Artificial Intelligence Training Research Articles

Employee learning and skilling in AI embedded organizations – predictors and outcomes

Purpose In this research article we aim to answer two research questions: - With ever increasing AI based integrations in organizations, it is imperative to understand how employees learn and adapt to these newer technologies? What is the impact of this learning and adaptation on AI-based employee productivity? Design/methodology/approach A survey-based research design has been conducted among the employees working in technology companies. Questionnaires were administered by the researchers physically using snowball based purposive sampling to reach the participants who are working in the AI driven technology companies. The data was analyzed using Structural Equation Modelling and numerous studies have previously used this approach for model fit and testing. Findings it was found that employee learning has significant positive mediational impact on the relationship between AI trust, knowledge sharing and AI skills and AI-employee collaboration. SEM results indicate that data fits the proposed model well. Practical implications findings indicate that as the trust in AI develops, employees are more interested in learning and increasing their understanding of AI driven work and increase their AI skills such as Machine learning and data analytics. To increase AI-employee collaborations and upskilling: organizations will need to foster AI training opportunities; make employees work on projects having AI based sociotechnical systems; create environment of knowledge sharing among employees on AI themes. Originality/value The use of Artificial Intelligence (AI) is ubiquitous, and more and more organizations are adopting AI in their operations which lead to massive scale changes in the functioning of organizations. These large-scale massive transformations are posing challenges and opportunities at the same time. At the same time, it created immense challenges for employees’ adjustment to the new learning and skilling requirements posed by AI (Sengupta et al., 2022). It is therefore expected that employees embrace the associated new technologies of AI such as big data analytics, Machine learning and Robotics.

Enhancing Workplace Safety through Personalized Environmental Risk Assessment: An AI-Driven Approach in Industry 5.0

This paper describes an integrated monitoring system designed for individualized environmental risk assessment and management in the workplace. The system incorporates monitoring devices that measure dust, noise, ultraviolet radiation, illuminance, temperature, humidity, and flammable gases. Comprising monitoring devices, a server-based web application for employers, and a mobile application for workers, the system integrates the registration of workers’ health histories, such as common diseases and symptoms related to the monitored agents, and a web-based recommendation system. The recommendation system application uses classifiers to decide the risk/no risk per sensor and crosses this information with fixed rules to define recommendations. The system generates actionable alerts for companies to improve decision-making regarding professional activities and long-term safety planning by analyzing health information through fixed rules and exposure data through machine learning algorithms. As the system must handle sensitive data, data privacy is addressed in communication and data storage. The study provides test results that evaluate the performance of different machine learning models in building an effective recommendation system. Since it was not possible to find public datasets with all the sensor data needed to train artificial intelligence models, it was necessary to build a data generator for this work. By proposing an approach that focuses on individualized environmental risk assessment and management, considering workers’ health histories, this work is expected to contribute to enhancing occupational safety through computational technologies in the Industry 5.0 approach.

Designing a Competency-Focused Course on Applied AI Based on Advanced System Research on Business Requirements

The consortium of “The Future is in Applied Artificial Intelligence” Project designed the first competency-based applied artificial intelligence curriculum at the higher-education institution level. The development was based on advanced system research on existing artificial intelligence-related resources and surveying target groups of teachers, information technology students, and employers, which should enhance the performance of implementing artificial intelligence education. A review of applied artificial intelligence was prepared in the form of keyword clustering. The initial data were collected with the help of surveying by identifying job offers, existing artificial intelligence training courses, scientific projects, and real cases. A synthetic analysis of the textual information from the studies was conducted using the word clouds technique. A tensor-based approach was used for the presentation of the competency-based course. The specific numerical requirements for the course in the form of priorities followed from the solution to decision-making problems using the analytic hierarchy process technique. Based on a comprehensive study of surveys, educational experience, scientific projects, and business requirements, and a meta-analysis of the recent references, we specified the criteria for a training course in the form of a tensor-based representation of competencies in relation to content and educational modules.

