Intelligent Software Research Articles

Will Precision Medicine Meet Digital Health? A Systematic Review of Pharmacogenomics Clinical Decision Support Systems Used in Clinical Practice.

Digital health, an emerging scientific domain, attracts increasing attention as artificial intelligence and relevant software proliferate. Pharmacogenomics (PGx) is a core component of precision/personalized medicine driven by the overarching motto "the right drug, for the right patient, at the right dose, and the right time." PGx takes into consideration patients' genomic variations influencing drug efficacy and side effects. Despite its potentials for individually tailored therapeutics and improved clinical outcomes, adoption of PGx in clinical practice remains slow. We suggest that e-health tools such as clinical decision support systems (CDSSs) can help accelerate the PGx, precision/personalized medicine, and digital health emergence in everyday clinical practice worldwide. Herein, we present a systematic review that examines and maps the PGx-CDSSs used in clinical practice, including their salient features in both technical and clinical dimensions. Using Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines and research of the literature, 29 relevant journal articles were included in total, and 19 PGx-CDSSs were identified. In addition, we observed 10 technical components developed mostly as part of research initiatives, 7 of which could potentially facilitate future PGx-CDSSs implementation worldwide. Most of these initiatives are deployed in the United States, indicating a noticeable lack of, and the veritable need for, similar efforts globally, including Europe.

Intelligent Hardware and Software Support and Increasing the Efficiency of Welding Processes

Intelligent Hardware and Software Support and Increasing the Efficiency of Welding Processes

Image quality and detection efficacy of zero echo time magnetic resonance imaging on Lung-RADS 2 pulmonary ground-glass nodules in comparison to thin-slice fat-saturated T2-weighted imaging.

Widely used computed tomography (CT) screening increases the detection of pulmonary pure ground-glass nodules (pGGNs), often classified as the second category of Lung Imaging Reporting and Data System (Lung-RADS 2). Despite their low malignancy risk, these nodules pose significant challenges and necessitate accurate assessment to minimize the risk of long-term follow-ups. This study investigated the detection efficacy of zero echo time (ZTE) magnetic resonance imaging (MRI) and thin-slice fat-saturated T2-weighted imaging (T2WI-FS) on 3.0 T MRI on the predictive accuracy of invasiveness for Lung-RADS 2 pGGNs. This prospective study enrolled 83 consecutive patients with 110 pGGNs who underwent preoperative CT and MRI scans. All CT images were assessed by artificial intelligence (AI) software and confirmed by a thoracic radiologist. Another two radiologists blind to pathology results assessed MRI for image quality (objective and subjective evaluations) and detection of pGGNs. Differences in nodule diameter, CT density and detection rate were compared within different pathological groups. The objective and subjective image quality scores were compared using the Wilcoxon signed rank test between ZTE and T2WI-FS. Interobserver agreement was calculated using the kappa coefficient. Receiver operating characteristic (ROC) curve analysis evaluated the diagnostic accuracy for distinguishing invasiveness. Among the 110 pGGNs evaluated, T2WI-FS demonstrated a higher detection rate (80.0%) compared to ZTE (51.8%). ZTE showed a superior signal-to-noise ratio (SNR) in the lung parenchyma, aorta, and peripheral lung structures, whereas T2WI-FS more effectively delineated tracheal walls and pulmonary nodules. Both observers rated ZTE higher for vascular and bronchial visibility, while T2WI-FS was better in terms of lower noise and fewer artifacts. Notably, ZTE visibility varied with pathological results, exhibiting a range from 0% in atypical adenomatous hyperplasia (AAH) to 94.1% in invasive adenocarcinoma (IAC). The key indicators for distinguishing invasive pGGNs from non-invasive ones were nodule diameter [area under the curve (AUC) =0.874], ZTE visibility (AUC =0.740), followed by CT values (AUC =0.682) and T2WI-FS visibility (AUC =0.678). MRI has the potential to detect and predict the invasiveness of pGGN. Both T2WI-FS and ZTE demonstrate reliable image quality in pulmonary imaging, each displaying strengths in visualizing pGGN. Thin-slice T2WI-FS has a superior detection rate, while ZTE better predicts histological invasiveness.

