Intelligence in humans

In this cross-sectional study, we evaluated the completeness, readability, and syntactic complexity of cardiovascular disease prevention information produced by GPT-4 in response to 4 kinds of prompts.

The initial enthusiasm about computational pathology (CP) and artificial intelligence (AI) was that they will replace pathologists entirely on the way to fully automated diagnostics. It is becoming clear that currently this is not the immediate model to pursue. On top of the legal and regulatory complexities surrounding its implementation, the majority of tested machine learning (ML)-based predictive algorithms do not display the exquisite performance needed to render them unequivocal, standalone decision makers for matters with direct implications to human health...

The field of theranostics is rapidly advancing, driven by the goals of enhancing patient care. Recent breakthroughs in artificial intelligence (AI) and its innovative theranostic applications have marked a critical step forward in nuclear medicine, leading to a significant paradigm shift in precision oncology. For instance, AI-assisted tumor characterization, including automated image interpretation, tumor segmentation, feature identification, and prediction of high-risk lesions, improves diagnostic processes, offering a precise and detailed evaluation...

Organoid Intelligence ushers in a new era by seamlessly integrating cutting-edge organoid technology with the power of artificial intelligence. Organoids, three-dimensional miniature organ-like structures cultivated from stem cells, offer an unparalleled opportunity to simulate complex human organ systems in vitro. Through the convergence of organoid technology and AI, researchers gain the means to accelerate discoveries and insights across various disciplines. Artificial intelligence algorithms enable the comprehensive analysis of intricate organoid behaviors, intricate cellular interactions, and dynamic responses to stimuli...

Enhanced recovery after surgery (ERAS) protocols have transformed perioperative care by implementing evidence-based strategies to hasten patient recovery, decrease complications, and shorten hospital stays. However, challenges such as inconsistent adherence and the need for personalized adjustments persist, prompting exploration into innovative solutions. The emergence of artificial intelligence (AI) and machine learning (ML) offers a promising avenue for optimizing ERAS protocols. While ERAS emphasizes preoperative optimization, minimally invasive surgery (MIS), and standardized postoperative care, challenges such as adherence variability and resource constraints impede its effectiveness...

The development of non-invasive methods and accessible tools for application to plant phenotyping is considered a breakthrough. This work presents the preliminary results using an electronic nose (E-Nose) and machine learning (ML) as affordable tools. An E-Nose is an electronic system used for smell global analysis, which emulates the human nose structure. The soybean (Glycine Max) was used to conduct this experiment under water stress. Commercial E-Nose was used, and a chamber was designed and built to conduct the measurement of the gas sample from the soybean...

INTRODUCTION: Service robot technology is increasingly gaining prominence in the field of artificial intelligence. However, persistent limitations continue to impede its widespread implementation. In this regard, human motion pose estimation emerges as a crucial challenge necessary for enhancing the perceptual and decision-making capacities of service robots. METHOD: This paper introduces a groundbreaking model, YOLOv8-ApexNet, which integrates advanced technologies, including Bidirectional Routing Attention (BRA) and Generalized Feature Pyramid Network (GFPN)...

Due to the heavy burden on human drivers when remotely controlling hexapod robots in complex terrain environments, there is a critical need for robot intelligence to assist in generating control commands. Therefore, this study proposes a mapping process framework that generates a combination of human-robot commands based on decision target values, focusing on the task of robot intelligence assisting drivers in generating human-robot command combinations. Furthermore, human-robot state constraints are quantified as geometric constraints on robot motion and driver fatigue constraints...

With the rapid development of international communication, the number of English courses has shown an explosive growth trend, which has caused a serious problem of information overload, resulting in poor teaching performance of recommended English courses. To solve this problem, this paper proposes a graph convolutional neural network model based on College English course texts, students' major, English foundation and network structure characteristics. First, by analyzing the relevant data of College English courses and combining with graph neural network, an English course recommendation algorithm model based on the College English learning strategy of proximity comparison is proposed...

