Artificial Intelligence Assistance Research Articles

THE OBJECTIVE NEED TO IMPLEMENT AN INFORMATION SYSTEM TO AUTOMATE DAILY TASKS IN RECRUITMENT WORKFLOW

This article emphasizes the demand and significance of the implementation and integration of an information system for the nowadays recruitment process optimization. The article introduces the inherent characteristics and nature of information systems and outlines an essential range of tasks they can perform in the context of recruitment. This research examines and determines the direction of implementation of information technology systems, automation, and regular recruiters' routine tasks and processes optimization using artificial intelligence assistance. The primary objectives of the implementation of the proposed information system are quality improvements, human resources saving, unification of the processes, and introduction of the end-to-end solution for candidate interviewing. The weak sides of existing processes, solutions, and the correlation between the effectiveness of the recruitment workflow and work automation are determined. The article conclusively proves that the introduction and utilization of new information system tools and solutions are indispensable conditions for successful up-to-date recruitment and it would be significant input into business development strategies since it will help to not only save human resources but as well work for the company's market brand, reputation gain and significantly decrease risks of wrong hiring.

The diagnostic value of chest X-ray scanning by the help of Artificial Intelligence in Heart Failure (ART-IN-HF).

Typical signs of heart failure (HF), like increased cardiothoracic ratio (CTR) and pleural effusion, can be seen on X-ray. Artificial Intelligence (AI) can help in the early and quicker diagnosis of HF. The study's goal was to demonstrate that the AI interpretation of chest X-rays can assist the clinician in diagnosing HF. Patients older than 45 years were included in the study. The study analyzed 10 100 deidentified outpatient chest X-rays by AI algorithm. The AI-generated report was later verified by an independent radiologist. Patients with CTR > 0.5 and pleural effusion were marked as potential HF. Flagged patients underwent confirmatory tests, and those labeled as negative also underwent further investigations to rule out HF. Out of 10 100, the AI algorithm detected 183 (1.8%) patients with increased CTR and pleural effusion on chest X-rays. One hundred and six out of 183 underwent diagnostic tests. Eighty-two (77%) out of 106 were diagnosed with HF according to current guidelines. From the remaining 9917 patients, 106 patients were randomly selected. Nine (8%) out of them were diagnosed with HF. The positive predictive value of AI for diagnosing HF is 77%, and the negative predictive value is 91%. More than half (54.9%) of newly diagnosed patients had HF with preserved ejection fraction. HF is a risky condition with nonspecific symptoms that are difficult to diagnose, especially in the early stages. Using AI assistance for X-ray interpretation can be helpful for early diagnosis of HF especially HF with preserved ejection fraction.

Artificial Intelligence Assists in the Detection of Blood Vessels in Whole Slide Images: Practical Benefits for Oncological Pathology.

The analysis of the microvasculature and the assessment of angiogenesis have significant prognostic value in various diseases, including cancer. The search for invasion into the blood and lymphatic vessels and the assessment of angiogenesis are important aspects of oncological diagnosis. These features determine the prognosis and aggressiveness of the tumor. Traditional manual evaluation methods are time consuming and subject to inter-observer variability. Blood vessel detection is a perfect task for artificial intelligence, which is capable of rapid analyzing thousands of tissue structures in whole slide images. The development of computer vision solutions requires the segmentation of tissue regions, the extraction of features and the training of machine learning models. In this review, we focus on the methodologies employed by researchers to identify blood vessels and vascular invasion across a range of tumor localizations, including breast, lung, colon, brain, renal, pancreatic, gastric and oral cavity cancers. Contemporary models herald a new era of computational pathology in morphological diagnostics.

A Deep Learning Model Enhances Clinicians' Diagnostic Accuracy to More Than 96% for Anterior Cruciate Ligament Ruptures on Magnetic Resonance Imaging