Evaluating coronary arteries and predicting MACEs using CCTA in lung cancer patients receiving chemotherapy or chemoradiotherapy

BackgroundWhether coronary computed-tomography angiography (CCTA) can detect cancer treatment-related impairments of coronary artery and predict major adverse cardiovascular events (MACEs) in lung cancer patients receiving chemotherapy (CHT) or chemoradiotherapy (CRT) is unclear. ObjectivesThis study aimed to evaluate coronary arteries using CCTA parameters and explore the association of these parameters with MACEs in patients with lung cancer receiving CHT or CRT. Materials and methodsThis study retrospectively collected data from 697 lung cancer patients who received CHT or CRT and underwent CCTA examination within 2 weeks before or after treatment from June 2013 to May 2019. The patients were divided into CHT and CRT group, and for the control group, the propensity score matching (PSM) was used and 125 participants without carcinoma with a single CCTA examination were included. CCTA parameters, assessed using artificial intelligence software, were compared across different groups (control vs. CHT & CRT; CHT vs. CRT). We analyzed associations between CCTA parameters and MACEs using a Cox-regression model and Kaplan–Meier curves to compare MACE-free survival rates. ResultsBefore CHT or CRT, compared with the control group, in CHT&CRT group we observed higher fat attenuation index (FAI), coronary-artery calcium (CAC) score, CAD-RADS classification, stenosis severity and lower computed-tomography fractional flow reserve (CT-FFR; all P<0.05). After treatment, the CT-FFR decreased and the FAI increased; simultaneously, we observed a lower CT-FFR and higher FAI (all P<0.05) in the CRT than in the CHT group. Among the 146 cases developed MACEs, lower CT-FFR and higher FAI values were found compared with the non-MACE group (all P<0.05), and CT-FFR and FAI before treatment were associated with MACEs. ConclusionCancer treatment-related impairments of coronary arteries could be identified using CT-FFR and FAI. Before treatment, these parameters were associated with MACEs in lung cancer patients receiving CHT or CRT.

Discrepancies in ASPECTS obtained by artificial intelligence and experts: Associated factors and prognostic implications

PurposeThe differences between the Alberta Stroke Program Early CT Score (ASPECTS) obtained by experts and artificial intelligence (AI) software require elucidation. We aimed to characterize the discrepancies between the ASPECTS obtained by AI and experts and determine the associated factors and prognostic implications. MethodsThis multicenter, retrospective, observational cohort study included patients showing acute ischemic stroke caused by large-vessel occlusion in the anterior circulation. ASPECTS was determined by AI software (RAPID ASPECTS) and experts from the core laboratory. Interclass correlation coefficients (ICCs) and Bland–Altman plots were used to illustrate the consistency and discrepancies; logistic regression analyses were used to assess the correlates of inconsistency; and receiver operating characteristic analyses were performed to assess the diagnostic performance for predicting unfavorable clinical outcomes. ResultsThe study population included 491 patients. The ICC for the expert and AI ASPECTS was 0.63 (95 % confidence interval [CI]: 0.25–0.79).The mean difference between expert and AI ASPECTS was 2.24. Chronic infarcts (odds ratio [OR], 1.9; 95 % CI, 1.1–3.4; P=0.021) and expert scores in the internal capsule (OR, 2.9; 95 % CI, 1.1–7.7; P=0.034) and lentiform (OR, 2.4; 95 % CI, 1.3–4.7; P=0.008) were significant correlates of inconsistency. The ASPECTS obtained by AI showed a significantly higher area under the curve for unfavorable outcomes (0.68 vs. 0.63, P=0.04). ConclusionsIn comparison with expert ASPECTS, AI ASPECTS overestimated the infarct extent. Future studies should aim to determine whether AI ASPECTS assessments should use a lower threshold to screen patients for endovascular therapy.

Artificial intelligence software for assessing brain ischemic penumbra/core infarction on computed tomography perfusion: A real-world accuracy study.