Conventional diagnostic methods for lung cancer, based on breath analysis using gas chromatography and mass spectrometry, have limitations for fast screening due to their limited availability, operational complexity, and high cost. As potential replacement, among several low-cost and portable methods, chemoresistive sensors for the detection of volatile organic compounds (VOCs) that represent biomarkers of lung cancer were explored as promising solutions, which unfortunately still face challenges. To address the key problems of these sensors, such as low sensitivity, high response time, and poor selectivity, this study presents the design of new chemoresistive sensors based on hybridised porous zeolitic imidazolate (ZIF-8) based metal-organic frameworks (MOFs) and laser-scribed graphene (LSG) structures, inspired by the architecture of the human lung...

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...

Breathing is an important physiological activity of human body, which not only reflects the state of human movement, but also is one of the important health indicators. Breathing can change the concentration of water molecules, so monitoring humidity has gradually become a hot topic in modern research. In this study, a humidity sensing composite film with high sensitivity and short response time was made by using the mixture of graphene oxide (GO) and bacterial cellulose (BC) with simple dry film-forming method...

The current challenge in effectively treating atrial fibrillation (AF) stems from a limited understanding of the intricate structure of the human atria. The objective and quantitative interpretation of the right atrium (RA) in late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) scans relies heavily on its precise segmentation. Leveraging the potential of artificial intelligence (AI) for RA segmentation presents a promising solution. However, the successful implementation of AI in this context necessitates access to a substantial volume of annotated LGE-MRI images for model training...

Microplastics (MPs) pollution is an emerging environmental health concern, impacting soil, plants, animals, and humans through their entry into the food chain via bioaccumulation. Human activities such as improper solid waste dumping are significant sources that ultimately transport MPs into the water bodies of the coastal areas. Moreover, there is a complex interplay between the coastal climate dynamics, environmental factors, the burgeoning issue of MPs pollution and the complex web of coastal pollution. We embark on a comprehensive journey, synthesizing the latest research across multiple disciplines to provide a holistic understanding of how these inter-connected factors shape and reshape the coastal ecosystems...

The use of artificial intelligence (AI) solutions is rapidly changing the way radiation therapy tasks, traditionally relying on human skills, are approached by enabling fast automation. This evolution represents a paradigm shift in all aspects of the profession, particularly for treatment planning applications, opening up opportunities but also causing concerns for the future of the multidisciplinary team. In Australia, radiation therapists (RTs), largely responsible for both treatment planning and delivery, are discussing the impact of the introduction of AI and the potential developments in the future of their role...

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OBJECTIVES: ChatGPT is an innovative artificial intelligence designed to enhance human activities and serve as a potent tool for information retrieval. This study aimed to evaluate the performance and limitation of ChatGPT on fourth-year pharmacy student examination. METHODS: This cross-sectional study was conducted on February 2023 at the Faculty of Pharmacy, Chiang Mai University, Thailand. The exam contained 16 multiple-choice questions and 2 short-answer questions, focusing on classification and medical management of shock and electrolyte disorders...

INTRODUCTION: Semi-quantitative scoring of various parameters in renal biopsy is accepted as an important tool to assess disease activity and prognostication. There are concerns on the impact of inter-observer variability in its prognostic utility, generating a need for computerized quantification. METHODS: We studied 94 patients with renal biopsies, 45 with native diseases and 49 transplant patients with index biopsies for Polyomavirus nephropathy. Chronicity scores were evaluated using two methods...

PURPOSE: This review provides an overview of the current state of artificial intelligence (AI) technology for automated detection of breast cancer in digital mammography (DM) and digital breast tomosynthesis (DBT). It aims to discuss the technology, available AI systems, and the challenges faced by AI in breast cancer screening. METHODS: The review examines the development of AI technology in breast cancer detection, focusing on deep learning (DL) techniques and their differences from traditional computer-aided detection (CAD) systems...

The detection of tetracycline antibiotics (TCs) in food holds great significance in minimizing their absorption within the human body. Hence, this study aims to develop a rapid, convenient, real-time, and accurate detection method for detecting antibiotics in an authentic market setting. A colorimetric fluorescence sensor was devised for tetracycline detection utilizing PVA aerogels as the substrate. Its operating principle is based on the IFE effect and antenna effect. A detection device is designed to capture fluorescence images while deep learning was employed to aid in the detection process...