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Machine Learning and Font Design -- Taking the Print Advertisement Generated by Artificial Intelligence As an Example 作者： Jianjun Fang 1 作者单位： 1. College of Art and Design, Beijing University of Technology 通讯作者： Jianjun Fang Email:[email protected] 提交时间： 2023-01-03 摘要: With the gradual maturity of artificial intelligence, the field of artificial intelligence and print advertising has been widely combined, and the generation of print advertising intelligence has been widely used. However, AI generated print ads have gradually exposed its shortcomings in font design. Only relying on a single copyright font library for graphic combination, it is easy to cause the font and advertising theme is not appropriate, the font and brand image positioning is not consistent, and the font and advertising claims are not up to standard. Therefore, the introduction of machine learning font design into the field of print advertising has a certain practical value. Machine learning Font design Print advertising eye movement experiment 期刊： 2022 Artistic Design, Communication and Engineering Science 分类： 工程与技术科学 >> 工程与技术科学其他学科 引用： ChinaXiv:202301.00054 (或此版本 ChinaXiv:202301.00054V1 ) doi:10.12074/202301.00054 CSTR:32003.36.ChinaXiv.202301.00054.V1 推荐引用方式： Jianjun Fang.(2023).Machine Learning and Font Design -- Taking the Print Advertisement Generated by Artificial Intelligence As an Example.2022 Artistic Design, Communication and Engineering Science.[ChinaXiv:202301.00054] 版本历史 [V1] 2023-01-03 15:09:02 ChinaXiv:202301.00054V1 下载全文

Machine Learning and Font Design -- Taking the Print Advertisement Generated by Artificial Intelligence As an Example 作者： Jianjun Fang 1 作者单位： 1. College of Art and Design, Beijing University of Technology 通讯作者： Jianjun Fang Email:[email protected] 提交时间： 2023-01-03 摘要: With the gradual maturity of artificial intelligence, the field of artificial intelligence and print advertising has been widely combined, and the generation of print advertising intelligence has been widely used. However, AI generated print ads have gradually exposed its shortcomings in font design. Only relying on a single copyright font library for graphic combination, it is easy to cause the font and advertising theme is not appropriate, the font and brand image positioning is not consistent, and the font and advertising claims are not up to standard. Therefore, the introduction of machine learning font design into the field of print advertising has a certain practical value. Machine learning Font design Print advertising eye movement experiment 期刊： 2022 Artistic Design, Communication and Engineering Science 分类： 工程与技术科学 >> 工程与技术科学其他学科 引用： ChinaXiv:202301.00054 (或此版本 ChinaXiv:202301.00054V1 ) doi:10.12074/202301.00054 CSTR:32003.36.ChinaXiv.202301.00054.V1 推荐引用方式： Jianjun Fang.(2023).Machine Learning and Font Design -- Taking the Print Advertisement Generated by Artificial Intelligence As an Example.2022 Artistic Design, Communication and Engineering Science.[ChinaXiv:202301.00054] 版本历史 [V1] 2023-01-03 15:09:02 ChinaXiv:202301.00054V1 下载全文

Ravi Ramos (gaming SW engineer in Ubisoft, expert in ML/AI and Bioinformatics) speaks about trends, possibilities and limits of AI/ML and data science with Erik Ferencz (Google Cloud Developer, full-stack engineer, Revolgy). Ravi shares his impressive professional experience in the gaming industry, cloud technology, science and the way from Brazil to Brno, Czechia. Ravi is a real expert in ML/AI, so enjoy technical deep dive!

To explore the extent of actual use of AI/ ML protocols for diagnosing cancer in prospective (clinical trial or real-world) setting

A scoping review that aims to answer the question: How has machine learning, deep learning, and artificial intelligence been applied to office laryngoscopy imaging?

