Although Artificial Intelligence Research Articles

Confirming the Query Search Method: Balancing AI Advancements in Reference Services

Although artificial intelligence (AI) has quickly become a tool that offers quick, simplistic answers to reference questions (Chen, 2023), in this paper, we interrogate AI’s ability to consider the human-to-human interpersonal nuances that occur and are vital to answering queries. We ask: what is most essential for reference services - human knowledge sharing or the machine’s artificial intelligence? AI, as a mechanistic language model, cannot discern the subtleties of humanity’s questions; AI itself admits that librarians are more efficient at answering reference queries from a humanistic approach and lens (Yang & Mason, 2023). This paper is the second part of our introduction to our methodology for reference services, the Query Search Method (QSM). We present conceptual substantiation that, when enacted, the QSM can incorporate AI as an addition to the librarian’s toolbox for honoring patrons’ ways of knowing that, through the sententiousness of the human experience, invariably seek knowledge beyond “the machine.”

The Manifestation and Implications of Bias in Artificial Intelligence on Global Society

Although artificial intelligence (AI) is becoming more and more prevalent, biases from data, algorithms, and feedback loops might still exist. Marginalized groups may be disproportionately affected by these biases, which may result in biased outcomes in areas such as facial recognition, credit scoring, and hiring. Reduced trust, sustained societal inequality, and stifled innovation are some of the effects. It is critical to support transparent development processes, diverse development teams, and ethical oversight in AI design in order to lessen these effects. If these issues are resolved, AI will become a more equitable instrument for advancing society.

Toward thoughtful integration of AI in education: mitigating uncritical positivity and dependence on ChatGPT via classroom discussions

ABSTRACT Although artificial intelligence (AI) tools (e.g., ChatGPT) have potential to act as a transformative educational tool, the widespread adoption of such technology also brings forth concerns related to academic integrity and learning gaps. This study examines whether class discussions on the negative aspects of AI-driven writing assistance have any impact on mitigating reliance on such technology. To address the inquiry, data were collected from undergraduate students in the U.S. Findings indicate that these discussions lead to a more critical evaluation of ChatGPT’s usefulness and favorability toward it, resulting in weaker intentions to use it. The study underscores the importance of class discussions and offers insights into how instructors and educators can navigate concerns about negative effects of AI-powered technology in education while leveraging the benefits of it.

Concept of Artificial Intelligence-oriented Public Health Model in Cancer Care

Abstract In recent years, the escalating volume of essential information for oncologists has created a challenge, making it arduous to stay abreast of the latest developments in the multifaceted field of cancer care. Although Artificial Intelligence (AI) is increasingly applied in healthcare, particularly for tasks like image recognition and big data analysis, we advocate for an AI-centric public health model tailored to comprehensive cancer care. This model aims to guide patients from their initial doctor’s visit to the conclusion of treatment, thereby minimizing direct doctor involvement. Results. The proposed AI system comprises distinct units: Regional AI (RAI) for patient management and coordination with healthcare specialists and facilities in specific areas, General AI (GAI) to oversee healthcare processes on a broader scale, and Scientific AI (SAI) for data analysis and hypothesis generation, essential for scientific research and clinical trials. To enhance cost efficiency, we suggest introducing an intermediate layer, Teacher AI (TAI), facilitating the development of AI systems like GAI or RAI based on human needs without necessitating extensive specialist intervention. Conclusions. Implementing this model can simplify oncologists’ daily tasks, reduce errors, improve treatment outcomes, and lower the cost of cancer care while maintaining its high quality. The Human–TAI–AI development model can streamline the system’s development and implementation, making it more cost-effective.

Navigating the Metaverse: A New Virtual Tool with Promising Real Benefits for Breast Cancer Patients.

BC, affecting both women and men, is a complex disease where early diagnosis plays a crucial role in successful treatment and enhances patient survival rates. The Metaverse, a virtual world, may offer new, personalized approaches to diagnosing and treating BC. Although Artificial Intelligence (AI) is still in its early stages, its rapid advancement indicates potential applications within the healthcare sector, including consolidating patient information in one accessible location. This could provide physicians with more comprehensive insights into disease details. Leveraging the Metaverse could facilitate clinical data analysis and improve the precision of diagnosis, potentially allowing for more tailored treatments for BC patients. However, while this article highlights the possible transformative impacts of virtual technologies on BC treatment, it is important to approach these developments with cautious optimism, recognizing the need for further research and validation to ensure enhanced patient care with greater accuracy and efficiency.

Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects.

Although artificial intelligence (AI) methods hold promise for medical imaging-based prediction tasks, their integration into medical practice may present a double-edged sword due to bias (i.e., systematic errors). AI algorithms have the potential to mitigate cognitive biases in human interpretation, but extensive research has highlighted the tendency of AI systems to internalize biases within their model. This fact, whether intentional or not, may ultimately lead to unintentional consequences in the clinical setting, potentially compromising patient outcomes. This concern is particularly important in medical imaging, where AI has been more progressively and widely embraced than any other medical field. A comprehensive understanding of bias at each stage of the AI pipeline is therefore essential to contribute to developing AI solutions that are not only less biased but also widely applicable. This international collaborative review effort aims to increase awareness within the medical imaging community about the importance of proactively identifying and addressing AI bias to prevent its negative consequences from being realized later. The authors began with the fundamentals of bias by explaining its different definitions and delineating various potential sources. Strategies for detecting and identifying bias were then outlined, followed by a review of techniques for its avoidance and mitigation. Moreover, ethical dimensions, challenges encountered, and prospects were discussed.

Dietary Assessment with Multimodal ChatGPT: A Systematic Analysis.

Conventional approaches to dietary assessment are primarily grounded in self-reporting methods or structured interviews conducted under the supervision of dietitians. These methods, however, are often subjective, potentially inaccurate, and time-intensive. Although artificial intelligence (AI)-based solutions have been devised to automate the dietary assessment process, prior AI methodologies tackle dietary assessment in a fragmented landscape (e.g., merely recognizing food types or estimating portion size), and encounter challenges in their ability to generalize across a diverse range of food categories, dietary behaviors, and cultural contexts. Recently, the emergence of multimodal foundation models, such as GPT-4V, has exhibited transformative potential across a wide range of tasks (e.g., scene understanding and image captioning) in various research domains. These models have demonstrated remarkable generalist intelligence and accuracy, owing to their large-scale pre-training on broad datasets and substantially scaled model size. In this study, we explore the application of GPT-4V powering multimodal ChatGPT for dietary assessment, along with prompt engineering and passive monitoring techniques. We evaluated the proposed pipeline using a self-collected, semi free-living dietary intake dataset comprising 16 real-life eating episodes, captured through wearable cameras. Our findings reveal that GPT-4V excels in food detection under challenging conditions without any fine-tuning or adaptation using food-specific datasets. By guiding the model with specific language prompts (e.g., African cuisine), it shifts from recognizing common staples like rice and bread to accurately identifying regional dishes like banku and ugali. Another GPT-4V's standout feature is its contextual awareness. GPT-4V can leverage surrounding objects as scale references to deduce the portion sizes of food items, further facilitating the process of dietary assessment.

AI-MARRVEL - A Knowledge-Driven AI System for Diagnosing Mendelian Disorders.

Diagnosing genetic disorders requires extensive manual curation and interpretation of candidate variants, a labor-intensive task even for trained geneticists. Although artificial intelligence (AI) shows promise in aiding these diagnoses, existing AI tools have only achieved moderate success for primary diagnosis. AI-MARRVEL (AIM) uses a random-forest machine-learning classifier trained on over 3.5 million variants from thousands of diagnosed cases. AIM additionally incorporates expert-engineered features into training to recapitulate the intricate decision-making processes in molecular diagnosis. The online version of AIM is available at https://ai.marrvel.org. To evaluate AIM, we benchmarked it with diagnosed patients from three independent cohorts. AIM improved the rate of accurate genetic diagnosis, doubling the number of solved cases as compared with benchmarked methods, across three distinct real-world cohorts. To better identify diagnosable cases from the unsolved pools accumulated over time, we designed a confidence metric on which AIM achieved a precision rate of 98% and identified 57% of diagnosable cases out of a collection of 871 cases. Furthermore, AIM's performance improved after being fine-tuned for targeted settings including recessive disorders and trio analysis. Finally, AIM demonstrated potential for novel disease gene discovery by correctly predicting two newly reported disease genes from the Undiagnosed Diseases Network. AIM achieved superior accuracy compared with existing methods for genetic diagnosis. We anticipate that this tool may aid in primary diagnosis, reanalysis of unsolved cases, and the discovery of novel disease genes. (Funded by the NIH Common Fund and others.).