A Beginner's Guide to Artificial Intelligence for Ophthalmologists.

The integration of artificial intelligence (AI) in ophthalmology has promoted the development of the discipline, offering opportunities for enhancing diagnostic accuracy, patient care, and treatment outcomes. This paper aims to provide a foundational understanding of AI applications in ophthalmology, with a focus on interpreting studies related to AI-driven diagnostics. The core of our discussion is to explore various AI methods, including deep learning (DL) frameworks for detecting and quantifying ophthalmic features in imaging data, as well as using transfer learning for effective model training in limited datasets. The paper highlights the importance of high-quality, diverse datasets for training AI models and the need for transparent reporting of methodologies to ensure reproducibility and reliability in AI studies. Furthermore, we address the clinical implications of AI diagnostics, emphasizing the balance between minimizing false negatives to avoid missed diagnoses and reducing false positives to prevent unnecessary interventions. The paper also discusses the ethical considerations and potential biases in AI models, underscoring the importance of continuous monitoring and improvement of AI systems in clinical settings. In conclusion, this paper serves as a primer for ophthalmologists seeking to understand the basics of AI in their field, guiding them through the critical aspects of interpreting AI studies and the practical considerations for integrating AI into clinical practice.

Video kills the radio star: Copyright and the human versus artificial creativity war

AbstractThis article contributes to the dynamic debate surrounding the intersection of artificial intelligence (AI) and copyright law, offering a fresh perspective that builds upon interdisciplinary analyses. Focusing on the cognitive processes underpinning creativity in both human and AI contexts, the study draws a detailed parallel between Vincent Van Gogh's iconic “Starry Night” and its AI‐generated counterpart generated through DeepDream technology. Central to the investigation is the application of psychological and neuroscientific theories to understand and compare the creative processes in humans and AI. Based on such exercise, the article first examines whether art generated with AI, devoid of human emotions and motivations yet capable of mimicking human creative cognitive processes, qualifies for copyright protection. The analysis suggests that the similarities between human and AI creativity, particularly in their cognitive structuring, could render the work “original” according to different jurisdictional standards and interpretation of copyright law. Second, the article investigates whether AI infringes copyright if protected material is used for its training and processing. This question becomes particularly relevant in light of recent legal actions against AI‐artwork generators in California, which raise issues of potential infringement by AI using latent diffusion techniques on existing artworks. The discussion provides an original perspective that can advance the ongoing debate on the use of copyrighted material for AI training. The paper aims to contribute to the ongoing debate about AI and copyright by challenging the traditional human‐centric view of authorship in copyright law. The article argues for a nuanced understanding that acknowledges the complex nature of creativity, transcending the binary division between human and artificial sources. This approach is critical in redefining legal frameworks, ensuring they are adaptive to the evolving landscape of AI capabilities. At the same time, the article addresses the implications of AI drawing inspiration from existing art, recognizing the need to balance different stakeholders' interests when drawing policy considerations. Ultimately, the goal is to provide a layered perspective that not only deepens the legal discourse but also respects and fosters the coexistence and mutual advancement of both human and artificial creativity in the digital age, in line with the purpose of copyright.

Designing a diagnostic method to predict the optimal artificial insemination timing in cows using artificial intelligence