Abstract: This issue (DOI: 10.13140/RG.2.2.22431.76966) includes the following articles; P1121606475, Author="M. AbdelDayem and H. Hemeda and A. Sarhan", Title="Enhanced User Authentication through Keystroke Biometrics for Short-Text and Long-Text Inputs" P1121602462, Author="Eslam Mahmoud and Ahmed M. Elmogy and Amany Sarhan", Title="Enhancing Grid Local Outlier Factor Algorithm for better Outlier Detection" P1121616491, author="K. ElDahshan and Afaf Abd El-kader and Nermeen Ghazy", title="Minimum Average Scheduling Algorithm, MASA, Performance Boosting Approach" P1121636514, author="Aya S. M. Hussein and Mohsen A. A. Rashwan and Amir F. Atiya", title="Arabic Full Text Diacritization using Light Layered Approach" P1121647525, author="Eman K. Elsayed and Kamal A.El-Dahshan and Enas E. El-Sharawy and Naglaa E.Ghannam4", title="Semantic Anti-patterns Detection in UML Models based on Ontology Catalogue" http://www.icgst.com/paper.aspx?pid=P1121701539

The requirement for talent to work in the field of artificial intelligence and machine learning is rapidly increasing. Our Certification in Artificial Intelligence and Machine Learning in collaboration with E&ICT Academy, IIT Guwahati is an industry-specific program designed by E&ICT Academy, IIT Guwahati and Imarticus Learning. This program adopts a very collaborative approach that focuses more on real industry projects and assignments, helps you prepare for interview opportunities through our career services and gives you the opportunity to create your own GitHub project portfolio.

Cancer immunotherapy has revolutionized cancer treatment by leveraging the immune system to target malignant cells, yet resistance in many cancers highlights the need for novel therapeutic targets. Artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools for identifying new immunotherapy targets by analyzing vast datasets from genomics, proteomics, and clinical studies. This review explores the role of AI and ML in advancing the discovery of cancer-specific immunotherapy targets, such as tumor antigens and immune pathways. Key advances include the integration of big data, neoantigen prediction, biomarker discovery, and single-cell analysis. Despite these advancements, challenges remain, including data quality and standardization, interpretability of AI models, computational costs, and the need for biological validation of AI-driven discoveries. As AI and ML technologies continue to evolve, they hold the potential to overcome these barriers, leading to personalized immunotherapy solutions. This review also discusses future directions for AI-driven immunotherapy, emphasizing the need for improved models, ethical considerations, and clinical integration. 

Artificial Intelligence (AI) can revolutionize agriculture which impacts a country’s economy, employing more than 30% of the world’s population directly or indirectly. It can fulfill the needs of an ever-growing world’s population through automation. Traditional farmland practices like weeding, pesticide spraying, irrigation, monitoring soil nutritional and moisture status, etc. can be performed quicker using robots, sensors, drones, and algorithms. It reduces water wastage and pesticide overuse, maintains soil fertility, helps in reducing labor and enhances crop yield and productivity despite world problems. However, its penetration into agriculture is still in its infancy due to its uneconomical nature, lack of expertise and big data requirement for accuracy among others. This paper delves deeper into the various applications and impacts of AI in agriculture, new tools being used, challenges and future scope related to this field. Combined with Artificial Neural Network (ANN) models and Machine Learning (ML), along with Expert systems (ES) and Internet of Things (IoT), AI can do wonders in agriculture in the subsequent years to come.

Unit 42 leaders Ryan Olson and Rick Howard present another episode of their \"Don't Panic\" podcast, where they break down the big issues in cyber security and tell you why you don't need to panic.Artificial Intelligence and Machine Learning are littered throughout cybersecurity literature, but just what exactly do these buzzwords mean? This episode uncovers the mysteries surrounding AI and Machine Learning to fully unpack these confusing concepts.

As a discipline, anesthesiology is well placed to benefit from applications of machine learning (ML) and artificial intelligence (AI). For clinical care and more specicifically pain management, perioperative management, drug discovery and intensive care, we seek to form a succint mapping study conducted in a systematic manner. The study will include either ongoing or completed records of anesthesiology research/practice/applications.

Amii is one of the leading organizations in Artificial intelligence and machine learning. They have been assisting employers and businesses of all sizes in the disruptive field of Ai and Ml adoption. Learn how AI is changing the world of business. 

Amii is one of the leading organizations in Artificial intelligence and machine learning. They have been assisting employers and businesses of all sizes in the disruptive field of Ai and Ml adoption. Learn how AI is changing the world of business. 

