Artificial Intelligence Support Research Articles

ChatGPT's Capabilities for Use in Anatomy Education and Anatomy Research

ChatGPT's Capabilities for Use in Anatomy Education and Anatomy Research

Future of cardiovascular diagnosis with the support of artificial intelligence

Introduction: Term Artificial inteligence was used for the first time by John McCarthy in 1956, from that time we can observe its great development, espiecially in the past decade. Nowadays, Artificial inteligence present a great influence in every aspect of human life, also health care. In times of digitalizaton, great data bases it can enable an improvment in all aspects of healthcare system such as prevention, screening and treatment of diseases. Purpose:The main purpose of the work was to present the basic aspects related to artificial intelligence. Another important aspect of the article was to indicate the possibilities related to their use in cardiology to improve the effectiveness of doctors and make medical treatment more detailed and personalized, but also to clarify terms related do AI, such as machine learning or deep learning. Materials and methods: For the purpose of writing this article, the available literature was reviewed. Using keywords such as artificial inteligence, cardiology, machine learning, echocardiography, deep learning, data bases PubMed we ware searching for various clinical trials, meta analysis and randomized controlled trials from past 5 years. Results: According to the data published on the website of the World Health Organization (WHO), cardiovascular diseases remain the main cause of mortality worldwide. It is the reason of the great interest in its use in cardiology. Algorithms based on artificial intelligence are also used in electrocardiography. The use of artificial intelligence can improve the estimation of cardiovascular risk. Its use in the healing process is also being investigated. Conclusion: Artificial intelligence is used in many fields, including medicine. Its use may have a positive impact on the quality of medical care. Artificial intelligence also has numerous limitations. Due to this, it is necessary to develop and improve artificial intelligence.

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.

Artificial intelligence assisted optimization of rammed aggregate pier supported raft foundation systems based on parametric three-dimensional finite element analysis

The increased usage of rammed aggregate piers has demanded optimizing the design parameters of this technique. In this study, numerous parametric three-dimensional finite element analyses were conducted to optimize the parameters of the rammed aggregate pier-supported raft foundation with novel methods. Three-dimensional finite element simulations were validated using a field loading test for a single rammed aggregate pier at Neola, Iowa (USA). After the validation, elasticity modules of the rammed aggregate piers, raft foundation thicknesses, and column spacings were varied. The raft foundation-rammed aggregate pier groups system in 81 different combinations was subjected to parametric three-dimensional finite element analyses to provide data for the optimization study. The effect of the changes in the elasticity modulus of the rammed aggregate piers, the raft foundation thickness, and column spacing on the settlement was investigated. Optimization analyses were performed using the artificial intelligence-supported novel Goal Attainment Method and the Response Surface Method. The novel coding developed in this study, which automatically selects the most reasonable prediction function with the support of artificial intelligence, created a prediction function with a high correlation coefficient used in the optimization analyses. Two different optimization methods specified in the optimization study performed as multi-objectives were compared, and the solution system with the highest desirability value was selected. Therefore, optimum design parameters were reached by using novel methods for general raft foundation-rammed aggregate pier group systems to guide researchers.

Generative Deep Learning for Visual Animation in Landscapes Design

The biggest challenge for architecture designers is the time required for the design process. Especially landscape architects who have different work limits from architects in general. In contrast to architects in general, who are assisted in producing design plans by building standards, building requirements, and space programs that adapt to the type of project being undertaken. At the same time, some design jobs demand high-productivity landscape animation presentation in a short time. The long process involved in designing animation often makes it difficult for designers to produce optimal work. This study proposes generative zooming animation with artificial intelligence support to shorten the designer’s work process and energy optimization. Deep learning with Vector Quantized Generative Adversarial Network and Contrastive Language-Image Pre-Training was used to generate alternative landscape designs from text prompt-based and compile them in animation. Our experiment shows that one frame can be generated roughly in 3.636 ± 0.089 s, which is significantly faster than the conventional method to create animation. Moreover, our method is able to achieve a good-quality image, which scored 3.2904 using inception score evaluation. The effectiveness of deep learning in visual landscape and animation creation can help designers speed up the design process. Furthermore, working time efficiency without compromising design quality will increase designer productivity and economic growth.

