Application Of Artificial Intelligence Research Articles

Applications of artificial intelligence in global diplomacy: A review of research and practical models

Applications of artificial intelligence in global diplomacy: A review of research and practical models

Clinical decision support system based on artificial intelligence and the patient’s subjective intention treatment model: A randomized controlled clinical trial.

10596 Background: To achieve intelligent diagnosis and treatment of tumors through the application of artificial intelligence and to meet the needs of areas lacking medical resources, a new treatment model based on the patient's subjective intention (PSI) was proposed, and a clinical decision support system (CDSS) called Doctors In Hands (DIH) was designed. Here, we report the DIH trial with the PSI treatment model to evaluate its efficiency. Methods: In this randomized controlled open-label trial (ChiCTR2400094469), patients from the departments of oncology, internal medicine, and surgery signed informed consent forms and were randomly divided into the clinician group, the CDSS plus clinician group and the CDSS group. The PSI treatment model was applied in the CDSS group. The primary endpoint was the sensitivity of alternative diagnoses and treatment options. The secondary endpoints were patient satisfaction and the specificity of alternative diagnoses and treatment options in different departments and groups. A multimodal large model based on the novel PSI treatment model was used as the intelligent platform for diagnosis and treatment data analysis and human‒computer interaction. Results: A total of 120 patients and 9 doctors were enrolled and randomly divided into three groups. In the clinician group, the sensitivity and specificity of diagnosis were 0.82 and 0.76, the Youden index was 0.58. In the CDSS group, the sensitivity and specificity were 0.85 and 0.80, the Youden index was 0.65. For the CDSS plus clinician group, the sensitivity and specificity were 0.90 and 0.87, the Youden index was 0.77. Subgroup analysis of patients according to treatment strategy revealed that the satisfaction of patients in the PSI-based CDSS group was significantly greater than that of patients in the clinician group (92% vs. 86%, P < 0.01), but there was no significant difference between the CDSS group and the CDSS plus clinician group (92% vs. 93%, P = 0.37). Subgroup analysis of the PSI-based CDSS group according to diagnostic strategy revealed that the diagnostic accuracy for patients from the department of oncology was significantly greater than that for patients from the department of internal medicine (0.88 vs. 0.81, P < 0.05) but was not significantly different from that for patients from the department of surgery (0.88 vs. 0.87, P = 0.14). Conclusions: DIH has good performance in the diagnosis of tumors and development of treatment plans. In the PSI model, patients can make independent choices regarding disease treatment plans according to the objective clinical or basic research evidence provided by DIH to reduce the duration of consultation and save medical resources. The clinical significance of this trial is that the CDSS can promote the sharing of medical resources and improve the efficiency and quality of clinical work in areas lacking medical institutions. Clinical trial information: ChiCTR2400094469 .

The Role of Smart Actuarial Calculations in Improving the Quality of Financial Reports of the Iraqi Insurance Company

The research aims to explore the role of actuarial calculations supported by artificial intelligence technologies (smart actuarial calculations) in enhancing the accuracy and quality of financial reporting. Companies face challenges related to the accuracy, transparency, and speed of preparing financial reports, necessitating innovative solutions based on smart technologies. The research analyzed the literature and employed an exploratory methodology to study the impact of smart actuarial calculations on the integrity of financial reports. The association between the application of artificial intelligence and professional standards and their impact on the performance of insurance companies and financial reporting practices was also examined. The researchers concluded that smart actuarial calculations enhance the quality of financial reports by increasing accuracy and speed, providing comprehensive analyses, reducing costs, and increasing efficiency through the reduction of human errors and precise risk analysis. These calculations also enhance compliance with accounting standards, build shareholder and stakeholder confidence, and emphasize the importance of professional standards and actuarial guidelines to ensure the quality of work. The research recommended improving the utilization of artificial intelligence in developing actuarial calculations, providing continuous training for experts, adhering to professional standards to ensure transparency and reliability, and encouraging future studies to investigate the implications of smart technology in the accounting and insurance fields. This research contributes to accounting literature by providing insights into artificial intelligence's role in developing actuarial calculations and improving insurance sector performance.