With the increasingly extensive application of artificial intelligence (AI) in medical systems, the accuracy of AI in medical diagnosis in the real world deserves attention and objective evaluation. To investigate the accuracy of AI diagnostic software (Shukun) in assessing ischemic penumbra/core infarction in acute ischemic stroke patients due to large vessel occlusion. From November 2021 to March 2022, consecutive acute stroke patients with large vessel occlusion who underwent mechanical thrombectomy (MT) post-Shukun AI penumbra assessment were included. Computed tomography angiography (CTA) and perfusion exams were analyzed by AI, reviewed by senior neurointerventional experts. In the case of divergences among the three experts, discussions were held to reach a final conclusion. When the results of AI were inconsistent with the neurointerventional experts' diagnosis, the diagnosis by AI was considered inaccurate. A total of 22 patients were included in the study. The vascular recanalization rate was 90.9%, and 63.6% of patients had modified Rankin scale scores of 0-2 at the 3-month follow-up. The computed tomography (CT) perfusion diagnosis by Shukun (AI) was confirmed to be invalid in 3 patients (inaccuracy rate: 13.6%). AI (Shukun) has limits in assessing ischemic penumbra. Integrating clinical and imaging data (CT, CTA, and even magnetic resonance imaging) is crucial for MT decision-making.

Recent advances in artificial intelligent strategies for tissue engineering and regenerative medicine.

Tissue engineering and regenerative medicine (TERM) aim to repair or replace damaged or lost tissues or organs due to accidents, diseases, or aging, by applying different sciences. For this purpose, an essential part of TERM is the designing, manufacturing, and evaluating of scaffolds, cells, tissues, and organs. Artificial intelligence (AI) or the intelligence of machines or software can be effective in all areas where computers play a role. The "artificial intelligence," "machine learning," "tissue engineering," "clinical evaluation," and "scaffold" keywords used for searching in various databases and articles published from 2000 to 2024 were evaluated. The combination of tissue engineering and AI has created a new generation of technological advancement in the biomedical industry. Experience in TERM has been refined using advanced design and manufacturing techniques. Advances in AI, particularly deep learning, offer an opportunity to improve scientific understanding and clinical outcomes in TERM. The findings of this research show the high potential of AI, machine learning, and robots in the selection, design, and fabrication of scaffolds, cells, tissues, or organs, and their analysis, characterization, and evaluation after their implantation. AI can be a tool to accelerate the introduction of tissue engineering products to the bedside. The capabilities of artificial intelligence (AI) can be used in different ways in all the different stages of TERM and not only solve the existing limitations, but also accelerate the processes, increase efficiency and precision, reduce costs, and complications after transplantation. ML predicts which technologies have the most efficient and easiest path to enter the market and clinic. The use of AI along with these imaging techniques can lead to the improvement of diagnostic information, the reduction of operator errors when reading images, and the improvement of image analysis (such as classification, localization, regression, and segmentation).

Decision-making solutions based artificial intelligence and hybrid software for optimal sizing and energy management in a smart grid system

This paper describes a decentralised smart grid system containing renewable energies, storage systems and distributed generation with human control and intervention. The importance of each element and the interaction between them leads to think about a decision-making strategy. In fact, the integration of a Photovoltaic Panel (PVP) is used due to its availability and its participation in the carbon emissions reduction. Also, a battery is required to fill a power gap or absorb extra generated energy. Moreover, an optimal sizing is needed to get an efficient system with minimum cost. Also, an energy management strategy (EMS) is essential to ensure the power resources scheduling in order to keep a continuous equilibrium supply-demand of electricity and avoid instabilities in the grid, with guaranteeing a minimum cost of electricity. In the first part, the proposed smart grid optimal sizing is determined under real weather data (solar radiation) of the city of Sousse, Tunisia, using the Hybrid Optimization of Multiple Energy Resources (HOMER) software technique. This approach is chosen thanks to its simplicity, effectiveness, and high precision compared to traditional techniques. In this paper, several configurations (Grid, (Grid-battery), (Grid-PVP), (Grid-PVP-battery)) are studied. The obtained results prove that the (Grid-PVP-battery) system configuration is the most efficient and economical solution. In the second part, a robust energy management strategy (EMS) is proposed for two smart grid configurations (grid-battery, grid-PVP-battery). This strategy is based on Fuzzy Logic Control (FLC) thanks to its non-linear modelling and its ability to make decisions relating to energy management. The primary goal of the suggested (EMS) is to ensure the energy resources scheduling in order to keep a continuous equilibrium among the production and consumption of electricity and avoid instabilities in the grid, with guaranteeing a minimum cost of electricity. As input data, (FLC) used time-varying price electricity (Price (t)) to solve an instant decision problem by choosing, at each instant, the optimal energy source (which provide electricity at the cheapest price possible). The obtained results, carrying out Matlab simulation, prove the efficacy of the proposed strategy, not only, in the energy resources scheduling to meet the load, but also, for the system cost reduction since the PVP has been used as much as possible since it is inexpensive relative to the costs of battery capacity and the grid.