Added value of an artificial intelligence algorithm in reducing the number of missed incidental acute pulmonary embolism in routine portal venous phase chest CT.

The purpose of this study was to evaluate the incremental value of artificial intelligence (AI) compared to the diagnostic accuracy of radiologists alone in detecting incidental acute pulmonary embolism (PE) on routine portal venous contrast-enhanced chest computed tomography (CT). CTs of 3089 consecutive patients referred to the radiology department for a routine contrast-enhanced chest CT between 27-5-2020 and 31-12-2020, were retrospectively analysed by a CE-certified and FDA-approved AI algorithm. The diagnostic performance of the AI was compared to the initial report. To determine the reference standard, discordant findings were independently evaluated by two readers. In case of disagreement, another experienced cardiothoracic radiologist with knowledge of the initial report and the AI output adjudicated. The prevalence of acute incidental PE in the reference standard was 2.2% (67 of 3089 patients). In 25 cases, AI detected initially unreported PE. This included three cases concerning central/lobar PE. Sensitivity of the AI algorithm was significantly higher than the outcome of the initial report (respectively 95.5% vs. 62.7%, p < 0.001), whereas specificity was very high for both (respectively 99.6% vs 99.9%, p = 0.012). The AI algorithm only showed a slightly higher amount of false-positive findings (11 vs. 2), resulting in a significantly lower PPV (85.3% vs. 95.5%, p = 0.047). The AI algorithm showed high diagnostic accuracy in diagnosing incidental PE, detecting an additional 25 cases of initially unreported PE, accounting for 37.3% of all positive cases. Radiologist support from AI algorithms in daily practice can prevent missed incidental acute PE on routine chest CT, without a high burden of false-positive cases. • Incidental pulmonary embolism is often missed by radiologists in non-diagnostic scans with suboptimal contrast opacification within the pulmonary trunk. • An artificial intelligence algorithm showed higher sensitivity detecting incidental pulmonary embolism on routine portal venous chest CT compared to the initial report. • Implementation of artificial intelligence support in routine daily practice will reduce the number of missed incidental pulmonary embolism.

Magnetic crawler for welds Visual Testing, based on 3D profilometry and 2D image processing

To ensure the satisfactory performance of a welded structure, the quality of the welds must be determined by adequate testing procedures. During Visual Testing (VT), the weld is examined through the eyes of an inspector to determine surface discontinuities. Although this is commonly considered the easiest, quickest, and least expensive type of inspection, it has some limitations: a certified inspector shall always be on site to perform the test, making it strongly dependant on his experience, knowledge and current environmental conditions. In addition, there is no possibility to record inspection data except standard pictures and comments to be added to inspection reports. Modern technology brings novel solutions for quality assurance. This article describes a powerful tool which can improve the reliability of VT that combines precise laser measurements, image processing and data cloud computing. A portable magnetic crawler has been developed using a Raspberry Pi SBC, a smart profile sensor and a 5 MPix industrial colour camera in order to gather both weld 3D point cloud and surface pictures. Laser triangulation and processing power directly integrated on board allow an easy weld profile measurement. Undercuts, reinforcement excess, spatters, hi-low are precisely detected and sized by the laser sensor while integrated 2D camera records and analyses surface features such contaminations, corrosion, and weld discoloration. The point cloud weld reconstruction is realized stitching together 2D profile data at a fixed interval that can range up to a hundredth of a millimetre with a spatial resolution up to 50um. Inspected weld is fully digitalized in the form of 3D object together with a set of numerical features, data are securely stored in a local flash memory and automatically synchronized with cloud platform for remote data visualization. Automatic weld assessment can also be applied using cloud computing and artificial intelligence (AI) algorithms together with deep learning tools. The combination of images, 3D models, measurements, and other evaluable features all together in the same digital platform definitely enriches the weld assessment making it quicker and reliable with the support of AI, it gives the possibility of remote visual inspection, it ensures a flawless workflow where visual inspection data are stored in a dedicated platform and can be accessed at any moment by stakeholders.