To develop a deep learning model to accurately detect anterior cruciate ligament (ACL) ruptures on magnetic resonance imaging (MRI) and to evaluate its effect on the diagnostic accuracy and efficiency of clinicians. A training dataset was built from MRIs acquired from January 2017 to June 2021, including patients with knee symptoms, irrespective of ACL ruptures. An external validation dataset was built from MRIs acquired from January 2021 to June 2022, including patients who underwent knee arthroscopy or arthroplasty. Patients with fractures or prior knee surgeries were excluded in both datasets. Subsequently, a deep learning model was developed and validated using these datasets. Clinicians of varying expertise levels in sports medicine and radiology were recruited, and their capacities in diagnosing ACL injuries in terms of accuracy and diagnosing time were evaluated both with and without artificial intelligence (AI) assistance. A deep learning model was developed based on the training dataset of 22,767 MRIs from 5 centers and verified with external validation dataset of 4,086 MRIs from 6 centers. The model achieved an area under the receiver operating characteristic curve of 0.987 and a sensitivity and specificity of 95.1%. Thirty-eight clinicians from 25 centers were recruited to diagnose 3,800 MRIs. The AI assistance significantly improved the accuracy of all clinicians, exceeding 96%. Additionally, a notable reduction in diagnostic time was observed. The most significant improvements in accuracy and time efficiency were observed in the trainee groups, suggesting that AI support is particularly beneficial for clinicians with moderately limited diagnostic expertise. This deep learning model demonstrated expert-level diagnostic performance for ACL ruptures, serving as a valuable tool to assist clinicians of various specialties and experience levels in making accurate and efficient diagnoses. Level III, retrospective comparative case series.

A robust and efficient AI assistant for breast tumor segmentation from DCE-MRI via a spatial-temporal framework

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) allows screening, follow up, and diagnosis for breast tumor with high sensitivity. Accurate tumor segmentation from DCE-MRI can provide crucial information of tumor location and shape, which significantly influences the downstream clinical decisions. In this paper, we aim to develop an artificial intelligence (AI) assistant to automatically segment breast tumors by capturing dynamic changes in multi-phase DCE-MRI with a spatial-temporal framework. The main advantages of our AI assistant include (1) robustness, i.e., our model can handle MR data with different phase numbers and imaging intervals, as demonstrated on a large-scale dataset from seven medical centers, and (2) efficiency, i.e., our AI assistant significantly reduces the time required for manual annotation by a factor of 20, while maintaining accuracy comparable to that of physicians. More importantly, as the fundamental step to build an AI-assisted breast cancer diagnosis system, our AI assistant will promote the application of AI in more clinical diagnostic practices regarding breast cancer.

Deep Learning-Based Precision Analysis for Acrosome Reaction by Modification of Plasma Membrane in Boar Sperm.

The analysis of AR is widely used to detect loss of acrosome in sperm, but the subjective decisions of experts affect the accuracy of the examination. Therefore, we develop an ARCS for objectivity and consistency of analysis using convolutional neural networks (CNNs) trained with various magnification images. Our models were trained on 215 microscopic images at 400× and 438 images at 1000× magnification using the ResNet 50 and Inception-ResNet v2 architectures. These models distinctly recognized micro-changes in the PM of AR sperms. Moreover, the Inception-ResNet v2-based ARCS achieved a mean average precision of over 97%. Our system's calculation of the AR ratio on the test dataset produced results similar to the work of the three experts and could do so more quickly. Our model streamlines sperm detection and AR status determination using a CNN-based approach, replacing laborious tasks and expert assessments. The ARCS offers consistent AR sperm detection, reduced human error, and decreased working time. In conclusion, our study suggests the feasibility and benefits of using a sperm diagnosis artificial intelligence assistance system in routine practice scenarios.

Artificial intelligence suppression as a strategy to mitigate artificial intelligence automation bias.

Incorporating artificial intelligence (AI) into clinics brings the risk of automation bias, which potentially misleads the clinician's decision-making. The purpose of this study was to propose a potential strategy to mitigate automation bias. This was a laboratory study with a randomized cross-over design. The diagnosis of anterior cruciate ligament (ACL) rupture, a common injury, on magnetic resonance imaging (MRI) was used as an example. Forty clinicians were invited to diagnose 200 ACLs with and without AI assistance. The AI's correcting and misleading (automation bias) effects on the clinicians' decision-making processes were analyzed. An ordinal logistic regression model was employed to predict the correcting and misleading probabilities of the AI. We further proposed an AI suppression strategy that retracted AI diagnoses with a higher misleading probability and provided AI diagnoses with a higher correcting probability. The AI significantly increased clinicians' accuracy from 87.2%±13.1% to 96.4%±1.9% (P < .001). However, the clinicians' errors in the AI-assisted round were associated with automation bias, accounting for 45.5% of the total mistakes. The automation bias was found to affect clinicians of all levels of expertise. Using a logistic regression model, we identified an AI output zone with higher probability to generate misleading diagnoses. The proposed AI suppression strategy was estimated to decrease clinicians' automation bias by 41.7%. Although AI improved clinicians' diagnostic performance, automation bias was a serious problem that should be addressed in clinical practice. The proposed AI suppression strategy is a practical method for decreasing automation bias.