Diagnosing Sarcopenia with AI-Aided Ultrasound (DINOSAUR)-A Pilot Study.

Background: Sarcopenia has been recognized as a determining factor in surgical outcomes and is associated with an increased risk of postoperative complications and readmission. Diagnosis is currently based on clinical guidelines, which includes assessment of skeletal muscle mass but not quality. Ultrasound has been proposed as a useful point-of-care diagnostic tool to assess muscle quality, but no validated cut-offs for sarcopenia have been reported. Using novel automated artificial intelligence (AI) software to interpret ultrasound images may assist in mitigating the operator-dependent nature of the modality. Our study aims to evaluate the fidelity of AI-aided ultrasound as a reliable and reproducible modality to assess muscle quality and diagnose sarcopenia in surgical patients. Methods: Thirty-six adult participants from an outpatient clinic were recruited for this prospective cohort study. Sarcopenia was diagnosed according to Asian Working Group for Sarcopenia (AWGS) 2019 guidelines. Ultrasonography of the rectus femoris muscle was performed, and images were analyzed by an AI software (MuscleSound® (Version 5.69.0)) to derive muscle parameters including intramuscular adipose tissue (IMAT) as a proxy of muscle quality. A receiver operative characteristic (ROC) curve was used to assess the predictive capability of IMAT and its derivatives, with area under the curve (AUC) as a measure of overall diagnostic accuracy. To evaluate consistency between ultrasound users of different experience, intra- and inter-rater reliability of muscle ultrasound parameters was analyzed in a separate cohort using intraclass correlation coefficients (ICC) and Bland-Altman plots. Results: The median age was 69.5 years (range: 26-87), and the prevalence of sarcopenia in the cohort was 30.6%. The ROC curve plotted with IMAT index (IMAT% divided by muscle area) yielded an AUC of 0.727 (95% CI: 0.551-0.904). An optimal cut-off point of 4.827%/cm2 for IMAT index was determined with a Youden's Index of 0.498. We also demonstrated that IMAT index has excellent intra-rater reliability (ICC = 0.938, CI: 0.905-0.961) and good inter-rater reliability (ICC = 0.776, CI: 0.627-0.866). In Bland-Altman plots, the limits of agreement were from -1.489 to 1.566 and -2.107 to 4.562, respectively. Discussion: IMAT index obtained via ultrasound has the potential to act as a point-of-care evaluation for sarcopenia screening and diagnosis, with good intra- and inter-rater reliability. The proposed IMAT index cut-off maximizes sensitivity for case finding, supporting its use as an easily implementable point-of-care test in the community for sarcopenia screening. Further research incorporating other ultrasound parameters of muscle quality may provide the basis for a more robust diagnostic tool to help predict surgical risk and outcomes.

Integrating ChatGPT in Orthopedic Education for Medical Undergraduates: Randomized Controlled Trial.