Dairy farmers and beef cattle breeders aim for one calf per year to optimize breeding efficiency, relying on artificial insemination of both dairy and beef cows. Accurate estrus detection and timely insemination are vital for improving conception rates. However, recent challenges such as operational expansion, increased livestock numbers, and heightened milk production have complicated these processes. We developed an artificial intelligence (AI)-based pregnancy probability diagnostic tool to predict the optimal timing for artificial insemination. This tool analyzes external uterine opening image data through AI analysis, enabling high conception rates when inexperienced individuals conduct the procedure. In the initial experimental phase, images depicting the external uterine opening during artificial insemination were acquired for AI training. Static images were extracted from videos to create a pregnancy probability diagnostic model (PPDM). In the subsequent phase, an augmented set of images was introduced to enhance the precision of the PPDM. Additionally, a web application was developed for real-time assessment of optimal insemination timing, and its effectiveness in practical field settings was evaluated. The results indicated that when PPDM predicted a pregnancy probability of 70% or higher, it demonstrated a high level of reliability with accuracy, precision, and recall rates of 76.2%, 76.2%, and 100%, respectively, and an F-score of 0.86. This underscored the applicability and reliability of AI-based tools in predicting optimal insemination timing, potentially offering substantial benefits to breeding operations.

Perceptions of undergraduate medical students on artificial intelligence in medicine: mixed-methods survey study from Palestine

BackgroundThe current applications of artificial intelligence (AI) in medicine continue to attract the attention of medical students. This study aimed to identify undergraduate medical students’ attitudes toward AI in medicine, explore present AI-related training opportunities, investigate the need for AI inclusion in medical curricula, and determine preferred methods for teaching AI curricula.MethodsThis study uses a mixed-method cross-sectional design, including a quantitative study and a qualitative study, targeting Palestinian undergraduate medical students in the academic year 2022–2023. In the quantitative part, we recruited a convenience sample of undergraduate medical students from universities in Palestine from June 15, 2022, to May 30, 2023. We collected data by using an online, well-structured, and self-administered questionnaire with 49 items. In the qualitative part, 15 undergraduate medical students were interviewed by trained researchers. Descriptive statistics and an inductive content analysis approach were used to analyze quantitative and qualitative data, respectively.ResultsFrom a total of 371 invitations sent, 362 responses were received (response rate = 97.5%), and 349 were included in the analysis. The mean age of participants was 20.38 ± 1.97, with 40.11% (140) in their second year of medical school. Most participants (268, 76.79%) did not receive formal education on AI before or during medical study. About two-thirds of students strongly agreed or agreed that AI would become common in the future (67.9%, 237) and would revolutionize medical fields (68.7%, 240). Participants stated that they had not previously acquired training in the use of AI in medicine during formal medical education (260, 74.5%), confirming a dire need to include AI training in medical curricula (247, 70.8%). Most participants (264, 75.7%) think that learning opportunities for AI in medicine have not been adequate; therefore, it is very important to study more about employing AI in medicine (228, 65.3%). Male students (3.15 ± 0.87) had higher perception scores than female students (2.81 ± 0.86) (p < 0.001). The main themes that resulted from the qualitative analysis of the interview questions were an absence of AI learning opportunities, the necessity of including AI in medical curricula, optimism towards the future of AI in medicine, and expected challenges related to AI in medical fields.ConclusionMedical students lack access to educational opportunities for AI in medicine; therefore, AI should be included in formal medical curricula in Palestine.

Cryo2StructData: A Large Labeled Cryo-EM Density Map Dataset for AI-based Modeling of Protein Structures

The advent of single-particle cryo-electron microscopy (cryo-EM) has brought forth a new era of structural biology, enabling the routine determination of large biological molecules and their complexes at atomic resolution. The high-resolution structures of biological macromolecules and their complexes significantly expedite biomedical research and drug discovery. However, automatically and accurately building atomic models from high-resolution cryo-EM density maps is still time-consuming and challenging when template-based models are unavailable. Artificial intelligence (AI) methods such as deep learning trained on limited amount of labeled cryo-EM density maps generate inaccurate atomic models. To address this issue, we created a dataset called Cryo2StructData consisting of 7,600 preprocessed cryo-EM density maps whose voxels are labelled according to their corresponding known atomic structures for training and testing AI methods to build atomic models from cryo-EM density maps. Cryo2StructData is larger than existing, publicly available datasets for training AI methods to build atomic protein structures from cryo-EM density maps. We trained and tested deep learning models on Cryo2StructData to validate its quality showing that it is ready for being used to train and test AI methods for building atomic models.