Application of artificial intelligence in laboratory hematology: Advances, challenges, and prospects

Application of artificial intelligence in laboratory hematology: Advances, challenges, and prospects

Applications of artificial intelligence in cardiovascular imaging: advantages, limitations, and future challenges

Artificial intelligence (AI) is rapidly transforming cardiovascular imaging, offering innovative solutions to enhance diagnostic precision, prognostic accuracy, and therapeutic decision-making. This review explores the role of AI in cardiovascular imaging, highlighting its applications, advantages, limitations, and future challenges. The discussion is structured by imaging modalities, including echocardiography, cardiac and coronary computed tomography, cardiac magnetic resonance, and nuclear cardiology. For each modality, we examine AI's contributions across the patient care continuum: from patient selection and image acquisition to quantitative and qualitative analysis, interpretation support, prognostic stratification, therapeutic guidance, and integration with other clinical data. AI applications demonstrate significant potential to streamline workflows, improve diagnostic accuracy, and provide advanced insights for complex clinical scenarios. However, several limitations must be addressed. Many AI algorithms are developed using data from single, high-expertise centers, raising concerns about their generalizability to routine clinical practice. In some cases, these algorithms may even produce misleading results. Additionally, the "black box" nature of certain AI systems poses challenges for cardiologists, making discrepancies difficult to interpret or rectify. Importantly, AI should be seen as a complementary tool rather than a replacement for cardiologists, designed to expedite routine tasks and allow clinicians to focus on complex cases. Future challenges include fostering clinician involvement in algorithm development and extending AI implementation to peripheral healthcare centers. This approach aims to enhance accessibility, understanding, and applicability of AI in everyday clinical practice, ultimately democratizing its benefits and ensuring equitable integration into healthcare systems.

Automatic Detection of Standard Planes in Fetal Ultrasound Images based on Convolutional Neural Networks and Ensemble Learning

Introduction: The wide application of artificial intelligence in various fields has shown its potential to aid medical diagnosis. Ultrasound is an important tool used to evaluate fetal development and diagnose fetal diseases. Methods: However, traditional diagnostic methods are time-consuming and laborious. Therefore, we constructed an end-to-end automatic diagnosis system based on convolutional neural networks using ensemble learning to improve the robustness and accuracy of the system. Results: The system classifies the ultrasound image dataset into six categories, namely, abdomen, brain, femur, thorax, maternal cervix, and other planes. Conclusion: After experiments, the results showed that the proposed end-to-end system can considerably improve the detection accuracy of the standard plane.

The application of artificial intelligence in full arch computer assisted implant surgery - A clinical study

The application of artificial intelligence in full arch computer assisted implant surgery - A clinical study

Applications of artificial intelligence in oncology patient education: A scoping review.

e13642 Background: As cancer care becomes increasingly complex, artificial intelligence (AI) tools have the potential to improve patient understanding, personalize communication, and support shared decision-making. Reviewing current literature on these applications is crucial to identifying effective strategies and addressing gaps in implementing AI-driven interventions in oncology care. Methods: We searched PubMed, EMBASE, Cochrane, Medline, and World of Science from January 2015 through November 2024 for original studies on implementations of AI in patient-facing oncology education. Abstracts and short communications were excluded. Data extraction included date of publication, cancer type(s), AI model type(s), study design, purpose, and findings. Reported score metrics included accuracy, comprehensibility, and readability of AI-generated education materials. Results: 1,437 publications were identified based on search terms, of which 77 studies were included. 59/77 (77%) studies were published in 2024. 13/77 (17%) focused on general oncology education, including symptom/pain control and treatment toxicities. 64/77 (83%) focused on addressing patient inquiries within specific cancer types, with prostate (20), breast (16), and colorectal (8) being the most common. 72/77 studies (94%) utilized some form of large language model (LLM) to summarize and deliver education materials, most of which used publicly available chatbots such as ChatGPT 3.5/4.0, Google Gemini, and Microsoft Copilot. The remaining studies used AI to build interactive platforms, virtual coaching assistants, and educational video generators. The role of AI in patient education in oncology is currently limited to (1) responding to patient questions, and (2) simplifying existing medical guidelines and texts to be more patient-friendly. Congregated data shows that AI-produced information has an overall accuracy of 81% (IQR 75%-87%) and comprehensibility of 83% (IQR 75%-90%). 21 studies used AI to improve readability of patient education materials, achieving an overall reduction in reading level from “post-college grad” to “high-school” (based on Flesch-Kincaid Grading). Only 2 studies achieved AMA and NIH’s recommendation of sixth-grade reading level. Conclusions: AI introduced novel and creative approaches to delivering oncology information to patients, although caution is needed due to limitations in accuracy and comprehensibility. In particular, AI-enhanced readability may still exceed the recommended level for patient education materials, highlighting the need for further optimization. Future research will be essential to refining AI tools and maximizing their clinical impact.

