Information Artificial Intelligence Research Articles

Predicting Reduction Mammaplasty Total Resection Weight With Machine Learning.

Machine learning (ML) is a form of artificial intelligence that has been used to create better predictive models in medicine. Using ML algorithms, we sought to create a predictive model for breast resection weight based on anthropometric measurements. We analyzed 237 patients (474 individual breasts) who underwent reduction mammoplasty at our institution. Anthropometric variables included body surface area (BSA), body mass index, sternal notch-to-nipple (SN-N), and nipple-to-inframammary fold values. Four different ML algorithms (linear regression, ridge regression, support vector regression, and random forest regression) either including or excluding the Schnur Scale prediction for the same data were trained and tested on their ability to recognize the relationship between the anthropometric variables and total resection weights. Resection weight prediction accuracy for each model and the Schnur scale alone were evaluated based on using mean absolute error (MAE). In our cohort, mean age was 40.36 years. Most patients (71.61%) were African American. Mean BSA was 2.0 m2, mean body mass index was 33.045 kg/m2, mean SN-N was 35.0 cm, and mean nipple-to-inframammary fold was 16.0 cm. Mean SN-N was found to have the greatest variable importance. All 4 models made resection weight predictions with MAE lower than that of the Schnur Scale alone in both the training and testing datasets. Overall, the random forest regression model without Schnur scale weight had the lowest MAE at 186.20. Our ML resection weight prediction model represents an accurate and promising alternative to the Schnur Scale in the setting of reduction mammaplasty consultations.

AI frontiers in oncology: Bridging the gap between humanity and machine.

e13657 Background: Artificial intelligence (AI) is rapidly transforming medical education and how healthcare services are delivered. Oncology healthcare professionals (HCPs) can significantly benefit from the advances of AI including optimized diagnostic capabilities, treatment plans, resource allocation, patient outcomes and much more. The synergy between technology and healthcare is strengthening, heralding opportunities for a revolution in medical education and the broader healthcare industry. We developed an introductory AI workshop and conducted pre- and post-surveys, aiming to inform the design of successive workshops and an AI curriculum, to access knowledge, attitudes and perceived challenges for AI integration into Medical Oncology. Methods: An interactive one-hour workshop was conducted for physicians and faculty of the Hematology and Oncology department. 18 participants, encompassing 6 physicians and 12 other HCPs completed a pre- and post-workshop survey. We evaluated participants' knowledge, opinions, and perceived limitations regarding AI in oncology research, practitioner education, and clinical applications using both open and closed-ended questions, such as multiple-choice and 1-5 Likert scale formats. Results: Survey results included 18 pre-workshop and 17 post-workshop participants, 83% had no formal computer science training: AI knowledge in medicine and awareness of clinical AI applications increased by 100%. Interestingly 24% felt the workshop influenced their view on AI's role in their medical career and better prepared for AI-related challenges. 58% believed AI would assist clinicians within 5 years. However, the belief that AI will play a significant role in their own career did not change after the intervention. Data privacy concerns increased by 25%, while concerns about physician over-reliance on AI decreased by 15%. Notably, 76% were motivated to engage with AI during their future practice after the workshop. Conclusions: The AI initiative significantly enhanced oncology healthcare professionals' understanding and readiness for AI integration, despite most lacking prior formal AI education. A marked increase in AI knowledge and motivation to use AI in practice was noted, although perceptions of AI's impact in their day-to-day work remained unchanged with one workshop, contrasting expert views and predictions. Data privacy concerns rose, but decreased fears of over-reliance on AI indicate a more balanced perspective. These findings stress the need for ongoing AI education in healthcare and underscore the importance of baseline evaluations and feedback for shaping future workshops. Since, AI's integration into society is poised to transform healthcare, an AI curriculum is essential.

The Use of Artificial Intelligence in Endodontics.