This scoping review aims to systematically map the existing literature on artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) tools developed to generate multimodal (visual, textual, and audio) summaries of the intensive care unit (ICU) journey and discharge experience. Survivors of critical illness often experience fragmented transitions of care due to inadequate communication about their ICU stay. Traditional discharge summaries tend to be clinician-centred and predominantly text-based, often excluding contextual and patient-understandable information. In contrast, emerging AI-driven technologies offer the potential to automatically generate personalised summaries that combine narrative text, visual timelines, and audio explanations, improving continuity of care and patient understanding. Despite the rapid advancement of AI applications in healthcare, there is currently no comprehensive review examining how such multimodal summary tools have been applied or developed specifically for the ICU context. This review will identify and describe existing tools, their output modalities (textual, visual, audio), target users (clinicians, patients, families), and reported outcomes, limitations, and implementation factors. The findings will inform the development of future AI-based solutions for ICU survivorship and recovery communication. This review also forms a key evidence base for the Critical Illness Brain Dysfunction Survivorship Plus (CIBS+) programme and supports ongoing NIHR and Wellcome-funded research into personalised post-ICU digital health tools.

trAIlique's facilities management software offers a powerful solution to streamline operations, enhance efficiency and reduce costs. Our computer-aided facilities management (CAFM) software simplifies tasks, from maintenance tracking to asset management, helping businesses optimize their facilities with ease. https://www.trailique.ai/solutions/facilities-management-software/

This research delves into the dynamic intersection of artificial  intelligence (AI), machine learning (ML), and quantum computing, exploring their collaborative potential and contributions. The proposed method, centered around the fusion of reinforcement learning for quantum calibration, quantum error correction, and variational quantum algorithms, emerges as a groundbreaking approach with transformative implications. The autonomy introduced by reinforcement learning is a cornerstone, offering an innovative paradigm for quantum calibration. Through intelligent agents adapting quantum parameters autonomously, the proposed method not only expedites calibration processes but also mitigates the risks associated with manual interventions, ensuring a more robust and reliable quantum processor. This autonomous adaptation leads to improved stability and precision, setting a new standard in quantum computing methodology. Quantum error correction, another critical facet of the proposed method, addresses the inherent vulnerabilities of quantum systems. Stabilizer codes are employed to detect and correct errors, fortifying the reliability of quantum computations. This feature is paramount for the practical implementation of quantum computing applications, where the fragility of quantum states poses a considerable challenge. Variational quantum algorithms contribute to the efficiency and adaptability of the proposed method. By iteratively refining quantum parameters through classical optimization, these algorithms ensure that quantum circuits are optimized for diverse applications, spanning optimization problems and machine learning tasks. Comparative analyses against traditional methods underscore the proposed method’s superiority across autonomy, error resilience, calibration time, stability, efficiency, and reliability. This comprehensive advantage positions the proposed method as a frontrunner in the evolution of quantum computing methodologies.

Downloaded from U.S. Food and Drug Administration Youtube channel on 2025-09-12 17:49:31 https://youtube.com/watch?v=KaO3V-B1Wjg -------- This presentation introduced background information on artificial intelligence and machine learning in pharmaceutical development and described the current landscape of AI/ML-related submissions and reviews at CDER. The session listed key challenges associated with AI/ML applications and discussed evolving regulatory considerations for these technologies. AI/ML-related activities within the Office of Clinical Pharmacology were highlighted to demonstrate the agency's comprehensive approach to integrating these emerging technologies into drug development and regulation. Timestamps 02:55 – AI/ML and Related terminology 04:48 – Important Types of Neural Networks 07:27 – Opportunities for the Application of AI/ML in Drug discovery, Drug Development and Patient Care 12:25 – Regulatory Considerations for AI/ML in Drug Development (Personal Opinion, Still Evolving) 14:37 – Review Examples 22:08 – Challenges in the Application of AI/ML 25:30 – AI/ML Activities in Office of Clinical Pharmacology (OCP)/FDA 29:30 – Questions Speaker: Qi Liu, PhD, MStat, FCP Associate Director for Innovation & Partnership Office of Clinical Pharmacology (OCP) Office of Translational Sciences (OTS) Center for drug Evaluation and Research (CDER) | FDA Learn more at: https://www.fda.gov/drugs/news-events-human-drugs/regulatory-education-industry-redi-annual-conference-2024-innovation-medical-product-development ----------------------- FDA CDER’s Small Business and Industry Assistance (SBIA) educates and provides assistance in understanding the regulatory aspects of human drug products & clinical research. Upcoming Training - https://www.fda.gov/cdersbia SBIA Listserv - https://public.govdelivery.com/accounts/USFDA/subscriber/new?topic_id=USFDA_352 SBIA 2024 Playlist - https://www.youtube.com/playlist?list=PLey4Qe-Uxcxaf1HvpvnKO8-n8u9QW412g SBIA LinkedIn - https://www.linkedin.com/showcase/cder-small-business-and-industry-assistance SBIA Training Resources - https://www.fda.gov/cdersbialearn Twitter - https://twitter.com/FDA_Drug_Info Email - [email protected] Phone - (301) 796-6707 I (866) 405-5367