Artificial Intelligence Ethics and Challenges in Healthcare Applications: A Comprehensive Review in the Context of the European GDPR Mandate

This study examines the ethical issues surrounding the use of Artificial Intelligence (AI) in healthcare, specifically nursing, under the European General Data Protection Regulation (GDPR). The analysis delves into how GDPR applies to healthcare AI projects, encompassing data collection and decision-making stages, to reveal the ethical implications at each step. A comprehensive review of the literature categorizes research investigations into three main categories: Ethical Considerations in AI; Practical Challenges and Solutions in AI Integration; and Legal and Policy Implications in AI. The analysis uncovers a significant research deficit in this field, with a particular focus on data owner rights and AI ethics within GDPR compliance. To address this gap, the study proposes new case studies that emphasize the importance of comprehending data owner rights and establishing ethical norms for AI use in medical applications, especially in nursing. This review makes a valuable contribution to the AI ethics debate and assists nursing and healthcare professionals in developing ethical AI practices. The insights provided help stakeholders navigate the intricate terrain of data protection, ethical considerations, and regulatory compliance in AI-driven healthcare. Lastly, the study introduces a case study of a real AI health-tech project named SENSOMATT, spotlighting GDPR and privacy issues.

Doctors Identify Hemorrhage Better during Chart Review when Assisted by Artificial Intelligence.

This study evaluated if medical doctors could identify more hemorrhage events during chart review in a clinical setting when assisted by an artificial intelligence (AI) model and medical doctors' perception of using the AI model. To develop the AI model, sentences from 900 electronic health records were labeled as positive or negative for hemorrhage and categorized into one of 12 anatomical locations. The AI model was evaluated on a test cohort consisting of 566 admissions. Using eye-tracking technology, we investigated medical doctors' reading workflow during manual chart review. Moreover, we performed a clinical use study where medical doctors read two admissions with and without AI assistance to evaluate performance when using and perception of using the AI model. The AI model had a sensitivity of 93.7% and a specificity of 98.1% on the test cohort. In the use studies, we found that medical doctors missed more than 33% of relevant sentences when doing chart review without AI assistance. Hemorrhage events described in paragraphs were more often overlooked compared with bullet-pointed hemorrhage mentions. With AI-assisted chart review, medical doctors identified 48 and 49 percentage points more hemorrhage events than without assistance in two admissions, and they were generally positive toward using the AI model as a supporting tool. Medical doctors identified more hemorrhage events with AI-assisted chart review and they were generally positive toward using the AI model.

Why and how to embrace AI such as ChatGPT in your academic life.

Generative artificial intelligence (AI), including large language models (LLMs), is poised to transform scientific research, enabling researchers to elevate their research productivity. This article presents a how-to guide for employing LLMs in academic settings, focusing on their unique strengths, constraints and implications through the lens of philosophy of science and epistemology. Using ChatGPT as a case study, I identify and elaborate on three attributes contributing to its effectiveness-intelligence, versatility and collaboration-accompanied by tips on crafting effective prompts, practical use cases and a living resource online (https://osf.io/8vpwu/). Next, I evaluate the limitations of generative AI and its implications for ethical use, equality and education. Regarding ethical and responsible use, I argue from technical and epistemic standpoints that there is no need to restrict the scope or nature of AI assistance, provided that its use is transparently disclosed. A pressing challenge, however, lies in detecting fake research, which can be mitigated by embracing open science practices, such as transparent peer review and sharing data, code and materials. Addressing equality, I contend that while generative AI may promote equality for some, it may simultaneously exacerbate disparities for others-an issue with potentially significant yet unclear ramifications as it unfolds. Lastly, I consider the implications for education, advocating for active engagement with LLMs and cultivating students' critical thinking and analytical skills. The how-to guide seeks to empower researchers with the knowledge and resources necessary to effectively harness generative AI while navigating the complex ethical dilemmas intrinsic to its application.

Open Artificial Intelligence Assistants for Educators.

Open Artificial Intelligence Assistants for Educators.

The Role of Recurrent Convolutional Neural Network in IoT for Building a Security Artificial Intelligence and Home Assistance System

The Role of Recurrent Convolutional Neural Network in IoT for Building a Security Artificial Intelligence and Home Assistance System

Intelligence brings responsibility - Even smart AI assistants are held responsible

People will not hold cars responsible for traffic accidents, yet they do when artificial intelligence (AI) is involved. AI systems are held responsible when they act or merely advise a human agent. Does this mean that as soon as AI is involved responsibility follows? To find out, we examined whether purely instrumental AIsystems stay clear of responsibility. We compared AI-powered with non-AI-powered car warning systems and measured their responsibility rating alongside their human users. Our findings show that responsibility is shared whenthe warning system is powered by AI but not by a purely mechanical system, even though people consider both systems as mere tools. Surprisingly, whetherthe warning prevents the accident introduces an outcome bias: the AI takes higher credit than blame depending on what the human manages or fails to do.