Inalienable data: Ethical imaginaries of de-identified health data ownership

Many legal, ethical, and regulatory frameworks allow de-identified health data to be shared for research without patients’ opt-in consent. However, there may be public concerns about this practice, as people may feel they should have some say in how such data is used. This paper introduces the concept of the “inalienability of de-identified data,” to describe a key assumption underlying that public concern and preference. The assumption, derived from ethnographic research with public and professional stakeholders in AI driven medical image analysis over the past two years, refers to a sense of felt ownership over de-identified health data, even where the subject has been obscured as referent and no clear legal rights of data ownership otherwise exist. The concept is important to medical ethics because it underpins public expectations regarding the rights people should have over the sharing of medical data (including expectations for consent). We note that where those expectations go counter to current legal and bioethical frameworks for de-identified data sharing, they provide a challenge for public support of big data and artificial intelligence driven health research.

May Artificial Intelligence Be a Co-Author on an Academic Paper?

Dear Colleagues,&#x0D; Recently, for an article submitted to the European Journal of Therapeutics, it was reported that the paper may have been written with artificial intelligence support at a rate of more than 50% in the preliminary examination made with Turnitin. However, the authors did not mention this in the article’s material method or explanations section. Fortunately, the article’s out-of-date content and fundamental errors in its methodology allowed us no difficulty making the desk rejection decision.&#x0D; On the other hand, similar situations that we may encounter later caused us to discuss how we would decide when the artificial intelligence support of the articles was written. The general opinion that we have adopted and currently available in the literature is that if artificial intelligence is used while writing an article, how artificial intelligence is used in the methodology should be written in detail.&#x0D; Moreover, we encountered a much more exciting situation during our evaluation. In a few academic studies, we have seen that artificial intelligence is added as a co-author. On July 06, 2023, in the Web of Science, using the advanced search, we found four articles with the author name ChatGPT [1]. We have determined that ChatGPT is the author in one of these articles [2] and the Group Author in three [3-5].&#x0D; Lee [6] stated that although artificial intelligence tools are much more advanced than search engines, they cannot be an author regarding research ethics because they cannot take responsibility for what they write. Similarly, Goto and Katanoda [7] stated that it is the author’s responsibility to confirm that the texts written by ChatGPT are correct. However, Pourhoseingholi et al. [8] reported that keeping up with technology is inevitable. Additionally, they said that“this action will be more fruitful and practical in extended dimensions when international institutes like ICMJE or COPE come up with the appropriate adjustments and establish robust criteria to scheme the AI authorship”.&#x0D; Most probably, the use of artificial intelligence applications in scientific articles and whether it can be a co-author in these papers will be discussed soon.&#x0D; We encourage interested authors to submit their ideas to our journal as a letter to the editor.&#x0D; Yours sincerely,

Artificial intelligence and pelvic fracture diagnosis on X-rays: a preliminary study on performance, workflow integration and radiologists' feedback assessment in a spoke emergency hospital

