional health care settings is yet to be reported, and there remains an open question as to what evidence should be required before such deployment is warranted. Early validation studies use unvalidated surrogate variables to represent clinical aptitude, and it may be necessary to conduct prospective randomized controlled trials to justify the use of an LLM for clinical advice or assistance, as potential pitfalls and pain points cannot be exhaustively predicted. This viewpoint states that as LLMs continue to revolutionize the field, there is an opportunity to improve the rigor of artificial intelligence (AI) research to reward innovation, conferring real benefits to real patients.

Using AI to Improve Radiologist Performance in Detection of Abnormalities on Chest Radiographs.

Background Chest radiography remains the most common radiologic examination, and interpretation of its results can be difficult. Purpose To explore the potential benefit of artificial intelligence (AI) assistance in the detection of thoracic abnormalities on chest radiographs by evaluating the performance of radiologists with different levels of expertise, with and without AI assistance. Materials and Methods Patients who underwent both chest radiography and thoracic CT within 72 hours between January 2010 and December 2020 in a French public hospital were screened retrospectively. Radiographs were randomly included until reaching 500 radiographs, with about 50% of radiographs having abnormal findings. A senior thoracic radiologist annotated the radiographs for five abnormalities (pneumothorax, pleural effusion, consolidation, mediastinal and hilar mass, lung nodule) based on the corresponding CT results (ground truth). A total of 12 readers (four thoracic radiologists, four general radiologists, four radiology residents) read half the radiographs without AI and half the radiographs with AI (ChestView; Gleamer). Changes in sensitivity and specificity were measured using paired t tests. Results The study included 500 patients (mean age, 54 years ± 19 [SD]; 261 female, 239 male), with 522 abnormalities visible on 241 radiographs. On average, for all readers, AI use resulted in an absolute increase in sensitivity of 26% (95% CI: 20, 32), 14% (95% CI: 11, 17), 12% (95% CI: 10, 14), 8.5% (95% CI: 6, 11), and 5.9% (95% CI: 4, 8) for pneumothorax, consolidation, nodule, pleural effusion, and mediastinal and hilar mass, respectively (P < .001). Specificity increased with AI assistance (3.9% [95% CI: 3.2, 4.6], 3.7% [95% CI: 3, 4.4], 2.9% [95% CI: 2.3, 3.5], and 2.1% [95% CI: 1.6, 2.6] for pleural effusion, mediastinal and hilar mass, consolidation, and nodule, respectively), except in the diagnosis of pneumothorax (-0.2%; 95% CI: -0.36, -0.04; P = .01). The mean reading time was 81 seconds without AI versus 56 seconds with AI (31% decrease, P < .001). Conclusion AI-assisted chest radiography interpretation resulted in absolute increases in sensitivity for all radiologists of various levels of expertise and reduced the reading times; specificity increased with AI, except in the diagnosis of pneumothorax. © RSNA, 2023 Supplemental material is available for this article.

Developing and Evaluating an AI-Based Computer-Aided Diagnosis System for Retinal Disease: Diagnostic Study for Central Serous Chorioretinopathy.