AI for decision support: What are possible futures, social impacts, regulatory options, ethical conundrums and agency constellations?

Although artificial intelligence (AI) and automated decision-making systems have been around for some time, they have only recently gained in importance as they are now actually being used and are no longer just the subject of research. AI to support decision-making is thus affecting ever larger parts of society, creating technical, but above all ethical, legal, and societal challenges, as decisions can now be made by machines that were previously the responsibility of humans. This introduction provides an overview of attempts to regulate AI and addresses key challenges that arise when integrating AI systems into human decision-making. The Special topic brings together research articles that present societal challenges, ethical issues, stakeholders, and possible futures of AI use for decision support in healthcare, the legal system, and border control.

AI‑based decision support systems and society: An opening statement

Although artificial intelligence (AI) and automated decision-making systems have been around for some time, they have only recently gained in importance as they are now actually being used and are no longer just the subject of research. AI to support decision-making is thus affecting ever larger parts of society, creating technical, but above all ethical, legal, and societal challenges, as decisions can now be made by machines that were previously the responsibility of humans. This introduction provides an overview of attempts to regulate AI and addresses key challenges that arise when integrating AI systems into human decision-making. The Special topic brings together research articles that present societal challenges, ethical issues, stakeholders, and possible futures of AI use for decision support in healthcare, the legal system, and border control.

Implementing Machine Learning approaches for accelerated prediction of bone strain in acetabulum of a hip joint

The Finite Element (FE) methods for biomechanical analysis involving implant design and subject parameters for musculoskeletal applications are extensively reported in literature. Such an approach is manually intensive and computationally expensive with longer simulations times. Although Artificial Intelligence (AI) based approaches are implemented to a limited extent in biomechanics, such approaches to predict bone strain in acetabulum of a hip joint, are hardly explored. In this context, the primary objective of this paper is to evaluate machine learning (ML) models in tandem with high-fidelity FEA data for the accelerated prediction of the biomechanical response in the acetabulum of the human hip joint, during the walking gait. The parameters used in the FEA study included the subject weight, number and distribution of fins on the periphery of the acetabular shell, bone condition and phases of the gait cycle. The biomechanical response has also been evaluated using three different acetabular liners, including pre-clinically validated HDPE-20% HA-20% Al2O3, highly-crosslinked ultrahigh molecular weight polyethylene (HC-UHMWPE) and ZrO2-toughened Al2O3 (ZTA). Such parametric variation in FEA analysis, involving 26 variables and a full factorial design resulted in 10,752 datasets for spatially varying bone strains. The bone condition, as opposed to subject weight, was found to play a statistically significant role in determining the strain response in the periprosthetic bone of the acetabulum. While utilising hyperparameter tuning, K-fold cross validation and statistical learning approaches, a number of ML models were trained on the FEA dataset, and the Random Forest model performed the best with a coefficient of determination (R2) value of 0.99/0.97 and Root Mean Square Error (RMSE) of 0.02/0.01 on the training/test dataset. Taken together, this study establishes the potential of ML approach as a fast surrogate of FEA for implant biomechanics analysis, in less than a minute.

Evaluation of a Deep Learning Model for Metastatic Squamous Cell Carcinoma Prediction From Whole Slide Images.