Data augmentation for generating synthetic electrogastrogram time series.

To address an emerging need for large number of diverse datasets for rigor evaluation of signal processing techniques, we developed and evaluated a new method for generating synthetic electrogastrogram time series. We used electrogastrography (EGG) data from an open database to set model parameters and statistical tests to evaluate synthesized data. Additionally, we illustrated method customization for generating artificial EGG time series alterations caused by the simulator sickness. Proposed data augmentation method generates synthetic EGG data with specified duration, sampling frequency, recording state (postprandial or fasting state), overall noise and breathing artifact injection, and pauses in the gastric rhythm (arrhythmia occurrence) with statistically significant difference between postprandial and fasting states in > 70% cases while not accounting for individual differences. Features obtained from the synthetic EGG signal resembling simulator sickness occurrence displayed expected trends. The code for generation of synthetic EGG time series is not only freely available and can be further customized to assess signal processing algorithms but also may be used to increase data diversity for training artificial intelligence (AI) algorithms. The proposed approach is customized for EGG data synthesis but can be easily utilized for other biosignals with similar nature such as electroencephalogram.

Towards holistic insect monitoring: species discovery, description, identification and traits for all insects.

Holistic insect monitoring needs scalable techniques to overcome taxon biases, determine species abundances, and gather functional traits for all species. This requires that we address taxonomic impediments and the paucity of data on abundance, biomass and functional traits. We here outline how these data deficiencies could be addressed at scale. The workflow starts with large-scale barcoding (megabarcoding) of all specimens from mass samples obtained at biomonitoring sites. The barcodes are then used to group the specimens into molecular operational taxonomic units that are subsequently tested/validated as species with a second data source (e.g. morphology). New species are described using barcodes, images and short diagnoses, and abundance data are collected for both new and described species. The specimen images used for species discovery then become the raw material for training artificial intelligence identification algorithms and collecting trait data such as body size, biomass and feeding modes. Additional trait data can be obtained from vouchers by using genomic tools developed by molecular ecologists. Applying this pipeline to a few samples per site will lead to greatly improved insect monitoring regardless of whether the species composition of a sample is determined with images, metabarcoding or megabarcoding. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Advancements in oligometastatic breast cancer: a comprehensive review of current strategies and the role of artificial intelligence.

In the dynamic landscape of Breast Cancer (BC), Oligo- Metastatic Breast Cancer (OMBC) presents unique challenges and opportunities. This comprehensive review delves into current strategies for addressing OMBC, covering locoregional and site-specific metastasis management, and addressing both surgical and minimally invasive therapies as essential components. Moreover, the transformative role of Artificial Intelligence (AI) is spotlighted. However, while the future looks promising, several limitations need addressing, including the need for further research, especially in diverse patient populations and resource-challenged settings. AI implementation may require overcoming the lack of Electronic Health Records acceptance in resource-challenged countries, which contributes to a scarcity of large datasets for AI training. As AI continues to evolve, validation and regulatory aspects must be continually addressed for seamless integration into clinical practice. In summary, this review outlines the evolving landscape of OMBC management, emphasizing the need for comprehensive research, global collaboration, and innovative AI solutions to enhance patient care and outcomes.

Automated precise seeding with drones and artificial intelligence: a workflow

Aerial seeding with drones has great potential in forest restoration but faces enormous challenges to be efficient and scalable. Current protocols use blanket seeding throughout the area to be restored, meaning a high demand for seed since many seeds arrive in sites unsuitable for establishment. High precision seeding directed to safe microsites at submeter scale could reduce seed use per hectare, reducing economic and ecological costs, while increasing establishment success. Here, we propose an alternative, precision approach to make drone seeding more successful and efficient. This requires (1) submeter‐scale selection of target microsites for seeding founded in ecological knowledge; (2) high‐resolution remote sensing imagery to train artificial intelligence (AI) systems in target microsite recognition; and (3) process automation by transferring target microsite coordinates from the AI system to the drone. This will reduce seed inputs per unit area, seedling establishment failure risks, and drone operation costs.