Endodontics is the dental specialty foremost concerned with diseases of the pulp and periradicular tissues. Clinicians often face patients with varying symptoms, must critically assess radiographic images in 2 and 3 dimensions, derive complex diagnoses and decision making, and deliver sophisticated treatment. Paired with low intra- and interobserver agreement for radiographic interpretation and variations in treatment outcome resulting from nonstandardized clinical techniques, there exists an unmet need for support in the form of artificial intelligence (AI), providing automated biomedical image analysis, decision support, and assistance during treatment. In the past decade, there has been a steady increase in AI studies in endodontics but limited clinical application. This review focuses on critically assessing the recent advancements in endodontic AI research for clinical applications, including the detection and diagnosis of endodontic pathologies such as periapical lesions, fractures and resorptions, as well as clinical treatment outcome predictions. It discusses the benefits of AI-assisted diagnosis, treatment planning and execution, and future directions including augmented reality and robotics. It critically reviews the limitations and challenges imposed by the nature of endodontic data sets, AI transparency and generalization, and potential ethical dilemmas. In the near future, AI will significantly affect the everyday endodontic workflow, education, and continuous learning.

Artificial intelligence and machine learning for early cancer prediction and response

Worldwide, cancer claims more lives than any other disease. Although cancer detection, prognosis, and treatment have all advanced, one major obstacle is the lack of personalized, data-driven care. Artificial intelligence (AI), used to forecast and automate numerous malignancies, has emerged as a viable solution for increasing healthcare accuracy and patient outcomes. Artificial intelligence applications in cancer include risk evaluation, early detection, patient prognosis prediction, and treatment selection based on comprehensive data. Machine learning (ML), a form of artificial intelligence that allows computers to learn from training data, is very successful in predicting breast, brain, lung, liver, and prostate cancers. Indeed, AI and machine learning have outperformed physicians in predicting cancer. These technologies can potentially enhance the diagnosis, prediction, and quality of life of patients with a wide range of disorders, not just cancer. As a result, it is critical to enhance existing AI and ML technologies and create new programs to help patients. This article discusses the use of AI and machine learning algorithms in cancer prediction, including its present uses, future possibilities, and limitations.

Was The Spoken Word the First Form of Artificial Intelligence? A Probe

Was The Spoken Word the First Form of Artificial Intelligence? A Probe

Treading water: new data on the impact of AI ethics information sessions in classics and ancient language pedagogy

Abstract Over 2023, many universities and policy organisations in the higher education (HE) sector are working to create guiding principles and guidelines for the use of generative artificial intelligence (AI) in HE Teaching and Learning (T&L). Despite these guidelines, students remain unsure if and how they should use AI. This article discusses the AI information sessions held over the Autumn 2023 term in the Department of Classics at the University of Reading, which aimed to provide students with the knowledge and tools to make informed judgements about using AI in their studies. These sessions discussed the benefits and drawbacks of generative AI, highlighting training data, content policy, environmental impact, and examples of potential uses. Staff and student participants were surveyed before and after these information sessions to gather their opinions surrounding AI use. Although at least 60% of participants had previously used generative AI, 80% of participants were apprehensive of or against using generative AI tools for learning purposes following the AI information sessions. By providing staff and students with the ethical considerations surrounding generative AI, they can make an informed judgement about using AI in their work without misplaced faith or excessive fear.

Accommodating Machine Learning Algorithms in Professional Service Firms

Machine learning algorithms, as one form of artificial intelligence, are significant for professional work because they create the possibility for some predictions, interpretations and judgements that inform decision-making to be made by algorithms. However, little is known about whether it is possible to transform professional work to incorporate machine learning while also addressing negative responses from professionals whose work is changed by inscrutable algorithms. Through original empirical analysis of the effects of machine learning algorithms on the work of accountants and lawyers, this paper identifies the role of accommodating machine learning algorithms in professional service firms. Accommodating machine learning algorithms involves strategic responses that both justify adoption in the context of the possibilities and new contributions of machine learning algorithms and respond to the algorithms’ limitations and opaque and inscrutable nature. The analysis advances understanding of the processes that enable or inhibit the cooperative adoption of artificial intelligence in professional service firms and develops insights relevant when examining the long-term impacts of machine learning algorithms as they become ever more sophisticated.