PurposeThe aim of our study is to evaluate artificial intelligence (AI) support in pelvic fracture diagnosis on X-rays, focusing on performance, workflow integration and radiologists’ feedback in a spoke emergency hospital. Materials and methodsBetween August and November 2021, a total of 235 sites of fracture or suspected fracture were evaluated and enrolled in the prospective study. Radiologist’s specificity, sensibility accuracy, positive and negative predictive values were compared to AI. Cohen's kappa was used to calculate the agreement between AI and radiologist. We also reviewed the AI workflow integration process, focusing on potential issues and assessed radiologists’ opinion on AI via a survey. ResultsThe radiologist performance in accuracy, sensitivity and specificity was better than AI but McNemar test demonstrated no statistically significant difference between AI and radiologist’s performance (p = 0.32). Calculated Cohen’s K of 0.64. ConclusionContrary to expectations, our preliminary results did not prove a real improvement of patient outcome nor in reporting time but demonstrated AI high NPV (94,62%) and non-inferiority to radiologist performance. Moreover, the commercially available AI algorithm used in our study automatically learn from data and so we expect a progressive performance improvement. AI could be considered as a promising tool to rule-out fractures (especially when used as a “second reader”) and to prioritize positive cases, especially in increasing workload scenarios (ED, nightshifts) but further research is needed to evaluate the real impact on the clinical practice.

Spirometry services in England post-pandemic and the potential role of AI support software: a qualitative study of challenges and opportunities.

Spirometry services to diagnose and monitor lung disease in primary care were identified as a priority in the NHS Long Term Plan, and are restarting post-COVID-19 pandemic in England; however, evidence regarding best practice is limited. To explore perspectives on spirometry provision in primary care, and the potential for artificial intelligence (AI) decision support software to aid quality and interpretation. Semi-structured interviews with stakeholders in spirometry services across England. Participants were recruited by snowball sampling. Interviews explored the pre- pandemic delivery of spirometry, restarting of services, and perceptions of the role of AI. Transcripts were analysed thematically. In total, 28 participants (mean years' clinical experience = 21.6 [standard deviation 9.4, range 3-40]) were interviewed between April and June 2022. Participants included clinicians (n = 25) and commissioners (n = 3); eight held regional and/or national respiratory network advisory roles. Four themes were identified: 1) historical challenges in provision of spirometry services; 2) inequity in post- pandemic spirometry provision and challenges to restarting spirometry in primary care; 3) future delivery closer to patients' homes by appropriately trained staff; and 4) the potential for AI to have supportive roles in spirometry. Stakeholders highlighted historic challenges and the damaging effects of the pandemic contributing to inequity in provision of spirometry, which must be addressed. Overall, stakeholders were positive about the potential of AI to support clinicians in quality assessment and interpretation of spirometry. However, it was evident that validation of the software must be sufficiently robust for clinicians and healthcare commissioners to have trust in the process.

Implementation of artificial intelligence at the workplace, considering the work ability of employees

The use of artificial intelligence (AI) as an innovation driver is increasingly gaining importance among small and medium-sized manufacturing enterprises. In order to enable a successful AI implementation, both the business requirements and the needs of human resources must be considered. One construct that brings these dimensions together is the concept of work ability. So far, there is little scientific evidence addressing work ability in the context of AI implementation. Therefore, this article aims to create a multidimensional framework using the results of a qualitative study on employee-friendly implementation of AI-based systems. The framework combines central aspects (implementation stage, AI-autonomy level, and work ability) and helps to identify suitable recommendations for companies to increase acceptance and trust in the implementation process. Based on the developed framework, a first version of a socio-technical AI support tool has been created.

Evaluation of the performance of an unmanned aerial vehicle with artificial intelligence support and Mavlink protocol designed for response to social incidents response

The unmanned aerial vehicle name is TOMHA, which was developed to be used in the response of social incidents, aims to support the operational activities of security forces in response to social incidents, to expand their domination areas, to detect incidents that may disturb social peace in advance and to provide rapid intervention with the new unmanned aerial vehicle technologies developed. The scope of the TOMHA system designed was kept comprehensive compared to other unmanned aerial vehicles and the scope includes intervention to social events, ordering in local administrations, defense, reconnaissance and attack activities in military operations, inspections arranged for public interest, AFAD and service areas, forest fires detection and intervention, and public order operations. This TOMHA is being developed using the Pixhawk flight control card and the jetson nano artificial intelligence card. In addition to these cards, it has the feature of manual or artificial intelligence supported autonomous flight thanks to GPS, telemetry, FPV transceiver module, camera systems and national software to be used. It is controlled through the controller using RC communication channel for manual use. TOMHA has a flight time of 13.6 minutes, a thrust of 4.45G and a speed of 78 km and a mileage of 4993 meters in optimum conditions. The findings obtained by the tests performed with the designed TOMHA prototype show similar results with the literature. Thanks to the national design, TOMHA stands out when it encounters other unmanned aerial vehicles. It is seen that the response of the system to the sudden changes caused by the maneuver movements in the simulation environment is very fast and it follows the changes.