Squamous cell carcinoma (SCC) is a histologic type of cancer that exhibits various degrees of keratinization. Identifying lymph node metastasis in SCC is crucial for prognosis and treatment strategies. Although artificial intelligence (AI) has shown promise in cancer prediction, applications specifically targeting SCC are limited. To design and validate a deep learning model tailored to predict metastatic SCC in radical lymph node dissection specimens, using whole slide images (WSIs). Using the EfficientNetB1 architecture, a model was trained on 6587 WSIs (2413 SCC and 4174 nonneoplastic) from several hospitals, encompassing esophagus, head and neck, lung, and skin specimens. The training exclusively relied on WSI-level labels without annotations. We evaluated the model on a test set consisting of 541 WSIs (41 SCC and 500 nonneoplastic) of radical lymph node dissection specimens. The model exhibited high performance, with receiver operating characteristic curve areas under the curve between 0.880 and 0.987 in detecting SCC metastases in lymph nodes. Although true positives and negatives were accurately identified, certain limitations were observed. These included false positives due to germinal centers, dust cell aggregations, and specimen-handling artifacts, as well as false negatives due to poor differentiation. The developed artificial intelligence model presents significant potential in enhancing SCC lymph node detection, offering workload reduction for pathologists and increasing diagnostic efficiency. Continuous refinement is needed to overcome existing challenges, making the model more robust and clinically relevant.

Assessing the Legality of Autonomous Weapon Systems: An In-depth Examination of International Humanitarian Law Principles

The use of autonomous weapons systems (AWS) to select targets and attack them without human intervention poses a real legal dilemma. What heralds the urgency of the issue is the emergence of some unofficial reports talking about AWS entering the battlefield in recent armed conflicts. Previous literature has been inconclusive on the legitimacy of AWS. This is what prompted us to do this research, which deserves to be investigated in more depth to help reach an international consensus within the international humanitarian law (IHL) framework. The article uses a combination of both doctrinal and non-doctrinal methodology to provide a more comprehensive understanding of the issue. The methodology focuses on analyzing AWS through the perspective of IHL principles because it is the most related law by which the legitimacy of AWS can be assessed. The data collected were secondary and analyzed using quantitative data analysis to shed light on the contradiction between public sentiment and the actual trajectory of AWS development. The results show that military necessity and humanity are two concepts inherent in the true principles of IHL that do not accept measurement or compromise. The article concludes that although artificial intelligence (AI) has not yet reached a threshold that allows reliable deployment of AWS, However, the acceleration of its development indicates that AWS will be able to comply with true IHL principles in the near future.

Enabling Personalization for Digital Cognitive Stimulation to Support Communication With People With Dementia: Pilot Intervention Study as a Prelude to AI Development.

Maintaining good communication and engagement between people with dementia and their caregivers is a major challenge in dementia care. Cognitive stimulation is a psychosocial intervention that supports communication and engagement, and several digital applications for cognitive stimulation have been developed. Personalization is an important factor for obtaining sustainable benefits, but the time and effort required to personalize and optimize applications often makes them difficult for routine use by nonspecialist caregivers and families. Although artificial intelligence (AI) has great potential to support automation of the personalization process, its use is largely unexplored because of the lack of suitable data from which to develop and train machine learning models. This pilot study aims to evaluate a digital application called Aikomi in Japanese care homes for its potential to (1) create and deliver personalized cognitive stimulation programs to promote communication and engagement between people with dementia and usual care staff and (2) capture meaningful personalized data suitable for the development of AI systems. A modular technology platform was developed and used to create personalized programs for 15 people with dementia living in 4 residential care facilities in Japan with the cooperation of a family member or care staff. A single intervention with the program was conducted with the person with dementia together with a care staff member, and for some participants, smell stimulation was provided using selected smell sticks in conjunction with the digital program. All sessions were recorded using a video camera, and the combined personalized data obtained by the platform were analyzed. Most people with dementia (10/15, 67%) showed high levels of engagement (>40 on Engagement of a Person with Dementia Scale), and there were no incidences of negative reactions toward the programs. Care staff reported that some participants showed extended concentration and spontaneous communication while using Aikomi, which was not their usual behavior. Smell stimulation promoted engagement for some participants even when they were unable to identify the smell. No changes in well-being were observed following the intervention according to the Mental Function Impairment Scale. The level of response to each type of content in the stimulation program varied greatly according to the person with dementia, and personalized data captured by the Aikomi platform enabled understanding of correlations between stimulation content and responses for each participant. This study suggests that the Aikomi digital application is acceptable for use by persons with dementia and care staff and may have the potential to promote communication and engagement. The platform captures personalized data, which can provide suitable input for machine learning. Further investigation of Aikomi will be conducted to develop AI systems and create personalized digital cognitive stimulation applications that can be easily used by nonspecialist caregivers.