This is the review protocol used to conduct a scoping review on the use of artificial intelligence (AI) and machine learning (ML) in treatng Chronic Kidney Disease-Mineral and Bone Disorder (CKD-MBD).

Title: Applications of Artificial Intelligence and Machine Learning to Office Laryngoscopy: A Scoping Review Authors: Peter Yao Alexander German Katerina Andreadis Anaïs Rameau Contact: Peter Yao [email protected] Weill Cornell Medical College Review Questions: How has machine learning, deep learning, and artificial intelligence been applied to office laryngoscopy imaging? Searches: We searched the databases of MEDLINE, EMBASE, COCHRANE, Web of Science, and IEEE Explore. We searched using a combination of database-specific subject headings and text words for the main concepts of artificial intelligence and laryngoscopy, laryngeal structures or laryngeal pathology. The search strategy was customized for each database. Types of study to be included: There are no restrictions on the methodology, the type of model developed, or publication date. Studies not written in English, were not peer reviewed, or were review papers were excluded. Reviews were excluded from the analysis, but we screened the reference lists of relevant reviews to identify potential eligible studies. Condition or domain being studied: Artificial intelligence models applied to laryngoscopic videos or images. Participants/population: Patients undergoing office laryngoscopies. Intervention: Machine learning and artificial intelligence algorithms applied to video laryngoscopy or laryngoscopy images. Comparator/control: Not applicable. Primary Outcome: Identify, appraise, and synthesize the applications of artificial intelligence to laryngoscopy including by not limited to identification, detection, and segmentation of pathology, informative frame filtering, and anatomical segmentation. Additional outcome: This review will also identify existing gaps in the literature, and help formulate the best approach for applying artificial intelligence to laryngoscopic imaging to guide existing and future researchers. Data extraction: Screening: Literature search results were imported to Covidence, an Internet-based systematic review data management software that facilitates collaboration among reviewers during the study selection process. Articles were screened using a two-step process. In the first step, articles were screened by title and abstract by teams of two reviewers working independently. In the second step, articles that passed the title-abstract screen were screened by full-text by teams of two reviewers working independently. A third reviewer reviewed articles and resolved disagreements through consensus when the original two reviewers were not able to reach one. We will calculate the adjusted kappa statistic to measure interrater agreement for eligibility screening. Data Collection We divided the items within the data collection form into four blocks: (1) study information including publication year, author information, funding or sponsorship information, type of study, journal name and PICO elements; (2) database information including data source and sample size; (3) patient demographic information including gender, age, race, and disease diagnosis; (4) ML methodological information including ML model name, type, task, data classes, class split, how ground-truth labels are determined, objective function, and model performance. Data Synthesis: A systematic narrative synthesis will be provided with information presented in the text and tables to summarize and explain the characteristics and findings of the included studies. Analysis of subgroups or subsets: We will analyze studies in groups based on their objectives. For example, studies focused on diagnosis will be analyzed separately from studies focused on anatomical segmentation.