ChatGPT is a natural language processing model developed by OpenAI, which can be iteratively updated and optimized to accommodate the changing and complex requirements of human verbal communication. The study aimed to evaluate ChatGPT's accuracy in answering orthopedics-related multiple-choice questions (MCQs) and assess its short-term effects as a learning aid through a randomized controlled trial. In addition, long-term effects on student performance in other subjects were measured using final examination results. We first evaluated ChatGPT's accuracy in answering MCQs pertaining to orthopedics across various question formats. Then, 129 undergraduate medical students participated in a randomized controlled study in which the ChatGPT group used ChatGPT as a learning tool, while the control group was prohibited from using artificial intelligence software to support learning. Following a 2-week intervention, the 2 groups' understanding of orthopedics was assessed by an orthopedics test, and variations in the 2 groups' performance in other disciplines were noted through a follow-up at the end of the semester. ChatGPT-4.0 answered 1051 orthopedics-related MCQs with a 70.60% (742/1051) accuracy rate, including 71.8% (237/330) accuracy for A1 MCQs, 73.7% (330/448) accuracy for A2 MCQs, 70.2% (92/131) accuracy for A3/4 MCQs, and 58.5% (83/142) accuracy for case analysis MCQs. As of April 7, 2023, a total of 129 individuals participated in the experiment. However, 19 individuals withdrew from the experiment at various phases; thus, as of July 1, 2023, a total of 110 individuals accomplished the trial and completed all follow-up work. After we intervened in the learning style of the students in the short term, the ChatGPT group answered more questions correctly than the control group (ChatGPT group: mean 141.20, SD 26.68; control group: mean 130.80, SD 25.56; P=.04) in the orthopedics test, particularly on A1 (ChatGPT group: mean 46.57, SD 8.52; control group: mean 42.18, SD 9.43; P=.01), A2 (ChatGPT group: mean 60.59, SD 10.58; control group: mean 56.66, SD 9.91; P=.047), and A3/4 MCQs (ChatGPT group: mean 19.57, SD 5.48; control group: mean 16.46, SD 4.58; P=.002). At the end of the semester, we found that the ChatGPT group performed better on final examinations in surgery (ChatGPT group: mean 76.54, SD 9.79; control group: mean 72.54, SD 8.11; P=.02) and obstetrics and gynecology (ChatGPT group: mean 75.98, SD 8.94; control group: mean 72.54, SD 8.66; P=.04) than the control group. ChatGPT answers orthopedics-related MCQs accurately, and students using it excel in both short-term and long-term assessments. Our findings strongly support ChatGPT's integration into medical education, enhancing contemporary instructional methods. Chinese Clinical Trial Registry Chictr2300071774; https://www.chictr.org.cn/hvshowproject.html ?id=225740&v=1.0.

Introducing a novel approach to dental color reproduction using AI technology.

This article aims to describe a systematic method for tooth color reproduction with ceramics restorations employing artificial intelligence (AI) software named Matisse. It provides a comprehensive analysis of the entire process, beginning with shade-taking and extending to ceramic application in a complex clinical case in the anterior region-specifically, a single central restoration supported by an implant. The clinical case presented highlights the potential of Matisse software for generating ceramic (inSync-Jensen Dental, USA) and staining (Miyo-Jensen Dental, USA) recipes over a zirconia abutment (Katana-Noritake Dental, Japan). This approach achieves an optimal single central restoration utilizing CAD-CAM and layering techniques. The systematic method employing the Matisse software achieved accurate color reproduction for a single central restoration supported by an implant. This result was achieved by the dental ceramist within the first attempt and without seeing the patient in the entire process. The Matisse AI-assisted protocol offers a systematic and scientifically grounded method for color reproduction in dentistry for indirect restorations.

Optimizing mining projects: how business intelligence enhances data-driven decisions

The project management sector grapples with the challenge of handling a vast amount of information. Data compilation is essential to support effective decision-making processes by project managers. This paper emphasizes the significance of both project planning and execution monitoring. It concurrently explores the applicability of Business Intelligence (BI) software and its contributions to enhancing project management. The methodology employed a literature review combined with the authors' expertise in engineering projects. The findings reveal that BI offers substantial benefits. These include reducing the time required to transform large datasets into clear and concise graphical information that can be readily presented and dynamically adjusted to meet specific user needs. A study conducted in a mining company demonstrated a significant reduction in project management activities. By utilizing Power BI software, they achieved a 50% reduction in total time dedicated to project management activities. Additionally, there was a remarkable 83% reduction in the time spent on report issuance and updates. This outcome enables that project team members are aligned with consolidated information, facilitating real-time decision-making and fostering greater integration and productivity.