Determinants influencing the adoption of artificial intelligence technology in non-life insurers.

Although artificial intelligence (AI) technology has been widely used in the insurance industry at a global scale, studies examining the adoption of AI technology in emerging markets are few and far between. This paper fills this gap by using Cronbach’s alpha, exploratory factor analysis, confirmatory factor analysis, and structural equation model (SEM) to discover significant factors affecting their behavioral intentions to adopt AI technology in Vietnam, a developing country. Data is collected from nearly 470 employees in Vietnamese non-life insurance firms. Empirical findings show that the most important determinant influencing the adoption of AI technology in Vietnamese non-life insurers is attitudes toward adoption. Attitudes toward adoption are positively related to the perceived ease of use and perceived usefulness, consistent with Gupta et al. (2022). Although perceived risk has a negative influence on the behavioral intention to adopt AI technology, it is not a serious issue for insurance companies.

Delegation of purchasing tasks to AI: The role of perceived choice and decision autonomy

Although artificial intelligence (AI) outperforms humans in many tasks, research suggests some consumers are still averse to having AI perform tasks on their behalf. Informed by the literature of customer decision-making process, we propose and show that consumer autonomy is a significant predictor of customers' decision to adopt AI in the purchasing context. Across three experiments, we found that the delegation of purchasing tasks to AI, which restricts choice and decision dimensions of consumers' perceived autonomy, reduces the likelihood of AI adoption. Our results show that the effects of choice and decision autonomy on AI adoption holds even when product choice evaluation is complex. We also found that identity-relevant consumption moderates this relationship, such that it interacts with choice and decision autonomy. Specifically, despite lacking choice and decision autonomy, those who identify strongly with a given activity are more likely to use an AI-enabled app to purchase the product needed to perform this activity. With these insights into when and why consumers are likely to use AI-enabled technology, firms might effectively increase its adoption.

Bots for mental health: the boundaries of human and technology agencies for enabling mental well-being within organizations

PurposeThe importance of mental wellbeing and the need for organizations to address it is increasing in the post-pandemic context. Although Artificial Intelligence (AI) is increasingly being adopted in HRM functions, its adoption and utility for enabling mental wellbeing is limited. Building on the Open System Theory (OST) and adopting the technology-in-practice lens, the authors examined the roles of human and technology agencies in enabling mental wellbeing.Design/methodology/approachThe study was conducted in two stages; in Stage 1, the authors adopted a case methodology approach to examine the feasibility of a technology company's offerings to assess mental wellbeing. In Stage 2, the authors followed the grounded theory approach and interviewed 22 key stakeholders and HR leaders of diverse organizations. The authors used Gioia's approach to analyze the data.FindingsThe study demonstrates the interdependence and inseparability of human activity, technological capability and structured context. Specifically, the authors observe that AI adoption is pushing the boundaries of how organizations could support employees' mental health and wellbeing. These technological advancements and adoption are likely to facilitate the evolution of agentic practices, routines and structures.Research limitations/implicationsThis study carries two important implications. While the advent of cutting-edge technologies appears to affect employees' mental wellbeing, the study findings indicate the assistive role of technology in supporting mental wellbeing and facilitating changes in organizational practices. Second, the ontology of technology-in-practice shows how human–machine agencies gain newer relevance from the interactions that unite them. Specifically, per OST, technology (from an external context) can potentially change how mental wellbeing practices in organizations are managed. The authors extend the existing literature by suggesting that both human agents and internal contexts effectively limit the potential of technology agents to change existing structures significantly.Originality/valueThe authors address the need for more research on the technology-management interface, and the boundaries of technology-enabled wellbeing at work. While AI-HRM scholarship has primarily relied on micro-level psychological theories to examine impact and outcomes, the authors borrow from the macro-level theories, such as the OST and the technology-in-practice to explain how AI is shifting the boundaries of human and machine agencies for enabling mental wellbeing.