Artificial intelligence and perceived effort in relationship maintenance: Effects on relationship satisfaction and uncertainty

Maintaining satisfying close relationships is important for individuals’ well-being. In the digital age, artificial intelligence (AI) has growing applications for relationship maintenance and thus implications for relational well-being. We hypothesize that although using AI to help with relational maintenance may reduce an individual’s effort, their partner may perceive AI-augmented activities negatively. According to the investment model and equity theory, perceptions of diminished effort in a relationship may lead to less satisfaction and greater uncertainty about the partner’s involvement in the relationship. In an online experiment, we presented participants ( N = 208) with hypothetical scenarios of relational maintenance initiated by a fictional close friend, with a 3 (agency: self-without-augmentation vs. AI-augmented vs. human-augmented) × 3 (relational task: support-giving vs. advice-giving vs. birthday celebration) between-subjects design. Compared to the self-without-augmentation condition (i.e., the control condition) where the friend completed a relational task with no external aid, using AI assistance led participants to perceive the friend expended less effort, reducing participants’ relationship satisfaction and increasing uncertainty. Getting help from another person was not significantly different from using AI in terms of perceived partner effort, relationship satisfaction, uncertainty, and perceived appropriateness. We discuss the implications of the findings for relational maintenance and technology-mediated communication.

Three Challenges for AI-Assisted Decision-Making.

Artificial intelligence (AI) has the potential to improve human decision-making by providing decision recommendations and problem-relevant information to assist human decision-makers. However, the full realization of the potential of human-AI collaboration continues to face several challenges. First, the conditions that support complementarity (i.e., situations in which the performance of a human with AI assistance exceeds the performance of an unassisted human or the AI in isolation) must be understood. This task requires humans to be able to recognize situations in which the AI should be leveraged and to develop new AI systems that can learn to complement the human decision-maker. Second, human mental models of the AI, which contain both expectations of the AI and reliance strategies, must be accurately assessed. Third, the effects of different design choices for human-AI interaction must be understood, including both the timing of AI assistance and the amount of model information that should be presented to the human decision-maker to avoid cognitive overload and ineffective reliance strategies. In response to each of these three challenges, we present an interdisciplinary perspective based on recent empirical and theoretical findings and discuss new research directions.

Is automatic cephalometric software using artificial intelligence better than orthodontist experts in landmark identification?

BackgroundTo evaluate the techniques used for the automatic digitization of cephalograms using artificial intelligence algorithms, highlighting the strengths and weaknesses of each one and reviewing the percentage of success in localizing each cephalometric point.MethodsLateral cephalograms were digitized and traced by three calibrated senior orthodontic residents with or without artificial intelligence (AI) assistance. The same radiographs of 43 patients were uploaded to AI-based machine learning programs MyOrthoX, Angelalign, and Digident. Image J was used to extract x- and y-coordinates for 32 cephalometric points: 11 soft tissue landmarks and 21 hard tissue landmarks. The mean radical errors (MRE) were assessed radical to the threshold of 1.0 mm,1.5 mm, and 2 mm to compare the successful detection rate (SDR). One-way ANOVA analysis at a significance level of P < .05 was used to compare MRE and SDR. The SPSS (IBM-vs. 27.0) and PRISM (GraphPad-vs.8.0.2) software were used for the data analysis.ResultsExperimental results showed that three methods were able to achieve detection rates greater than 85% using the 2 mm precision threshold, which is the acceptable range in clinical practice. The Angelalign group even achieved a detection rate greater than 78.08% using the 1.0 mm threshold. A marked difference in time was found between the AI-assisted group and the manual group due to heterogeneity in the performance of techniques to detect the same landmark.ConclusionsAI assistance may increase efficiency without compromising accuracy with cephalometric tracings in routine clinical practice and research settings.

Artificial intelligence and advanced driver assistance systems absorption (ADAS) in Mexico

The driver assistance systems ADAS, are revolutionizing the way in which man and vehicle interact, while the car develops the work that the driver can lose by distraction. Also, the roads are an important point for the driver to have problems when driving, and the difficulties that these new technologies present with society seek to provide security and not be perceived as obstacles when companies implement such technology in their vehicles. The objective of this work is to conduct a comprehensive literature review to determine the degree of absorption of both artificial intelligence and ADAS systems in motor vehicles in Mexico. This study shows how artificial intelligence assists man in his motor functions so that driving is a human-machine complement, strengthening the bond as an assistant.