Applications of artificial intelligence in industry 4.0 and smart manufacturing

Applications of artificial intelligence in industry 4.0 and smart manufacturing

Application of artificial intelligence and machine learning in expert systems for the mining industry: modern methods and technologies

The mining industry has changed significantly in recent decades with the introduction of advanced technologies such as artificial intelligence (AI) and machine learning (ML). These innovations contribute to the creation of expert systems that help in optimizing processes, increasing the safety and sustainability of operations. This article is a literature review of modern AI and ML methods and technologies used in the mining industry. Discusses various intelligent and expert systems used to improve productivity, reduce operating costs, improve occupational safety, environmental sustainability, machine automation, predictive analytics, quality monitoring and control, and inventory and logistics management. The advantages and disadvantages of different approaches are analyzed, as well as their potential impact on the future of the mining industry. The review highlights the importance of integrating AI and ML into mining processes to achieve more efficient and safer solutions.

Artificial intelligence to predict cancer risk, are we there yet? A comprehensive review across cancer types.

Artificial intelligence to predict cancer risk, are we there yet? A comprehensive review across cancer types.

Advancing the frontier of artificial intelligence on emerging technologies to redefine cancer diagnosis and care.

Advancing the frontier of artificial intelligence on emerging technologies to redefine cancer diagnosis and care.

Access to technology, access to justice: China’s artificial intelligence application in criminal proceedings

Access to technology, access to justice: China’s artificial intelligence application in criminal proceedings

Perceptions, attitudes, and concerns about artificial intelligence applications in patients with cancer.

e13626 Background: The use of artificial intelligence (AI) in oncology has increased rapidly, spanning applications such as pathology, radiology, diagnostics, prognosis, genomics, treatment planning, and clinical trials. However, perspectives, comfort levels, and concerns about AI in cancer care remain largely unexplored. Methods: This descriptive cross-sectional survey study was conducted between May 20 and October 22, 2024, among 363 patients with cancer from two different hospitals affiliated with Ankara University, a tertiary care center in Turkey. Results: A majority believed AI would somewhat (32%) or significantly (18.7%) improve healthcare. One-third (33.1%) felt very uncomfortable with AI diagnosing cancer, with higher discomfort among less-educated participants (p < 0.005). Concerns included incorrect diagnoses (31.1%), healthcare costs (27.5%), and data privacy (19.6%). Patients with higher education levels expressed less discomfort and fewer concerns. Conclusions: Patients' perspectives on AI varied significantly based on education, highlighting the need for targeted educational initiatives. While AI holds potential to revolutionize cancer care, addressing concerns about accuracy, security, and transparency is critical to enhance its acceptance and effectiveness in clinical practice. Cancer patients' concerns with artificial intelligence in healthcare. Question -Response Total (n=363) (%) Gender p-value Age p-value Education p-value Male (n=187)(%) Female (n=176)(%) 18–64 y(n=218)(%) ≥65 y (n=139)(%) Primary(n=191) (%) High School (n=72) (%) University (n=72)(%) Postgraduate(n=26)(%) 1. How concerned you are about the use of AI in medicine for each of the following My health information will not be kept confidential Not concerned 122 (33.6) 69 (36.9) 53 (30.1) X0.158 75 (34.4) 46 (33.1) 00.123 57 (29.8) 25 (34.7) 31 (43.1) 9 (34.6) 0.484 Somewhat concerned 170 (46.8) 88 (47.1) 82 (46.6) 93 (42.7) 72 (51.8) 94 (49.2) 32 (44.4) 28 (38.9) 14 (53.8) Very concerned 71 (19.6) 30 (16) 41 (23.3) 50 (22.9) 21 (15.1) 40 (20.9) 15 (20.8) 13 (18.1) 3 (11.5) The AI will make the wrong diagnosis Not concerned 65 (17.9) 35 (18.7) 30 (17) X0.496 39 (17.9) 25 (18) X0.031 31 (16.2) 16 (22.2) 13 (18.1) 5 (19.2) 0.086 Somewhat concerned 183 (50.4) 98 (52.4) 85 (48.3) 98 (45) 80 (57.6) 91 (47.6) 31 (43.1) 42 (58.3) 18 (69.2) Very concerned 115 (31.7) 54 (28.9) 61 (34.7) 81 (37.2) 34 (24.5) 69 (36.1) 25 (34.7) 17 (23.6) 3 (11.5) AI will mean I spend less time with my doctor Not concerned 66 (18.2) 38 (20.3) 28 (15.9) o.526 39 (17.9) 25 (18) X0.230 32 (16.8) 16 (22.2) 11 (15.3) 7 (26.9) 0.500 Somewhat concerned 184 (50.7) 91 (48.7) 93 (52.8) 103 (47.2) 77 (55.4) 96 (50.3) 33 (45.8) 39 (54.2) 15 (57.7) Very concerned 113 (31.1) 58 (31) 55 (31.3) 76 (34.9) 37 (26.6) 63 (33) 23 (31.9) 22 (30.6) 4 (15.4)