High‐definition map automatic annotation system based on active learning

AbstractAs autonomous vehicle technology advances, high‐definition (HD) maps have become essential for ensuring safety and navigation accuracy. However, creating HD maps with accurate annotations demands substantial human effort, leading to a time‐consuming and costly process. Although artificial intelligence (AI) and computer vision (CV) algorithms have been developed for prelabeling HD maps, a significant gap remains in accuracy and robustness between AI‐based methods and traditional manual pipelines. Additionally, building large‐scale annotated datasets and advanced machine learning algorithms for AI‐based HD map labeling systems can be resource‐intensive. In this paper, we present and summarize the Tencent HD Map AI (THMA) system, an innovative end‐to‐end, AI‐based, active learning HD map labeling system designed to produce HD map labels for hundreds of thousands of kilometers while employing active learning to enhance product iteration. Utilizing a combination of supervised, self‐supervised, and weakly supervised learning, THMA is trained directly on massive HD map datasets to achieve the high accuracy and efficiency required by downstream users. Deployed by the Tencent Map team, THMA serves over 1000 labeling workers and generates more than 30,000 km of HD map data per day at its peak. With over 90% of Tencent Map's HD map data labeled automatically by THMA, the system accelerates traditional HD map labeling processes by more than tenfold, significantly reducing manual annotation burdens and paving the way for more efficient HD map production.

Assessing the Performance of ChatGPT in Medical Biochemistry Using Clinical Case Vignettes: Observational Study.

ChatGPT has gained global attention recently owing to its high performance in generating a wide range of information and retrieving any kind of data instantaneously. ChatGPT has also been tested for the United States Medical Licensing Examination (USMLE) and has successfully cleared it. Thus, its usability in medical education is now one of the key discussions worldwide. The objective of this study is to evaluate the performance of ChatGPT in medical biochemistry using clinical case vignettes. The performance of ChatGPT was evaluated in medical biochemistry using 10 clinical case vignettes. Clinical case vignettes were randomly selected and inputted in ChatGPT along with the response options. We tested the responses for each clinical case twice. The answers generated by ChatGPT were saved and checked using our reference material. ChatGPT generated correct answers for 4 questions on the first attempt. For the other cases, there were differences in responses generated by ChatGPT in the first and second attempts. In the second attempt, ChatGPT provided correct answers for 6 questions and incorrect answers for 4 questions out of the 10 cases that were used. But, to our surprise, for case 3, different answers were obtained with multiple attempts. We believe this to have happened owing to the complexity of the case, which involved addressing various critical medical aspects related to amino acid metabolism in a balanced approach. According to the findings of our study, ChatGPT may not be considered an accurate information provider for application in medical education to improve learning and assessment. However, our study was limited by a small sample size (10 clinical case vignettes) and the use of the publicly available version of ChatGPT (version 3.5). Although artificial intelligence (AI) has the capability to transform medical education, we emphasize the validation of such data produced by such AI systems for correctness and dependability before it could be implemented in practice.

Explainable artificial intelligence in disaster risk management: Achievements and prospective futures

Disasters can have devastating impacts on communities and economies, underscoring the urgent need for effective strategic disaster risk management (DRM). Although Artificial Intelligence (AI) holds the potential to enhance DRM through improved decision-making processes, its inherent complexity and "black box" nature have led to a growing demand for Explainable AI (XAI) techniques. These techniques facilitate the interpretation and understanding of decisions made by AI models, promoting transparency and trust. However, the current state of XAI applications in DRM, their achievements, and the challenges they face remain underexplored. In this systematic literature review, we delve into the burgeoning domain of XAI-DRM, extracting 195 publications from the Scopus and ISI Web of Knowledge databases, and selecting 68 for detailed analysis based on predefined exclusion criteria. Our study addresses pertinent research questions, identifies various hazard and disaster types, risk components, and AI and XAI methods, uncovers the inherent challenges and limitations of these approaches, and provides synthesized insights to enhance their explainability and effectiveness in disaster decision-making. Notably, we observed a significant increase in the use of XAI techniques for DRM in 2022 and 2023, emphasizing the growing need for transparency and interpretability. Through a rigorous methodology, we offer key research directions that can serve as a guide for future studies. Our recommendations highlight the importance of multi-hazard risk analysis, the integration of XAI in early warning systems and digital twins, and the incorporation of causal inference methods to enhance DRM strategy planning and effectiveness. This study serves as a beacon for researchers and practitioners alike, illuminating the intricate interplay between XAI and DRM, and revealing the profound potential of AI solutions in revolutionizing disaster risk management.