Status Quo of Artificial Intelligence’s Role in the HRM Operations

Coexistence of technology and business dates back to the late 18th century when the first ever use of a computer was for recording the census by the US government in 1890. The use of technology in business can be ascribed to different organisations in the different nations on the parallel lines of time. It includes invention of cash machines and their use by Barclays in England in the early 1960s, the induction of telephone-based modems for order management by Baxter Pharmaceuticals and use of small desktop computing device called Minitel for processing customer orders in France were the other notable developments in the history of coexistence of technology and business. Increasing operations in the business functions have created an urge for the technological innovation in the industry to handle the operations electronically. Figueiredo and Cohen (2019) say that technology has become an indispensable component of every business function by delivering ease in operations and productivity. The end of the 20th century had witnessed the leaping progress in computing in the form of artificial intelligence (AI) performing the tasks that were unimaginable to comprehend a decade back in time. Developments in the technological research and development prove that organisations have started inducting AI into as many fields as possible at a considerable pace. As a part of the shifting technological dynamics in the industry HR function has also transformed digitally. Tools like enterprise applications have forayed intensely into the operations of human resources management (HRM). These enterprise resource planning (ERP) tools remain to primarily serve the integration of HRM to the other functions. However, enterprise tools could not serve the purpose of supporting decisiveness in the areas of HR planning, workforce design and performance management at large. However, Tuck (2019) argues that AI is assuming increased responsibilities in the different sections of the society and business including the HRM function. At present, the amount of knowledge on the status quo of the role of AI in the HRM functions is scarcely available. Literature related to this disruptive technology in the HR function is still at the nascent stage. This study will examine the role of AI as a key component in the HRM function, which is regarded to be highly human-driven.

AN IN-DEPTH EXPLORATION OF ARTIFICIAL INTELLIGENCE IN THE CONTEXT OF CONTEMPORARY DATA CHALLENGES; DIFFERENCES BETWEEN HUMAN AND MACHINE LEARNING

Machine learning and artificial intelligence produce algorithms that appear to be able to make "intelligent" decisions similar to those of humans but function differently from human thinking. To make decisions based on machine suggestions, humans should be able to understand the background of these suggestions. However, since humans are oriented to understand human intelligence, it is not yet fully clear whether humans can truly understand the "thinking" generated by machine learning, or whether they merely transfer human-like cognitive processes to machines. In addition, media representations of artificial intelligence show higher capabilities and greater human likeness than they currently have. In our daily lives, we increasingly encounter assistance systems that are designed to facilitate human tasks and decisions based on intelligent algorithms. These algorithms are predominantly based on machine learning technologies, which make it possible to discover previously unknown correlations and patterns by analyzing large amounts of data. One example is the machine analysis of thousands of X-ray images of sick and healthy people. This requires identifying the patterns by which images labeled as "healthy" can be distinguished from those labeled as "sick" and to find an algorithm that identifies the latter. In the meantime, "trained" algorithms created in this way are used in various fields of application, not only for medical diagnoses but also in the pre-selection of applicants for a job advertisement or in communication with the help of voice assistants. These voice assistants are enabled by intelligent algorithms to offer internet services through short commands. Harald Lesch, referring to his book Unpredictable, written together with Thomas Schwarz, says the development of artificial intelligence can be compared to bringing aliens to Earth. With machine learning, a previously unknown form of non-human intelligence has been created. This chapter discusses whether forms of artificial intelligence, as they are currently being publicly discussed, differ substantially from human thinking. Furthermore, it will be discussed to what extent humans can comprehend the functioning of artificial intelligence that has been created through machine learning when interacting with them. Finally, the risks and opportunities will be weighed and discussed..