Machine Learning for Clinical Decision Support in the Neonatal Intensive Care Unit

The neonatal intensive care unit (NICU) is a data-rich environment that is an ideal setting for the implementation of machine learning (ML) and artificial intelligence (AI) in clinical decision support (CDS). Despite their potential, ML and AI applications are rarely used in clinical practice because of infrastructure and technical limitations. In this article, we review the technical requirements for data acquisition solutions, storage, and processing needed to handle the varied sources of data generated by hospitalized newborns. In addition, we describe the challenges for integrating structured and unstructured data from electronic health records, bedside monitors, imaging, and other sources and we consider the ethical and legal implications of using ML and AI for CDS. Finally, we emphasize that the study and application of ML and AI models in CDS requires rigorous research and quality improvement methodology. The NICUs that realize the potential of ML and AI in quality improvement and clinical research applications will be uniquely positioned to apply their findings to improve neonatal outcomes.

From inconsistent annotations to ground truth: Aggregation strategies for annotations of proximal carious lesions in dental imagery.

From inconsistent annotations to ground truth: Aggregation strategies for annotations of proximal carious lesions in dental imagery.

Navigating the Transformative Impact of Artificial Intelligence in Health Services Research.

Artificial intelligence (AI) is transforming health services research by providing novel insights, enhancing care quality, and improving patient outcomes. This review sought to assess AI's impact on health services research, highlighting its applications, benefits, and associated challenges. We conducted a comprehensive review of recent literature on AI applications in health services research. Key areas of focus included image processing and language processing. The review also addressed the ethical and practical challenges of integrating AI into healthcare. Over the past decade, AI-related research has markedly increased. AI has significantly advanced health services research by improving diagnostic precision, care quality, decision-making, hospital operations, and personalized care. The benefits of AI in image processing and language processing have been substantial, resulting in positive impacts on healthcare practices. However, integrating AI into healthcare presents considerable ethical and practical challenges, including the need for robust data security, the mitigation of algorithmic biases, and the achievement of interoperability among diverse data systems. AI offers significant potential to advance health services research and enhance patient care through powerful applications in image processing, language processing, diagnostic precision, decision-making, and hospital operations. By leveraging AI's capabilities, healthcare systems can achieve more personalized, efficient, and accurate care. Addressing key challenges is essential for the effective and equitable integration of AI into healthcare systems.

An overview of the applications of AI for detecting anatomical configurations in endodontics.

An overview of the applications of AI for detecting anatomical configurations in endodontics.

Artificial intelligence in fetal brain imaging: Advancements, challenges, and multimodal approaches for biometric and structural analysis.

Artificial intelligence in fetal brain imaging: Advancements, challenges, and multimodal approaches for biometric and structural analysis.

Early prediction of cardiovascular events following treatments in female breast cancer patients: Application of real-world data and artificial intelligence.

Early prediction of cardiovascular events following treatments in female breast cancer patients: Application of real-world data and artificial intelligence.