Although previous research has made substantial progress in developing high-performance artificial intelligence (AI)-based computer-aided diagnosis (AI-CAD) systems in various medical domains, little attention has been paid to developing and evaluating AI-CAD system in ophthalmology, particularly for diagnosingretinal diseases using optical coherence tomography (OCT) images. This diagnostic study aimed to determine the usefulness of a proposed AI-CAD system in assisting ophthalmologists with the diagnosis of central serous chorioretinopathy (CSC), which is known to be difficult to diagnose, using OCT images. For the training and evaluation of the proposed deep learning model, 1693 OCT images were collected and annotated. The data set included 929 and 764 cases of acute and chronic CSC, respectively. In total, 66 ophthalmologists (2 groups: 36 retina and 30 nonretina specialists) participated in the observer performance test. To evaluate the deep learning algorithm used in the proposed AI-CAD system, the training, validation, and test sets were split in an 8:1:1 ratio. Further, 100 randomly sampled OCT images from the test set were used for the observer performance test, and the participants were instructed to select a CSC subtype for each of these images. Each image was provided under different conditions: (1) without AI assistance, (2) with AI assistance with a probability score, and (3) with AI assistance with a probability score and visual evidence heatmap. The sensitivity, specificity, and area under the receiver operating characteristic curve were used to measure the diagnostic performance of the model and ophthalmologists. The proposed system achieved a high detection performance (99% of the area under the curve) for CSC, outperforming the 66 ophthalmologists who participated in the observer performance test. In both groups, ophthalmologists with the support of AI assistance with a probability score and visual evidence heatmap achieved the highest mean diagnostic performance compared with that of those subjected to other conditions (without AI assistance or with AI assistance with a probability score). Nonretina specialists achieved expert-level diagnostic performance with the support of the proposed AI-CAD system. Our proposed AI-CAD system improved the diagnosis of CSC by ophthalmologists, which may support decision-making regarding retinal disease detection and alleviate the workload of ophthalmologists.

Laparoscopic Colorectal Surgery with Anatomical Recognition with Artificial Intelligence Assistance for Nerves and Dissection Layers

BackgroundIn digestive tract surgery, dissection of an avascular space consisting of loose connective tissue (LCT) appearing by countertraction improves oncological outcomes and reduces complications.1–3 Kumazu et al.4 described a deep learning approach that automatically segments LCT to help surgeons.4 During left colorectal surgery, lumbar splanchnic, hypogastric, and pelvic visceral nerve injuries cause sexual dysfunction and/or urinary issues.5 As nerve preservation is critical for functional preservation, the AI model Kumazu reported is named Eureka (Anaut Inc., Tokyo, Japan) and was developed to separate nerves automatically. The educative efficacy of intraoperative nerve visualization has been described.6 Artificial intelligence (AI) assisted navigation is expected to aid in the anatomical recognition of nerves and the safe dissection layers surrounding nerves in the future.MethodsWe used Eureka as an educational tool for surgeons’ training during laparoscopic colorectal surgery. The laparoscopic system used was Olympus VISERA ELITE3 (Tokyo, Japan).ResultsTotal mesorectal excision (TME) was safely performed with nerve preservation. No postoperative complications occurred. Automatic segmentation and highlighting of LCT in the dissected layers, lumbar splanchnic, hypogastric, and pelvic visceral nerves (S3, S4), were performed in real time.ConclusionsIn colorectal cancer surgery, the nerves are vital anatomical structures serving as landmarks for dissection. Lumbar splanchnic, hypogastric, and pelvic visceral nerve injuries (S3, S4) cause sexual dysfunction or urinary disorders.5 Nerve preservation is important for functional preservation. AI-assisted navigation methods are noninvasive, user-friendly, and expected to improve in accuracy in the future. They have the potential to develop nerve-guided TME.

AI-Powered Mental Health Virtual Assistants' Acceptance: An Empirical Study on Influencing Factors Among Generations X, Y, and Z.

This study aims to analyze various influencing factors among generations X (Gen X), Y (Gen Y), and Z (Gen Z) of artificial intelligence (AI)-powered mental health virtual assistants. A cross-sectional survey design was adopted in this study. The study sample consisted of outpatients diagnosed with various mental health illnesses, such as anxiety, depression, schizophrenia, and behavioral disorders. A survey questionnaire was designed based on the factors (performance expectancy, effort expectancy, social influence, facilitating conditions, and behavioural intention) identified from the unified theory of acceptance and use of the technologymodel. Ethical approval was received from the Ethics Committee at Imam Abdulrahman Bin Faisal University, Saudi Arabia. A total of 506 patients participated in the study, with over 80% having moderate to high experience in using mental health AI assistants. The ANOVA results for performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), and behavioral intentions (BI) indicate that there are statistically significant differences (p < 0.05) between the Gen X, Gen Y, and Gen Z participants. The findings underscore the significance of considering generational differences in attitudes and perceptions, with Gen Y and Gen Z demonstrating more positive attitudes and stronger intentions to use AI mental health virtual assistants, while Gen X appears to be more cautious.