Artificial Intelligence Creates Art? An Experimental Investigation of Value and Creativity Perceptions

ABSTRACTThis paper presents a study analyzing the perception of artistic products created by or with the support of artificial intelligence (AI). The research builds on previous studies revealing that the output of artificial creativity processes can indeed rival human‐made products, satisfy consumer expectations, and derive enjoyment. However, in contrast to earlier works, the study focuses on the awareness of how AI is used during production processes (full automation, human‐AI co‐creation, and solely human) and product types (levels of involvement and product tangibility). A laboratory experiment was conducted to compare the value and creativity perceptions of 199 participants. Results revealed that, overall, the use of AI enhanced perceptions of process novelty. Additionally, the use of AI led to more positive evaluations of intangible and high involvement products such as songs compared to tangible and high involvement products such as paintings. This shows a decrease in perceived quality as more AI is involved in the production process. Furthermore, the level of product involvement level indicated no causal relationship to the degree of AI used in the production process.

Ways to improve the efficiency of implementing artificial intelligence systems in medical practice

BACKGROUND: The medical market offers interesting solutions that use artificial intelligence (AI) technologies; however, such solutions often remain at the startup level or are used locally. The question is how to achieve the maximum when introducing AI systems into medical practice. AIM: To answer the question of why the large number of existing developments in AI and medical decision support systems are not used as widely as medical information systems, telemedical consultations, and other health IT solutions. Possible ways to improve the efficiency of implementing AI technologies and medical decision support systems in the work of physicians were presented. METHODS: Theoretical and general scientific (analysis of literature and Internet sources on the problem of research, synthesis, generalization, comparison, and systematization) and empirical (observation, interview, and testing) methods were used. RESUTLS: The main barriers to the effective implementation of AI systems in medical practice and possible options to solve the following problems were highlighted. Problem 1: incorrect data collection. Solution: care must be taken with the accumulation of materials used for the analysis and training by AI systems in medicine. Problem 2: incompetent developers. Solutions: involvement of effective third-party specialists or training ones own. Problem 3: medical workers and/or patients aversion to AI technologies. Solutions: education in the successful application of AI technologies and medical decision support systems in healthcare and involvement of practicing physicians as experts during the AI conceptualization. CONCLUSIONS: If the above criteria for the development and use of AI technologies and medical decision support systems are met, the effect of their introduction into medical practice will tend to be maximized.

How to harness the potential of ChatGPT in education?

Technological advancements, particularly in the field of artificial intelligence (AI) have played an increasingly important role in transforming education. More recently, ground-breaking AI applications like ChatGPT have demonstrated the potential to bring radical changes to the educational landscape due to their capability to understand complex questions, generate plausible responses and human-like writing, and assist with the completion of complex tasks. However, ChatGPT has limitations in the quality of its output, such as the inclusion of inaccurate, fabricated and biased information and the lack of critical thinking and in-depth understanding. The combinations of these capabilities and limitations along with external factors (e.g., the growing demand for personalized learning support, the irresponsible and unethical use of AI) presents a range of opportunities and challenges to the potential use of ChatGPT in education. This paper presents a thorough SWOT (strength, weakness, opportunity, threat) analysis of ChatGPT, based on which we propose how ChatGPT can be properly integrated into teaching and learning practice to harness its potential in education.