Addressing the critical gap in examining AI and ML applications and their strengths and limitations on system and organizational-level dimensions of PHC is essential for understanding how these technologies could impact core elements including health system governance resource allocation, workforce planning, and intersectoral coordination. As Health Services and Policy Research focuses on studying the structural and organizational elements underpinning health care systems, this scoping review aims to (i) map the current applications of AI and ML in meso- and macro-level elements of primary health care (PHC) within Health Services and Policy Research literature, (ii) assess the strengths and limitations of these tools, and (iii) identify application gaps and future directions in the field.

Artificial intelligence (AI) is an intelligence technology that is artificially programmed by humans to mimic humans. In clinical context, the use of AI and related technologies (e.g., machine learning and deep learning) are increasingly becoming popular. These technologies can aid clinicians in the diagnostic process and in the rehabilitation pathway, especially in disorder of consciousness (DoC), in which this aspect is particularly challenging. The aim of this scoping review is to map the literature on the role of AI, ML and DL in the field of diagnosis and rehabilitation treatment of DoCs, highlighting the current strengths and gaps, suggesting future perspectives. In particular, we tried to answer to the following questions: -What is the role of AI in the diagnosis and rehabilitation of DoCs? -What are the most used AI technologies in the field of DoCs? -How can AI improve patients’ care? -How reliable is AI in DoCs diagnosis compared to traditional diagnostic methods?

To watch the remainder of the webinar follow the link: https://bit.ly/2IqUuEH AI and ML adoption in the enterprise is exploding from Silicon Valley to Wall Street. Ubuntu is becoming the premier platform for these ambitions — from developer workstations, to racks, to clouds and to the edge with smart connected IoT. But with new developer trends comes a plethora of new technologies and terminologies to understand. In this webinar, join Canonical’s Kubernetes Product Manager Carmine Rimi for: ∙ A decoding of key concepts in AI/ML ∙ A look into why AI applications and their development are reshaping company’s IT ∙ A deep dive into how enterprises are applying devops practices to their ML infrastructure and workflows ∙ An introduction to Canonical AI / ML portfolio from Ubuntu to the Canonical Distribution of Kubernetes and and how to get started quickly with your project And in addition, we’ll be answering some of these questions: ∙ What do Kubeflow, Tensorflow, Jupyter, Edge AI, and GPGPUs do? ∙ What’s the difference between AI and ML? ∙ What is an AI model? How do you train it? How do you develop / improve it? How do you execute it?

Artificial intelligence and machine learning and data science has become what IT and ITES industry is used to be in the 1980s the scope of opportunities as a huge in these new emerging sectors--- This episode is sponsored by - Anchor: The easiest way to make a podcast. https://anchor.fm/app Support this podcast: https://anchor.fm/nirmit-verma/support

A scoping review which covers the different types or systems of Artificial intelligence and machine learning technologies and algorithms and its applications in various domains of Orthognathic surgery

Artificial Intelligence and Machine Learning are all over the news, taking over academia, getting billions of dollars of commercial investment, and will change both computer networking and wireless communications in fundamental ways. What does the future of amateur radio look like when radios use machine learning to operate? How does our relationship to the bands change with cognitive radio? Where are we in this transformation? What will happen next? This presentation was made at the QSO Today Virtual Ham Expo. For more information go to: https://qsotodayhamexpo.com Also visit the QSO Today Podcast for 1 hour interviews of ham radio operators who make these presentations: https://www.qsotoday.com Source: https://www.youtube.com/watch?v=-ojTlFECrSY Uploader: QSO Today Virtual Ham Expo

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Artificial intelligence is part of almost every business today; it facilitates business operations, increases productivity, and offers a variety of ways to speed up communication processes. Artificial intelligence and software (or software applications installed on it), as well as automation through AI systems, perform many of the tasks previously performed by employees and workers. Switching to an automated working environment has resulted in a lot of unnecessary business expenses, substantial time savings and a gradual increase in profits. The automation through AI of various business processes has taken many companies and organizations to the next level in terms of production and management.So, this article explains the role of artificial intelligence, machine learning and cloud computing in business. Dr. Pawan Whig 2019. Artificial Intelligence and Machine Learning In Business. International Journal on Integrated Education. 2, 2 (Jun. 2019). Pdf Url : https://journals.researchparks.org/index.php/IJIE/article/view/516/493 Paper Url : https://journals.researchparks.org/index.php/IJIE/article/view/516

This scoping review aims to identify the application of clinical outcome prediction modeling based on artificial intelligence and machine learning using nursing data in intensive care units.