Multilevel-based algorithm for hyperspectral image interpretation

Hyperspectral imagery contains spatial and spectral information, which can reveal the material properties of the target while intuitively displaying its spatial attributes. It has been applied in target recognition, search and rescue, and other fields. However, manual detection inevitably leads to missed detections and false alarms, necessitating the assistance of artificial intelligence for detection. To address this, we propose the multilevel-based algorithm for hyperspectral image interpretation. 1) From the spatial and spectral dimensions, we propose a semantic segmentation algorithm based on multidimensional information fusion to achieve semantic segmentation. 2) From the semantic and textual representation dimensions, we introduce a context interpretation module based on visual attention. We construct both real and simulated databases to validate the effectiveness of the algorithm. Experimental results demonstrate that the average accuracy of semantic segmentation achieved by the proposed algorithm is 74.3%. Additionally, the BLEU1 score reaches 71.2, outperforming mainstream algorithms by 1.4.

A New Era of Dental Care: Harnessing Artificial Intelligence for Better Diagnosis and Treatment.

The integration of artificial intelligence (AI) into dental care holds the promise of revolutionizing the field by enhancing the accuracy of dental diagnosis and treatment. This paper explores the impact of AI in dental care, with a focus on its applications in diagnosis, treatment planning, and patient engagement. AI-driven dental imaging and radiography, computer-aided detection and diagnosis of dental conditions, and early disease detection and prevention are discussed in detail. Moreover, the paper delves into how AI assists in personalized treatment planning and provides predictive analytics for dental care. Ethical and privacy considerations, including data security, fairness, and regulatory aspects, are addressed, highlighting the need for a responsible and transparent approach to AI implementation. Finally, the paper underscores the potential for a collaborative partnership between AI and dental professionals to offer the best possible care to patients, making dental care more efficient, patient-centric, and effective. The advent of AI in dentistry presents a remarkable opportunity to improve oral health outcomes, benefiting both patients and the healthcare community.

Autonomy, Power and the Special Case of Scarcity: Consumer Adoption of Highly Autonomous Artificial Intelligence

AbstractUnlike previous generations of artificial intelligence (AI), AI assistants today can autonomously perform actions without human input or intervention. Several studies have proposed but not tested the notion that increased levels of AI autonomy may ultimately conflict with consumers’ fundamental need for autonomy themselves. Across five experiments (N = 1981), including representative samples and pre‐registered hypotheses, we investigate consumer responses to high (vs. low) AI autonomy in the context of online shopping. The results show a pronounced negative effect of high AI autonomy on consumers’ adoption intentions – an effect mediated by consumers’ relative state of powerlessness in the presence of high AI autonomy. However, when consumers face situations characterized by scarcity, such as when preferred options are being sold out rapidly (e.g. Black Friday), the aversive aspects of high (vs. low) AI autonomy are attenuated. Together, these findings offer novel insights regarding whether, when and why consumers are willing to adopt high (vs. low)‐autonomy AI assistants in online shopping settings.

A global bibliometric and visualized analysis of gait analysis and artificial intelligence research from 1992 to 2022.