Innovative advances for neglected tropical disease (NTD): A global perspective from intellectual property

This study examines innovative advances related to neglected tropical diseases (NTDs), which have severe health, social, and economic consequences that affect more than one billion people worldwide. It addresses the 2030 Agenda for the Sustainable Development Goals in tackling some of the “Grand Challenges,” such as dengue fever (DF), one of the primary and re-emerging diseases that have advanced worldwide in recent years. One of the challenges is the requirement that high investments in innovation can be rationalized by the potential returns sustained by the intellectual property protection system. This study answers the question: “How have dengue-related innovations been approached globally from an intellectual property perspective?” Seeking to shed light on innovative advances in NTD, this exploratory study adopted a (i) longitudinal approach, assessing the global impact of DF, and (ii) historical analysis from the intellectual property perspective, seeking the effectiveness of commercially viable solutions against the disease. An extensive search was conducted in international patent databases using Questel® Orbit Intelligence software to address the proposed objective. This study selected 2307 invention patent families (FamPat) from 1993 to 2023, of which nearly half have expired or been revoked. China is the largest jurisdiction, with 415 FamPat filings. However, many regions with a high global burden of DF lack intellectual protection for DF-related innovations. Although the number of Fampat is relatively high for DF, and the interaction between universities, government, and private companies shows the potential development of an innovation ecosystem, few technological solutions are available. This study concludes that the capacity to absorb knowledge influences the effectiveness of making a technology available as a commercial product.

Game-changing intelligence: Unveiling the societal impact of artificial intelligence in game software

Efficient artificial intelligence (AI) in gaming software optimizes development, improves user experiences, and increases industrial production, all of which contribute positively to economic development and technical advancement. Possible drawbacks include employment displacement, ethical problems, algorithmic biases, and excessive reliance on AI; everyone has an impact on society’s dynamics and principles. In this study, we proposed a novel method called Fine-Tuned Ring Toss Game Optimization Adaptive Artificial Neural Network (FRTGO-AANN) to improve user experience in game software, examine social implications to promote ethical growth, explore AI transformational significance in games, and shape new game-play. In this research, we employ a dataset of 250 video game projects. The collected data are undergoing feature extraction by using a Principal Component Analysis (PCA) method. We used a few parameters for our suggested and current approaches to analyze the research’s findings. Our proposed FRTGO-AANN method achieves superior results like accuracy (96.7 %), precision (92.5 %), F1-score (95.3 %), recall (90.6 %).AI in gaming software represents a game changer, transforming user experience, narrative intricacy, along with the world of virtual reality. It has a profound social influence, altering entertainment and promoting innovation, along with difficult ethical considerations.

Expanding molecular diagnostic coverage for tuberculosis by combining computer-aided chest radiography and sputum specimen pooling: a modeling study from four high-burden countries

BackgroundIn 2022, fewer than half of persons with tuberculosis (TB) had access to molecular diagnostic tests for TB due to their high costs. Studies have found that the use of artificial intelligence (AI) software for chest X-ray (CXR) interpretation and sputum specimen pooling can each reduce the cost of testing. We modeled the combination of both strategies to estimate potential savings in consumables that could be used to expand access to molecular diagnostics.MethodsWe obtained Xpert testing and positivity data segmented into deciles by AI probability scores for TB from the community- and healthcare facility-based active case finding conducted in Bangladesh, Nigeria, Viet Nam, and Zambia. AI scores in the model were based on CAD4TB version 7 (Zambia) and qXR (all other countries). We modeled four ordinal screening and testing approaches involving AI-aided CXR interpretation to indicate individual and pooled testing. Setting a false negative rate of 5%, for each approach we calculated additional and cumulative savings over the baseline of universal Xpert testing, as well as the theoretical expansion in diagnostic coverage.ResultsIn each country, the optimal screening and testing approach was to use AI to rule out testing in deciles with low AI scores and to guide pooled vs individual testing in persons with moderate and high AI scores, respectively. This approach yielded cumulative savings in Xpert tests over baseline ranging from 50.8% in Zambia to 57.5% in Nigeria and 61.5% in Bangladesh and Viet Nam. Using these savings, diagnostic coverage theoretically could be expanded by 34% to 160% across the different approaches and countries.ConclusionsUsing AI software data generated during CXR interpretation to inform a differentiated pooled testing strategy may optimize TB diagnostic test use, and could extend molecular tests to more people who need them. The optimal AI thresholds and pooled testing strategy varied across countries, which suggests that bespoke screening and testing approaches may be needed for differing populations and settings.