How do patients perceive the AI-radiologists interaction? Results of a survey on 2119 responders

PurposeIn this study we investigate how patients perceive the interaction between artificial intelligence (AI) and radiologists by designing a survey. MethodWe created a survey focused on the application of Artificial Intelligence in radiology which consisted of 20 questions distributed in three sections:1.Demographic characteristics.2.General knowledge of new technologies and AI.3.Patients' perceptions of AI in diagnostic settings.Only completed questionnaires were considered for analysis. Results2119 subjects completed the survey. Among them, 1216 respondents were over 60 years old, showing interest in AI even though they were not digital natives. Although >45% of the respondents reported a high level of education, only 3% said they were AI experts. 87% of respondents favored using AI to support diagnosis but would like to be informed. Only 10% would consult another specialist if their doctor used AI support. Most respondents (76%) said they would not feel comfortable if the diagnosis was made by the AI alone, highlighting the importance of the physician's role in the emotional management of the patient. Finally, 36% of respondents were willing to discuss the topic further in a focus group. ConclusionPatients’ perception of the use of AI in radiology was positive, although still strictly linked to the supervision of the radiologist. Respondents showed interest and willingness to learn more about AI in the medical field, confirming how patients' confidence in AI technology and its acceptance is central to its widespread use in clinical practice.

Generative artificial intelligence empowers educational reform: current status, issues, and prospects

The emergence of Chat GPT has once again sparked a wave of information revolution in generative artificial intelligence. This article provides a detailed overview of the development and technical support of generative artificial intelligence. It conducts an in-depth analysis of the current application of generative artificial intelligence in the field of education, and identifies problems in four aspects: opacity and unexplainability, data privacy and security, personalization and fairness, and effectiveness and reliability. Corresponding solutions are proposed, such as developing explainable and fair algorithms, upgrading encryption technology, and formulating relevant laws and regulations to protect data, as well as improving the quality and quantity of datasets. The article also looks ahead to the future development trends of generative artificial intelligence in education from four perspectives: personalized education, intelligent teaching, collaborative education, and virtual teaching. The aim of the study is to provide important reference value for research and practice in this field.

Evolutionary trends in the use of artificial intelligence in support of architectural design

The paper analyses the evolutionary tendencies in the use of artificial intelligence in building design, highlighting: a) the ground lost by knowledge-based expert systems and the diffusion of systems combined with heuristic and stochastic approaches; b) the spread of evolutionary algorithms in the field of optimisation; c) a climax of the influence of probabilistic approaches around 2010; d) the progressive dominance of deep learning since 2012. It is likely that the next step requires hybridisation of deep learning and symbolic artificial intelligence, and it is expected that the contribution of design domain experts in terms of knowledge formalisation will be a fundamental driver of it.

Artificial intelligence in personalised learning: a bibliometric analysis

PurposeThis paper aims to present a comprehensive overview of the patterns and trends of publications on artificial intelligence (AI) in personalised learning. It addresses the need to investigate the intellectual structure and development of this area in view of the growing amount of related research and practices.Design/methodology/approachA bibliometric analysis was conducted to cover publications on AI in personalised learning published from 2000 to 2022, including a total of 1,005 publications collected from the Web of Science and Scopus. The patterns and trends in terms of sources of publications, intellectual structure and major topics were analysed.FindingsResearch on AI in personalised learning has been widely published in various sources. The intellectual bases of related work were mostly on studies on the application of AI technologies in education and personalised learning. The relevant research covered mainly AI technologies and techniques, as well as the design and development of AI systems to support personalised learning. The emerging topics have addressed areas such as big data, learning analytics and deep learning.Originality/valueThis study depicted the research hotspots of personalisation in learning with the support of AI and illustrated the evolution and emerging trends in the field. The results highlight its latest developments and the need for future work on diverse means to support personalised learning with AI, the pedagogical issues, as well as teachers’ roles and teaching strategies.