Пошатнулась ли криминалистика (судебная экспертиза)?

Scientifi c and technological progress is always accompanied with hasty sensational declarations of certain researchers that are picked up by mass media. This sometimes results in distortion of reality and loss of the rational kernel of hyped developments. The article analyzes poorly-grounded ideas about yet another "collapse" of criminalistics (criminalistic examination). The authors show the baselessness of statements about the "scientifi c judge" in the form of artifi cial intelligence. The important thing in automation of jurisdictional processes is not the deployment of smart systems as such but the development of algorithms of systematization of regulations and technical standards considering their content.

Use of artificial intelligence and the future of peer review.

Conducting high-quality peer review of scientific manuscripts has become increasingly challenging. The substantial increase in the number of manuscripts, lack of a sufficient number of peer-reviewers, and questions related to effectiveness, fairness, and efficiency, require a different approach. Large-language models, 1 form of artificial intelligence (AI), have emerged as a new approach to help resolve many of the issues facing contemporary medicine and science. We believe AI should be used to assist in the triaging of manuscripts submitted for peer-review publication.

Use of natural language processing to uncover racial bias in obstetrical documentation

Natural Language Processing (NLP), a form of Artificial Intelligence, allows free-text based clinical documentation to be integrated in ways that facilitate data analysis, data interpretation and formation of individualized medical and obstetrical care. In this cross-sectional study, we identified all births during the study period carrying the radiology-confirmed diagnosis of fibroid uterus in pregnancy (defined as size of largest diameter of >5 cm) by using an NLP platform and compared it to non-NLP derived data using ICD10 codes of the same diagnosis. We then compared the two sets of data and stratified documentation gaps by race. Using fibroid uterus in pregnancy as a marker, we found that Black patients were more likely to have the diagnosis entered late into the patient's chart or had missing documentation of the diagnosis.With appropriate algorithm definitions, cross referencing and thorough validation steps, NLP can contribute to identifying areas of documentation gaps and improve quality of care.

An effective role of artificial intelligence and machine learning in banking sector

Financial institutions now have a tool at their disposal in the form of artificial intelligence (AI) and machine learning (ML) to meet the rising demand from customers for better, safer, and more convenient methods to manage their money. Artificial intelligence also helps the financial sector streamline and improve processes including financial risk management, quantified trading, and investment decision-making. Consequently, solutions based on automation are predicted to revolutionize the way individuals receive financial services. With the help of AI and ML, managers may devote more time to the organization's strategic and critical tasks and less to its menial tasks. Artificial intelligence (AI) is quickly replacing humans in important financial tasks like risk assessment, stock trading, and extending credit to those who apply for loans. To elaborate, AI is a powerful tool with many applications in the banking and insurance sectors. The financial services business might greatly benefit from the application of ML and AI. They completely transform the financial services industry and how they are provided to customers. It has the potential to increase the efficiency of internal procedures, cybersecurity, and risk reduction while also leading to more informed and personalized products and services. Customer satisfaction is enhanced by the speedier response made possible by AI. Artificial intelligence (AI) and machine learning (ML) are changing the way financial institutions produce and utilize data insights, which is leading to new types of innovation in business models, changes in competitive ecosystems and workforces, new dynamics in risk, and new problems for companies and governments.

Advances in the study of tertiary lymphoid structures in the immunotherapy of breast cancer.