An explainable Artificial Intelligence software system for predicting diabetes

Implementing diabetes surveillance systems is paramount to mitigate the risk of incurring substantial medical expenses. Currently, blood glucose is measured by minimally invasive methods, which involve extracting a small blood sample and transmitting it to a blood glucose meter. This method is deemed discomforting for individuals who are undergoing it. The present study introduces an Explainable Artificial Intelligence (XAI) system, which aims to create an intelligible machine capable of explaining expected outcomes and decision models. To this end, we analyze abnormal glucose levels by utilizing Bi-directional Long Short-Term Memory (Bi-LSTM) and Convolutional Neural Network (CNN). In this regard, the glucose levels are acquired through the glucose oxidase (GOD) strips placed over the human body. Later, the signal data is converted to the spectrogram images, classified as low glucose, average glucose, and abnormal glucose levels. The labeled spectrogram images are then used to train the individualized monitoring model. The proposed XAI model to track real-time glucose levels uses the XAI-driven architecture in its feature processing. The model's effectiveness is evaluated by analyzing the performance of the proposed model and several evolutionary metrics used in the confusion matrix. The data revealed in the study demonstrate that the proposed model effectively identifies individuals with elevated glucose levels.

Artificial Intelligence: A Help or Hindrance to Scientific Writing?

We have assessed the chatbot Generative Pretrained Transformer, a type of artificial intelligence software designed to simulate conversations with human users, in an experiment designed to test its relevance to scientific writing. chatbot Generative Pretrained Transformer could become a promising and powerful tool for tasks such as automated draft generation, which may be useful in academic activities to make writing work faster and easier. However, the use of this tool in scientific writing raises some ethical concerns and therefore there have been calls for it to be regulated. It may be difficult to recognize whether an abstract or paper is written by a chatbot or a human being because chatbots use advanced techniques, such as natural language processing and machine learning, to generate text that is similar to human writing. To detect the author is a complex task and requires thorough critical reading to reach a conclusion. The aim of this paper is, therefore, to explore the pros and cons of the use of chatbots in scientific writing.

Deep Learning Reconstruction of Accelerated MRI: False-Positive Cartilage Delamination Inserted in MRI Arthrography Under Traction.

The radiological imaging industry is developing and starting to offer a range of novel artificial intelligence software solutions for clinical radiology. Deep learning reconstruction of magnetic resonance imaging data seems to allow for the acceleration and undersampling of imaging data. Resulting reduced acquisition times would lead to greater machine utility and to greater cost-efficiency of machine operations. Our case shows images from magnetic resonance arthrography under traction of the right hip joint from a 30-year-old, otherwise healthy, male patient. The undersampled image data when reconstructed by a deep learning tool can contain false-positive cartilage delamination and false-positive diffuse cartilage defects. In the future, precision of this novel technology will have to be put to thorough testing. Bias of systems, in particular created by the choice of training data, will have to be part of those assessments.

Independent evaluation of the accuracy of 5 artificial intelligence software for detecting lung nodules on chest X-rays.

The integration of artificial intelligence (AI) into medicine is growing, with some experts predicting its standalone use soon. However, skepticism remains due to limited positive outcomes from independent validations. This research evaluates AI software's effectiveness in analyzing chest X-rays (CXR) to identify lung nodules, a possible lung cancer indicator. This retrospective study analyzed 7,670,212 record pairs from radiological exams conducted between 2020 and 2022 during the Moscow Computer Vision Experiment, focusing on CXR and computed tomography (CT) scans. All images were acquired during clinical routine. The final dataset comprised 100 CXR images (50 with lung nodules, 50 without), selected consecutively and based on inclusion and exclusion criteria, to evaluate the performance of all five AI-based solutions, participating in the Moscow Computer Vision Experiment and analyzing CXR. The evaluation was performed in 3 stages. In the first stage, the probability of a nodule in the lung obtained from AI services was compared with the Ground Truth (1-there is a nodule, 0-there is no nodule). In the second stage, 3 radiologists evaluated the segmentation of nodules performed by the AI services (1-nodule correctly segmented, 0-nodule incorrectly segmented or not segmented at all). In the third stage, the same radiologists additionally evaluated the classification of the nodules (1-nodule correctly segmented and classified, 0-all other cases). The results obtained in stages 2 and 3 were compared with Ground Truth, which was common to all three stages. For each stage, diagnostic accuracy metrics were calculated for each AI service. Three software solutions (Celsus, Lunit INSIGHT CXR, and qXR) demonstrated diagnostic metrics that matched or surpassed the vendor specifications, and achieved the highest area under the receiver operating characteristic curve (AUC) of 0.956 [95% confidence interval (CI): 0.918 to 0.994]. However, when evaluated by three radiologists for accurate nodule segmentation and classification, all solutions performed below the vendor-declared metrics, with the highest AUC reaching 0.812 (95% CI: 0.744 to 0.879). Meanwhile, all AI services demonstrated 100% specificity at stages 2 and 3 of the study. To ensure the reliability and applicability of AI-based software, it is crucial to validate performance metrics using high-quality datasets and engage radiologists in the evaluation process. Developers are recommended to improve the accuracy of the underlying models before allowing the standalone use of the software for lung nodule detection. The dataset created during the study may be accessed at https://mosmed.ai/datasets/mosmeddatargogksnalichiemiotsutstviemlegochnihuzlovtipvii/.