Transforming Healthcare with AI: Promises, Pitfalls, and Pathways Forward.

This perspective paper provides a comprehensive examination of artificial intelligence (AI) in healthcare, focusing on its transformative impact on clinical practices, decision-making, and physician-patient relationships. By integrating insights from evidence, research, and real-world examples, it offers a balanced analysis of AI's capabilities and limitations, emphasizing its role in streamlining administrative processes, enhancing patient care, and reducing physician burnout while maintaining a human-centric approach in medicine. The research underscores AI's capacity to augment clinical decision-making and improve patient interactions, but it also highlights the variable impact of AI in different healthcare settings. The need for context-specific adaptations and careful integration of AI technologies into existing healthcare workflows is emphasized to maximize benefits and minimize unintended consequences. Significant attention is given to the implications of AI on the roles and competencies of healthcare professionals. The emergence of AI necessitates new skills in data literacy and technology use, prompting a shift in educational curricula towards digital health and AI training. Ethical considerations are a pivotal aspect of the discussion. The paper explores the challenges posed by data privacy concerns, algorithmic biases, and ensuring equitable access to AI-driven healthcare. It advocates for the development of comprehensive ethical frameworks and ongoing research to guide the responsible use of AI in healthcare. Conclusively, the paper advocates for a balanced approach to AI adoption in healthcare, highlighting the importance of ongoing research, strategic implementation, and the synergistic combination of human expertise with AI technologies for optimal patient care.

Integration of Artificial Intelligence in Facial Recognition Systems for Software Security

Facial recognition technology, a cornerstone in modern software security, has seen significant advancements through the integration of Artificial Intelligence (AI). This research focuses on enhancing facial recognition systems by incorporating sophisticated machine learning algorithms and deep neural networks. By doing so, the goal is to increase the accuracy and reliability of these systems in security applications. The study uses a variety of facial datasets to train AI models that are adept at extracting facial features and recognizing patterns. These models are subjected to rigorous testing to evaluate their performance in terms of identification accuracy, processing speed, and adaptability to different environmental conditions. One of the key challenges addressed in the research is the system's vulnerability to errors and potential misuse. Ethical considerations and privacy concerns are at the forefront of the study. The research highlights the importance of designing AI-based facial recognition systems that respect user privacy and are resistant to biases, thus fostering trust and acceptance among users. The results of the study show a marked improvement in system performance, demonstrating enhanced recognition accuracy and speed, while maintaining robustness across different conditions. By offering practical recommendations for the development of secure, ethical, and privacy-aware facial recognition systems, this research contributes valuable insights into the integration of AI in software security. It underscores the importance of continuous innovation and ethical responsibility in the deployment of facial recognition technologies, shaping the future landscape of technological security measures

Exploring the Performance of ChatGPT Versions 3.5, 4, and 4 With Vision in the Chilean Medical Licensing Examination: Observational Study.