Breast cancer, as one of the most common malignancies in women, exhibits complex and heterogeneous pathological characteristics across different subtypes. Triple-negative breast cancer (TNBC) and HER2-positive breast cancer are two common and highly invasive subtypes within breast cancer. The stability of the breast microbiota is closely intertwined with the immune environment, and immunotherapy is a common approach for treating breast cancer.Tertiary lymphoid structures (TLSs), recently discovered immune cell aggregates surrounding breast cancer, resemble secondary lymphoid organs (SLOs) and are associated with the prognosis and survival of some breast cancer patients, offering new avenues for immunotherapy. Machine learning, as a form of artificial intelligence, has increasingly been used for detecting biomarkers and constructing tumor prognosis models. This article systematically reviews the latest research progress on TLSs in breast cancer and the application of machine learning in the detection of TLSs and the study of breast cancer prognosis. The insights provided contribute valuable perspectives for further exploring the biological differences among different subtypes of breast cancer and formulating personalized treatment strategies.

A SWOT ANALYSIS OF BREAST CANCER DIAGNOSIS IN DIGITAL MAMMOGRAPHY USING DEEP CONVOLUTIONAL NEURAL NETWORK

Breast cancer is a significant global health challenge that affects both men and women, leading to cause-specific deaths. Current early screening interventions, such as digital mammography (DM), are susceptible to high false-positives and false-negatives. This paper explores the potential of convolutional neural network (CNN), a form of artificial intelligence (AI), to support screening mammography with the aim to enhance accuracy in lesion detection, image classification and diagnostic prediction. Because the adoption of AI in cancer diagnosis is still in its infancy, the objective of this paper is to provide insight into the benefits and limitations of deep learning-based approaches to detect and diagnose cancer. An analysis of the implementation of CNN in AI-screening mammography models was conducted, using the SWOT strategic analysis tool. Internal strengths that improve the predictive accuracy of CNN include transfer learning and data augmentation, whereas the internal weaknesses include a lack of data standardisation and reproducibility. External opportunities consist of increased sensitivity in differentiating between microcalcifications and non-tumorous structures, improved predictive diagnosis and reduced workload. Nevertheless, integration within clinical settings must also consider the external threats of breaching patient privacy, automation biases and the role of clinical judgement.

인공지능(AI)과 한국 부동산의 부패방지에 관한 연구

This study attempted to present discussions and directions regarding artificial intelligence(AI) and the prevention of corruption in Korean real estate. This Paper dealt with moral hazard, corruption prevention, and trust using real estate, the 4th industrial revolution(4IR), AI, and intelligent information technology as keywords. Prior research is focusing on economic utility and technical benefits related to AI and real estate. In contrast, this study pointed out the risk of moral hazard and corruption arising from the combination of AI and real estate, and conducted a paper to prevent moral hazard and corruption. In this respect, there is a difference between this study and existing previous studies. This is where the originality of this study appears. The results of this study on anti-corruption of AI and Korean real estate are as follows. As a result of the study, the problems of AI and Korean real estate, and anti-corruption were presented in three major categories: 1. AI and the problem of changing times, 2. Korean real estate and corruption problem, and 3. AI and real estate problem. In relation to this, this study largely derived three measures as implications of the research. The contents include, first, responses to the issues of AI and changing times, second, responses to Korean real estate and corruption issues, and third, measures to respond to AI and real estate issues. This study hopes to restore market trust through Artificial Intelligence(AI) and the prevention of corruption in Korean real estate, and to build a future where the benefits and utility provided by real estate, AI, and intelligent information technology are desirable.

Can ChatGPT exceed humans in construction project risk management?

PurposeThe objective of this research is to investigate the capabilities of the ChatGPT GPT-4 model, a form of artificial intelligence (AI), in comparison to human experts in the context of construction project risk management.Design/methodology/approachEmploying a mixed-methods approach, the study draws a qualitative and quantitative comparison between 16 human risk management experts from Finnish construction companies and the ChatGPT AI model utilizing anonymous peer reviews. It focuses primarily on the areas of risk identification, analysis, and control.FindingsChatGPT has demonstrated a superior ability to generate comprehensive risk management plans, with its quantitative scores significantly surpassing the human average. Nonetheless, the AI model's strategies are found to lack practicality and specificity, areas where human expertise excels.Originality/valueThis study marks a significant advancement in construction project risk management research by conducting a pioneering blind-review study that assesses the capabilities of the advanced AI model, GPT-4, against those of human experts. Emphasizing the evolution from earlier GPT models, this research not only underscores the innovative application of ChatGPT-4 but also the critical role of anonymized peer evaluations in enhancing the objectivity of findings. It illuminates the synergistic potential of AI and human expertise, advocating for a collaborative model where AI serves as an augmentative tool, thereby optimizing human performance in identifying and managing risks.