Artificial intelligence software for analysing chest X-ray images to identify suspected lung cancer: an evidence synthesis early value assessment.

Lung cancer is one of the most common types of cancer in the United Kingdom. It is often diagnosed late. The 5-year survival rate for lung cancer is below 10%. Early diagnosis may improve survival. Software that has an artificial intelligence-developed algorithm might be useful in assisting with the identification of suspected lung cancer. This review sought to identify evidence on adjunct artificial intelligence software for analysing chest X-rays for suspected lung cancer, and to develop a conceptual cost-effectiveness model to inform discussion of what would be required to develop a fully executable cost-effectiveness model for future economic evaluation. The data sources were MEDLINE All, EMBASE, Cochrane Database of Systematic Reviews, Cochrane CENTRAL, Epistemonikos, ACM Digital Library, World Health Organization International Clinical Trials Registry Platform, clinical experts, Tufts Cost-Effectiveness Analysis Registry, company submissions and clinical experts. Searches were conducted from 25 November 2022 to 18 January 2023. Rapid evidence synthesis methods were employed. Data from companies were scrutinised. The eligibility criteria were (1) primary care populations referred for chest X-ray due to symptoms suggestive of lung cancer or reasons unrelated to lung cancer; (2) study designs that compared radiology specialist assessing chest X-ray with adjunct artificial intelligence software versus radiology specialists alone and (3) outcomes relating to test accuracy, practical implications of using artificial intelligence software and patient-related outcomes. A conceptual decision-analytic model was developed to inform a potential full cost-effectiveness evaluation of adjunct artificial intelligence software for analysing chest X-ray images to identify suspected lung cancer. None of the studies identified in the searches or submitted by the companies met the inclusion criteria of the review. Contextual information from six studies that did not meet the inclusion criteria provided some evidence that sensitivity for lung cancer detection (but not nodule detection) might be higher when chest X-rays are interpreted by radiology specialists in combination with artificial intelligence software than when they are interpreted by radiology specialists alone. No significant differences were observed for specificity, positive predictive value or number of cancers detected. None of the six studies provided evidence on the clinical effectiveness of adjunct artificial intelligence software. The conceptual model highlighted a paucity of input data along the course of the diagnostic pathway and identified key assumptions required for evidence linkage. This review employed rapid evidence synthesis methods. This included only one reviewer conducting all elements of the review, and targeted searches that were conducted in English only. No eligible studies were identified. There is currently no evidence applicable to this review on the use of adjunct artificial intelligence software for the detection of suspected lung cancer on chest X-ray in either people referred from primary care with symptoms of lung cancer or people referred from primary care for other reasons. Future research is required to understand the accuracy of adjunct artificial intelligence software to detect lung nodules and cancers, as well as its impact on clinical decision-making and patient outcomes. Research generating key input parameters for the conceptual model will enable refinement of the model structure, and conversion to a full working model, to analyse the cost-effectiveness of artificial intelligence software for this indication. This study is registered as PROSPERO CRD42023384164. This award was funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme (NIHR award ref: NIHR135755) and is published in full in Health Technology Assessment; Vol. 28, No. 50. See the NIHR Funding and Awards website for further award information.