Antimicrobial stewardship programs (ASPs) play a crucial role in promoting the appropriate use of antibiotics to combat antimicrobial resistance. ASPs encompass various strategies, including prospective audit with feedback, local guidelines implementation, education, and among others. However, implementing and maintaining ASPs can be resource-intensive, limiting their effectiveness and creating challenges for optimal ASP operations. Recent advancements in artificial intelligence (AI) and machine learning (ML) present promising opportunities to enhance the effectiveness of ASPs. Despite the potential benefits, there is a knowledge gap regarding the use of AI and ML in ASPs. Although some studies have shown positive outcomes using AI and ML in various healthcare applications, their specific impact on ASPs remains underexplored. This review aims to address this gap by providing an overview of the current state of research on AI and ML in ASPs, thereby informing actionable applications and guiding future research.

Artificial intelligence and machine learning 

Systematic and critical evidence syntheses remain essential for clinical decision making. Indeed, there has been an explosion of available evidence in many fields making it challenging for systematic reviews to keep up. Novel tools and processes have been developed recently to automate systematic review steps using algorithms including artificial intelligence (AI) and machine learning (ML) (AI-ML). The objective of this project is to evaluate the performance and usability of previously published AI-ML tools by comparing their performance to that of “human reviewers” in the title and abstract screening phase of the systematic review process. The dataset generated by a large and highly cited Cochrane review will provide a testing ground for the AI-ML tools evaluated in this project. (1)

Establishing early diagnosis for female adults using AI and ML is especially important since after the COVID-19 pandemic, as there was a noticeable decline in breast and cervical cancer screening of 87% and 84% in the United States with low-income women and women of color. This reduction in screening could lead to delayed diagnosis, later-stage identification of the cancer, and, unfortunately, worse outcomes, like death. A recent prospective study evaluated the use of AI and ML in cancer detection and false positive findings by looking at female adults with breast cancer. This study revealed that using AI significantly increased cancer detection rates while false positives declined compared with the standard of care, reducing unnecessary worry for many women. However, cancer screening within women continues to decline, even after COVID-19. There is a need to identify and understand the current circumstances of AI and ML's application on cancer screening and prevention among adults, primarily female adults, for more effective cancer care prevention and recommendations for the future.This scoping review aims to identify the barriers encountered in applying innovative AI, ML, and technological tools in promoting cancer screening among adult females. It will also address the role of patient-provider communication and trust in ensuring the success of AI applications and integration in primary care and prevention efforts. This scoping review will inform and guide future global recommendations for AI application in cancer prevention and care for female populations.

Advocates of artificial intelligence and machine learning in drug repurposing claim that it will increase efficiency, speed development, decrease costs, and protect patient welfare. In the following, we will use systematic review and meta-analytic methods to empirically evaluate the impact and outcomes for drugs and biologicals that have been repurposed as therapy for Covid-19 on the basis of artificial intelligence or machine learning hypotheses. For a cohort of drug repurposing hypotheses that entered the literature between 2020-01-01 and 2020-04-30, we will measure impact in terms of FDA approvals, FDA Emergency Use Authorizations, recommendation in NIH Covid-19 Treatment Guidelines, and positive RCT result within 6 months of follow-up after the first published recommendation of the drug repurposing hypothesis. We will measure trial outcomes in terms of number of trials, patient enrolment, meta-analyzed overall survival hazard ratios, and meta-analyzed proportions for treatment-related deaths and grade 3-4 treatment-related adverse events.

This systematic review examines the state of artificial intelligence and machine learning techniques applied to pediatric surgical pathology cases. Our results are expected to highlight promising avenues of artificial intelligence applications to pediatric pathology, as well as showcasing current gaps in performance and clinical workflow implementation.

This is a scoping review intending to investigate the sources of data, research gaps, and potential implications as reported in studies on the use of the AI/ML data analysis for exploring the potential correlation of self-reported health (SRH) with social determinants of health (SDOH).