The deployment of OpenAI's ChatGPT-3.5 and its subsequent versions, ChatGPT-4 and ChatGPT-4 With Vision (4V; also known as "GPT-4 Turbo With Vision"), has notably influenced the medical field. Having demonstrated remarkable performance in medical examinations globally, these models show potential for educational applications. However, their effectiveness in non-English contexts, particularly in Chile's medical licensing examinations-a critical step for medical practitioners in Chile-is less explored. This gap highlights the need to evaluate ChatGPT's adaptability to diverse linguistic and cultural contexts. This study aims to evaluate the performance of ChatGPT versions 3.5, 4, and 4V in the EUNACOM (Examen Único Nacional de Conocimientos de Medicina), a major medical examination in Chile. Three official practice drills (540 questions) from the University of Chile, mirroring the EUNACOM's structure and difficulty, were used to test ChatGPT versions 3.5, 4, and 4V. The 3 ChatGPT versions were provided 3 attempts for each drill. Responses to questions during each attempt were systematically categorized and analyzed to assess their accuracy rate. All versions of ChatGPT passed the EUNACOM drills. Specifically, versions 4 and 4V outperformed version 3.5, achieving average accuracy rates of 79.32% and 78.83%, respectively, compared to 57.53% for version 3.5 (P<.001). Version 4V, however, did not outperform version 4 (P=.73), despite the additional visual capabilities. We also evaluated ChatGPT's performance in different medical areas of the EUNACOM and found that versions 4 and 4V consistently outperformed version 3.5. Across the different medical areas, version 3.5 displayed the highest accuracy in psychiatry (69.84%), while versions 4 and 4V achieved the highest accuracy in surgery (90.00% and 86.11%, respectively). Versions 3.5 and 4 had the lowest performance in internal medicine (52.74% and 75.62%, respectively), while version 4V had the lowest performance in public health (74.07%). This study reveals ChatGPT's ability to pass the EUNACOM, with distinct proficiencies across versions 3.5, 4, and 4V. Notably, advancements in artificial intelligence (AI) have not significantly led to enhancements in performance on image-based questions. The variations in proficiency across medical fields suggest the need for more nuanced AI training. Additionally, the study underscores the importance of exploring innovative approaches to using AI to augment human cognition and enhance the learning process. Such advancements have the potential to significantly influence medical education, fostering not only knowledge acquisition but also the development of critical thinking and problem-solving skills among health care professionals.

VAEFL: Integrating Variational Autoencoders for Privacy Preservation and Performance Retention in Federated Learning

Federated Learning (FL) heralds a paradigm shift in the training of artificial intelligence (AI) models by fostering collaborative model training while safeguarding client data privacy. In sectors where data sensitivity and AI model security are of paramount importance, such as fintech and biomedicine, maintaining the utility of models without compromising privacy is crucial with the growing application of artificial intelligence technologies. Therefore, the adoption of FL is attracting significant attention. However, traditional Federated Learning methods are vulnerable to Deep Leakage from Gradients (DLG) attacks, and typical defensive strategies often result in excessive computational costs or substantial decreases in model accuracy. To navigate these challenges, this research introduces VAEFL, an innovative FL framework that incorporates Variational Autoencoders (VAEs) to bolster privacy protection without undermining the predictive prowess of the models. VAEFL strategically partitions the model into a private encoder and a public decoder. The private encoder, remaining local, transmutes sensitive data into a latent space fortified for privacy, while the public decoder and classifier, through collaborative training across clients, learn to derive precise predictions from the encoded data. This bifurcation ensures that sensitive data attributes are not disclosed, circumventing gradient leakage attacks and simultaneously allowing the global model to benefit from the diverse knowledge of client datasets. Comprehensive experiments demonstrate that VAEFL not only surpasses standard FL benchmarks in privacy preservation but also maintains competitive performance in predictive tasks. VAEFL thus establishes a novel equilibrium between data privacy and model utility, offering a secure and efficient federated learning approach for the sensitive application of FL in the financial domain.

Automated scoring of glomerular injury in TNS2-deficient nephropathy.

Several artificial intelligence (AI) systems have been developed for glomerular pathology analysis in clinical settings. However, the application of AI systems in nonclinical fields remains limited. In this study, we trained a convolutional neural network model, which is an AI algorithm, to classify the severity of Tensin 2 (TNS2)-deficient nephropathy into seven categories. A dataset consisting of 803 glomerular images was generated from kidney sections of TNS2-deficient and wild-type mice. Manual evaluations of the images were conducted to assess their glomerular injury scores. The trained AI achieved approximately 70% accuracy in predicting the glomerular injury score for TNS2-deficient nephropathy. However, the AI achieved approximately 100% accuracy when considering predictions within one score of the true label as correct. The AI's predicted mean score closely matched the true mean score. In conclusion, while the AI model may not replace human judgment entirely, it can serve as a reliable second assessor in scoring glomerular injury, offering potential benefits in enhancing the accuracy and objectivity of such assessments.