Artificial Intelligence Assisted Fabrication of 3D, 4D and 5D Printed Formulations or Devices for Drug Delivery.

5D & 4D printings are an advanced version of 3D printing class and are one of the most revolutionary and powerful fabrication methods used for preparing innovative structures and solid substances using precise additive manufacturing technology. It captures the imagination of one with its potential to produce flexible designing and fabrication of innovative products with high complexity and speed. This technology with the assistance of AI (Artificial Intelligence) facilitates real-time sensing, adapting to change, and predicting the state of printing. 3D printing works by employing advanced materials utilizing a computer aided design with tomography scan under AI control which deposits printing material in accordance with the nature of a file usually in STL format, but it requires time for printing. This shortcoming can be overcome by 4D printing where smart materials are incorporated with time as 4th dimension. This technique has self-repair and self-assembly properties that will save around 80% of time. Some printed materials are made sensitive to temperature, humidity, light, and other parameters so that they can respond to stimulus, but it's one limitation of not being able to print complex shapes having curved surfaces can be overcome by utilising 5D printing where additive manufacturing is done by rotation of extruder head and rotation of print bed to print in 5 different axes. This review evaluates the prospective of these techniques with AI interference in medicine and pharmacy, with its effective and efficient production for the required design precision.

New possibilities for medical support systems utilizing artificial intelligence (AI) and data platforms.

In Japan, there is a growing initiative to construct centralized databases and platforms that can aggregate and manage a vast range of medical, health, and caregiving data for research and analysis. Recent advancements in artificial intelligence (AI), particularly in general-purpose models like the Segment Anything model and Chat GPT, promise significant progress towards utilizing such data-rich platforms effectively for healthcare. Traditionally, AI has displayed superior performance by learning specific images or languages, but now it is advancing towards creating models capable of learning universal traits from images and languages by training on extensive datasets. The challenge lies in the fact that these general-purpose models are trained on data that does not sufficiently incorporate medical information, making their direct application to healthcare difficult. However, the introduction of data platforms can potentially solve this problem. This would lead to the development of universally applicable models to process medical images and AI assistants that can support both doctors and patients. These medical AI assistants can function as a "sub-doctor" with extensive knowledge, assisting in comprehensive analysis of symptoms, early detection of rare diseases, and more. They can also serve as an intermediary between the doctor and the patient, helping to simplify consultations and enhance patient understanding of medical conditions and treatments. By bridging this gap, the AI assistant can help to reduce doctors' workload, improve the quality of healthcare, and facilitate early detection and prevention in the elderly population.

Impact of Artificial Intelligence–Assisted Indication Selection on Appropriateness Order Scoring for Imaging Clinical Decision Support

PurposeThe aim of this study was to assess appropriateness scoring and structured order entry after the implementation of an artificial intelligence (AI) tool for analysis of free-text indications. MethodsAdvanced outpatient imaging orders in a multicenter health care system were recorded 7 months before (March 1, 2020, to September 21, 2020) and after (October 20, 2020, to May 13, 2021) the implementation of an AI tool targeting free-text indications. Clinical decision support score (not appropriate, may be appropriate, appropriate, or unscored) and indication type (structured, free-text, both, or none) were assessed. The χ2 and multivariate logistic regression adjusting for covariables with bootstrapping were used. ResultsIn total, 115,079 orders before and 150,950 orders after AI tool deployment were analyzed. The mean patient age was 59.3 ± 15.5 years, and 146,035 (54.9%) were women; 49.9% of orders were for CT, 38.8% for MR, 5.9% for nuclear medicine, and 5.4% for PET. After deployment, scored orders increased to 52% from 30% (P < .001). Orders with structured indications increased to 67.3% from 34.6% (P < .001). On multivariate analysis, orders were more likely to be scored after tool deployment (odds ratio [OR], 2.7, 95% CI, 2.63-2.78; P < .001). Compared with physicians, orders placed by nonphysician providers were less likely to be scored (OR, 0.80; 95% CI, 0.78-0.83; P < .001). MR (OR, 0.84; 95% CI, 0.82-0.87) and PET (OR, 0.12; 95% CI, 0.10-0.13) were less likely to be scored than CT (; P < .001). After AI tool deployment, 72,083 orders (47.8%) remained unscored, 45,186 (62.7%) with free-text-only indications. ConclusionsEmbedding AI assistance within imaging clinical decision support was associated with increased structured indication orders and independently predicted a higher likelihood of scored orders. However, 48% of orders remained unscored, driven by both provider behavior and infrastructure-related barriers.