MoDOT Research Summary: Data Acquisition and Processing Using Artificial Intelligence and Machine Learning, July 2024; Prepared by MoDOT construction, Materials, and Research, Missouri Department of Transportation; MODOT; transportation planning; Project TR202215;

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The objective of this review is to analyze the studies published to date on PK/PD mod-eling for antibiotic dose optimization using AI techniques. In addition, this review aims to evaluate in detail the different AI techniques used and the statistical metrics applied to assess the accuracy of the prediction and its comparison with the conven-tional monitoring techniques.

The integration of advanced technologies such as Artificial Intelligence (AI), encompassing machine learning (ML), deep learning, and neural networks, holds significant promise in revolutionizing patient care within outpatient primary clinics and ambulatory care settings. While the exploration of these technologies' implications for healthcare delivery is ongoing, several studies have delved into their potential benefits. AI-driven algorithms can analyze vast amounts of patient data, including electronic health records (EHRs), diagnostic images, and wearable device data, to assist healthcare providers in making more accurate diagnoses and treatment decisions. ML algorithms can predict patient outcomes, identify patterns in disease progression, and personalize treatment plans based on individual patient characteristics and preferences. Deep learning techniques excel in image recognition tasks, aiding in the interpretation of medical images such as X-rays, MRIs, and CT scans. Furthermore, AI-powered clinical decision support systems can augment healthcare professionals' decision-making processes by providing real-time recommendations and alerts regarding potential diagnoses, medication interactions, and treatment options. Despite these advancements, challenges remain in integrating AI technologies into outpatient primary clinics and ambulatory care settings. Issues such as data privacy and security, algorithm bias, interoperability of AI systems with existing healthcare infrastructure, and provider acceptance and adoption need to be addressed to realize the full potential of AI in improving patient care efficiency and effectiveness. In summary, while there has been examination and exploration of the integration of advanced technologies like AI, ML, deep learning, and neural networks in outpatient primary clinics and ambulatory care settings, further research and implementation efforts are needed to fully leverage their benefits and address associated challenges.

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In Indonesia, rainfall is one crucial triggering factor for landslides. This paper aims to build landslide event prediction models using several machine learning and artificial intelligence algorithms. The algorithms were trained with two different methods. The input of the algorithms was precipitation data obtained from the global satellite mapping of precipitation satellite observation, and the target was landslide event occurrence data obtained from the Indonesian National Board for Disaster Management. Each algorithm provided some model candidates with different parameter settings for each method. As a result, there were 52 and 72 model candidates for both methods. The best model was then chosen from each method. The result shows that the model generated by generalized linear model was the best model for the first method and deep learning for the second one. Furthermore, the best models at each method gained 0.828 and 0.836 for the area under receiver operating characteristics curve, and their log-loss were 0.156 and 0.154. The second method, which used input data transformation, provided better performance.

Assessing the feasibility and acceptability of the AI and machine learning in this area can help to understand the opportunities and possible barriers for the implementation and further provide evidence to support the real-world practice. To our knowledge, no study has reviewed and synthesized the current evidence on this topic. Therefore, this scoping review aims to systematically locate and review literature on the feasibility and acceptability of AI and machine learning applications in health care for anxiety-related disorders. This will enable the identification of research gaps in this topic and then contribute to improving future research and supporting evidence-based real-world practice of AI and machine learning in mental health care.

Breast cancer remains one of the most prevalent cancers affecting women globally. Early and accurate diagnosis is critical for improving treatment outcomes and survival rates. In recent years, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as powerful tools in the field of medical diagnostics, particularly in oncology. These technologies have shown promise in enhancing diagnostic accuracy, reducing human error, and streamlining workflows in radiology, pathology, and genomic analysis. This systematic review aims to comprehensively evaluate existing literature on the application of AI and ML techniques in breast cancer diagnosis. The review will focus on assessing the diagnostic performance (e.g., sensitivity, specificity, accuracy) of these models across various diagnostic modalities such as mammography, ultrasound, MRI, histopathological images, and genetic biomarkers. The purpose of this review is to Synthesize evidence on how AI and ML are being utilized in breast cancer diagnosis, Identify which algorithms and datasets are most commonly used, Compare the performance of AI/ML models with conventional diagnostic methods, Highlight gaps in current research and provide recommendations for future studies, The expected outcomes include a detailed summary of current AI/ML technologies in breast cancer diagnostics, an evaluation of their diagnostic accuracy, and an identification of challenges and future opportunities for clinical implementation. This review will contribute to the body of knowledge that can inform clinicians, researchers, and healthcare policymakers about the readiness and reliability of AI-assisted diagnostic tools in breast cancer care.