Gait is an important basic function of human beings and an integral part of life. Many mental and physical abnormalities can cause noticeable differences in a person's gait. Abnormal gait can lead to serious consequences such as falls, limited mobility and reduced life satisfaction. Gait analysis, which includes joint kinematics, kinetics, and dynamic Electromyography (EMG) data, is now recognized as a clinically useful tool that can provide both quantifiable and qualitative information on performance to aid in treatment planning and evaluate its outcome. With the assistance of new artificial intelligence (AI) technology, the traditional medical environment has undergone great changes. AI has the potential to reshape medicine, making gait analysis more accurate, efficient and accessible. In this study, we analyzed basic information about gait analysis and AI articles that met inclusion criteria in the WoS Core Collection database from 1992-2022, and the VosViewer software was used for web visualization and keyword analysis. Through bibliometric and visual analysis, this article systematically introduces the research status of gait analysis and AI. We introduce the application of artificial intelligence in clinical gait analysis, which affects the identification and management of gait abnormalities found in various diseases. Machine learning (ML) and artificial neural networks (ANNs) are the most often utilized AI methods in gait analysis. By comparing the predictive capability of different AI algorithms in published studies, we evaluate their potential for gait analysis in different situations. Furthermore, the current challenges and future directions of gait analysis and AI research are discussed, which will also provide valuable reference information for investors in this field.

Guiding principles and proposed classification system for the responsible adoption of artificial intelligence in scientific writing in medicine.

The integration of large language models (LLMs) and artificial intelligence (AI) into scientific writing, especially in medical literature, presents both unprecedented opportunities and inherent challenges. This manuscript evaluates the transformative potential of LLMs for the synthesis of information, linguistic enhancements, and global knowledge dissemination. At the same time, it raises concerns about unintentional plagiarism, the risk of misinformation, data biases, and an over-reliance on AI. To address these, we propose governing principles for AI adoption that ensure integrity, transparency, validity, and accountability. Additionally, guidelines for reporting AI involvement in manuscript development are delineated, and a classification system to specify the level of AI assistance is introduced. This approach uniquely addresses the challenges of AI in scientific writing, emphasizing transparency in authorship, qualification of AI involvement, and ethical considerations. Concerns regarding access equity, potential biases in AI-generated content, authorship dynamics, and accountability are also explored, emphasizing the human author's continued responsibility. Recommendations are made for fostering collaboration between AI developers, researchers, and journal editors and for emphasizing the importance of AI's responsible use in academic writing. Regular evaluations of AI's impact on the quality and biases of medical manuscripts are also advocated. As we navigate the expanding realm of AI in scientific discourse, it is crucial to maintain the human element of creativity, ethics, and oversight, ensuring that the integrity of scientific literature remains uncompromised.

Abstract 11601: Does Artificial Intelligence Dream of Elevated Natriuretic Peptide in Chest Radiography?

Introduction: Heart failure pandemic is a medical issue, and its diagnosis can be challenging. Hypothesis: To assess the hypothesis that the artificial intelligence (AI) model for predicting elevated brain natriuretic peptide (BNP) levels from chest X-ray images has sufficient performance and could improve the diagnostic performance of human. Methods: Patients who underwent chest radiography and BNP testing on the same day were enrolled in this study. The chest X-ray images were labeled based on plasma BNP levels &gt;= 200 pg/mL. We trained models that predicted the label, and the final ensemble model was constructed. Humans were trained and tested to predict labels, followed by the same test, referring to the prediction of the AI model. Diagnostic performance was assessed. Results: Among the 1607 patients, 10103 chest X-ray images and BNP values were collected. We assembled 31 models as the final model with an accuracy of 0.855, precision of 0.873, recall of 0.827, receiver-operating-characteristics area-under-curve (AUC) of 0.929, and precision-recall AUC of 0.934. The accuracy of the testing by 35 individuals significantly improved from 0.708±0.049 to 0.829±0.069 (P &amp;lt; 0.001) with AI assistance. The accuracy of the experts was higher than that of non-experts (0.692±0.042 vs. 0.728±0.051, P = 0.030); however, with the AI assistance, the accuracy of the non-experts was rather higher than that of the experts (0.851±0.074 vs. 0.803±0.054, P = 0.033). Conclusions: The AI model can predict elevated BNP levels from chest X-ray images and could improve the diagnostic performance of humans.