FastMRI Prostate: A public, biparametric MRI dataset to advance machine learning for prostate cancer imaging

Magnetic resonance imaging (MRI) has experienced remarkable advancements in the integration of artificial intelligence (AI) for image acquisition and reconstruction. The availability of raw k-space data is crucial for training AI models in such tasks, but public MRI datasets are mostly restricted to DICOM images only. To address this limitation, the fastMRI initiative released brain and knee k-space datasets, which have since seen vigorous use. In May 2023, fastMRI was expanded to include biparametric (T2- and diffusion-weighted) prostate MRI data from a clinical population. Biparametric MRI plays a vital role in the diagnosis and management of prostate cancer. Advances in imaging methods, such as reconstructing under-sampled data from accelerated acquisitions, can improve cost-effectiveness and accessibility of prostate MRI. Raw k-space data, reconstructed images and slice, volume and exam level annotations for likelihood of prostate cancer are provided in this dataset for 47468 slices corresponding to 1560 volumes from 312 patients. This dataset facilitates AI and algorithm development for prostate image reconstruction, with the ultimate goal of enhancing prostate cancer diagnosis.

Water transport in PEMFC with metal foam flow fields: Visualization based on AI image recognition

Water transport is an essential process in proton exchange membrane fuel cells (PEMFC). For the novel metal foam flow fields, water transport is difficult to be visualized and still remains inadequately understood. In this work, for the first time, the two-phase flows in the metal foam flow fields of a transparent PEMFC was visualized by using an artificial intelligence (AI) image recognition method. In order to distinguish water from the metal foam ligaments, a scoring system was developed, which effectively solved the problem of inconsistent reflectance of liquid water between the ligament and the gas diffusion layer. According to the state of liquid water, the scoring system categorized the local images into three labels of waterless state, wet state, and slug state for AI image recognition network training. The trained AI image recognition model was then used to visualize the liquid water state in the metal foam flow fields under different operating conditions of relative humidity (RH), temperature, and inlet flow rate. Results show that, since the porous structure of the metal foam provides flexible transport paths, the PEMFC can operate effectively in a wide range of wet states. Even a 25% difference in the wet-state percentage between different operating conditions yields an almost consistent voltage. The increased slugs in the flow fields are the primary reason for the performance degradation of the PEMFC. At a high current density of 2 A/cm2, the voltage is as low as 0.03 V when the slug state occupies 35% of the flow fields (100% RH, 60 °C, 1.0 SLPM cathode inlet flow rate), and as high as 0.56 V when the slug state is absent in the flow fields (100% RH, 60 °C, 2.0 SLPM cathode inlet flow rate). The present work provides a novel approach for the studies of water transport in PEMFC.

Reinvestigating the performance of artificial intelligence classification algorithms on COVID-19 X-Ray and CT images

There are concerns that artificial intelligence (AI) algorithms may create underdiagnosis bias by mislabeling patient individuals with certain attributes (e.g., female and young) as healthy. Addressing this bias is crucial given the urgent need for AI diagnostics facing rapidly spreading infectious diseases like COVID-19. We find the prevalent AI diagnostic models show an underdiagnosis rate among specific patient populations, and the underdiagnosis rate is higher in some intersectional specific patient populations (for example, females aged 20-40 years). Additionally, we find training AI models on heterogeneous datasets (positive and negative samples from different datasets) may lead to poor model generalization. The model's classification performance varies significantly across test sets, with the accuracy of the better performance being over 40% higher than that of the poor performance. In conclusion, we developed an AI bias analysis pipeline to help researchers recognize and address biases that impact medical equality and ethics.