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This scoping review aims to provide insight on the usage of advanced ML and AI techniques in the design of clinical trials and acknowledge the gap between potential and actual applicability.

This study examines the impact of artificial intelligence (AI) and machine learning (ML) on professional roles within the context of financial reporting and auditing practices. Utilizing a quantitative approach, data were collected from 142 accountants in private businesses in Erbil through a structured questionnaire assessing perceptions on efficiency, accuracy, fraud detection, compliance, and professional impact. Statistical analyses, including multiple regression and correlation, were employed to determine the relationships between AI and ML integration and various professional outcomes. Contrary to expectations, the results revealed no significant impact of AI and ML on the perceived efficiency, accuracy, fraud detection capabilities, or compliance within the professional roles of accountants. These findings suggest a disconnect between the theoretical benefits of AI and ML technologies and their practical perceptions among professionals in the field. Recommendations include the need for enhanced training, incremental technology implementation, and improved governance structures to foster effective integration and utilization of AI and ML in financial practices. The study's implications are significant for organizations considering or currently implementing AI and ML technologies, highlighting the importance of addressing both technological and human factors to maximize the potential benefits of these innovations. Future research is encouraged to explore qualitative aspects of technology adoption and to conduct longitudinal studies to assess changes over time as professionals adapt to AI and ML tools. The limitations of the study, such as its geographical focus and cross-sectional design, suggest caution in generalizing the findings and point towards the need for broader, more diverse investigations. This research contributes to the ongoing discourse on the practical challenges of integrating advanced technologies in specialized professional domains, underscoring the need for a balanced approach that

The management of customer services by telephone encounters several problems: an uncontrollable flow of calls, complicated resource management, a very high cost of service, and more. Opportunities to improve the quality of service, save time and money triggered the widespread implementation of artificial intelligence (AI) based callbot. This article outlines the straightforward workflow developed to model the architecture of the callbot. Therefore, several algorithms were evaluated and compared based on real knowledge of a call center of an insurance society. The algorithms considered are: k-nearest neighbours (KNN), support vector machine (SVM), random forests (RF), logistic regression (LR), and Naıve Bayes (NB). The comparison criteria are: correct responses, response time, accuracy, Cohen’s kappa and F1 score using n-gram (1.1) and (2.2). The results obtained show that the SVM (accuracy=70.29%) presents the best results on all the comparison criteria. The comparison between the results of the human agents and the callbot shows an improvement in several levels: the cost savings are greater than 80% on all the tests carried out, the holding time decrease to 0 seconds, and the processing time (almost a third or more). The results obtained sufficiently meet the objectives of this project.

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The evolution of new technologies has spurred a growing body of literature exploring their application and impact on the financial sector, particularly the integration of artificial intelligence (AI). This paper delves into the rapid adoption of AI and machine learning within the financial sector, highlighting their potential to enhance financial stability and productivity. By reviewing research from 2018 to 2023, the study categorizes AI applications in finance into three main areas: cybersecurity, customer services, and financial management. Furthermore, the research identifies and classifies various threats posed to the integrity and stability of the financial system by AI, along with associated challenges for policy and regulatory frameworks. It also addresses the risks and obstacles inherent in deploying AI within financial markets and banking sectors, offering recommended strategies to mitigate these limitations. Despite the recognized advantages, the comprehensive understanding of AI's benefits and drawbacks remains incomplete due to its evolving nature and varied applications in banking. Clear policies governing AI usage are imperative to safeguard financial consumers and promote a fair and transparent financial market. These guidelines should prioritize human decision-making and foster an unbiased approach to policymaking, ultimately fostering innovation within the industry.

Perma.cc archive of https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device created on 2021-11-27 17:20:00+00:00.