Crossing Boundaries: The Ethics of AI and Geographic Information Technologies

Over the past two decades, there has been increasing research on the use of artificial intelligence (AI) and geographic information technologies for monitoring and mapping varying phenomena on the Earth’s surface. At the same time, there has been growing attention given to the ethical challenges that these technologies present (both individually and collectively in fields such as critical cartography, ethics of AI and GeoAI). This attention has produced a growing number of critical commentaries and articles as well as guidelines (by academic, governmental, and private institutions) that have been drafted to raise these ethical challenges and suggest potential solutions. This paper presents a review of 16 ethical guidelines of AI and 8 guidelines of geographic information technologies, analysing how these guidelines define and employ a number of ethical values and principles (e.g., autonomy, bias, privacy, and consent)One of the key findings from this review is the asymmetrical mentioning of certain values and principles within the guidelines. The AI guidelines make very clear the potential of AI to negatively impact social and environmental justice, autonomy, fairness and dignity, while far less attention is given to these impacts in the geographic information guidelines. This points to a need for the geo-information guidelines to be more attentive to the role geographic information can play in disempowering individuals and groups.

Bridging the Knowledge Gap in Artificial Intelligence: The Roles of Social Media Exposure and Information Elaboration

This study examined how social media influence the knowledge gap between low and high socioeconomic status (SES) groups in artificial intelligence (AI), a highly debated scientific subject warranting immediate scholarly attention. A national survey of U.S. adults ( N = 965) was conducted. The results showed that education and social media exposure to AI information (SME) predicted greater AI knowledge, and SME did not moderate the SES-based AI knowledge gap. Furthermore, information elaboration moderated the association between SME and the AI knowledge gap. SME was associated with a smaller AI knowledge gap when information elaboration was high rather than low.

Detection of spondylosis deformans in thoracolumbar and lumbar lateral X-ray images of dogs using a deep learning network.

Spondylosis deformans is a non-inflammatory osteophytic reaction that develops to re-establish the stability of weakened joints between intervertebral discs. However, assessing these changes using radiography is subjective and difficult. In human medicine, attempts have been made to use artificial intelligence to accurately diagnose difficult and ambiguous diseases in medical imaging. Deep learning, a form of artificial intelligence, is most commonly used in medical imaging data analysis. It is a technique that utilizes neural networks to self-learn and extract features from data to diagnose diseases. However, no deep learning model has been developed to detect vertebral diseases in canine thoracolumbar and lumbar lateral X-ray images. Therefore, this study aimed to establish a segmentation model that automatically recognizes the vertebral body and spondylosis deformans in the thoracolumbar and lumbar lateral radiographs of dogs. A total of 265 thoracolumbar and lumbar lateral radiographic images from 162 dogs were used to develop and evaluate the deep learning model based on the attention U-Net algorithm to segment the vertebral body and detect spondylosis deformans. When comparing the ability of the deep learning model and veterinary clinicians to recognize spondylosis deformans in the test dataset, the kappa value was 0.839, indicating an almost perfect agreement. The deep learning model developed in this study is expected to automatically detect spondylosis deformans on thoracolumbar and lumbar lateral radiographs of dogs, helping to quickly and accurately identify unstable intervertebral disc space sites. Furthermore, the segmentation model developed in this study is expected to be useful for developing models that automatically recognize various vertebral and disc diseases.