Assist Decision Makers Research Articles

Geographic Information System for Shortest Route Search for Clinics in Pamekasan Regency Using the Djikstra Method

Earthquake natural disasters can cause significant infrastructure damage, hindering community access and mobility. In such a situation, determining the shortest path for evacuation becomes very important. This research uses linear programming by utilising Excel Solver to determine the shortest evacuation route for earthquake natural disasters. Excel Solver is a feature in Microsoft Excel that serves as an analytical tool to solve linear optimisation problems. Excel solver can be used to find solutions that maximise or minimise the objective function, with respect to predetermined constraints. This research utilises relevant earthquake natural disaster simulation data as constraints in the optimisation model. The results show that the model can generate shortest paths that fulfil various constraints and criteria. The model can also be used to predict the time required to reach the destination. This research makes an important contribution to the development of tools and methods for shortest path optimisation in natural disaster scenarios. The optimisation model developed in this research can be used to assist decision makers in improving the efficiency and effectiveness of evacuation and disaster relief operations.

Implementasi Kombinasi LOPCOW dan Operational Competitiveness Rating Analysis Dalam Rekomendasi Tempat Wisata Indoor

Indoor attractions offer an exciting and comfortable holiday experience, especially for those who want to avoid extreme weather or want indoor comfort. The selection of indoor attractions is often faced with various problems involving distance, price, rating, and cleanliness. All these factors must be carefully considered to ensure that the chosen indoor attraction can provide a satisfying and enjoyable experience. The application of a combination of Logarithmic Percentage Change-Driven Objective Weighting (LOPCOW) and Operational Competitiveness Rating Analysis (OCRA) in the performance evaluation of business alternatives offers an innovative and robust approach to decision making. LOPCOW allows objective determination of criteria weighting based on logarithmic percentage changes, emphasizing the dynamics of relative performance changes between criteria. Integration with OCRA, which evaluates operational competitiveness through efficient ratio analysis. The ranking results showed that the first best rank was obtained by Puncak Mas with a final value of 1.2596, the second-best rank was obtained by Wira Garden with a final value of 1.1607, the third best rank was obtained by Lampung Walk with a final value of 1.0289. The combination of these two methods increases the accuracy and reliability of ranking results, assisting decision makers in choosing truly superior alternatives based on relevant and up-to-date data. By utilizing data-driven analysis and robust methodologies, the decision-making process becomes more efficient and can be done faster, saving time and resources.

Sistem Pendukung Keputusan Pemilihan Media Pembelajaran online Terbaik Menggunakan Metode Smart pada Perguruan Tinggi LLDIKTI Wilayah IX

Along with the development of information and communication technology (ICT), in the current digital era, various kinds of learning media have emerged that can be used by higher education institutions in carrying out the teaching and learning process, including audio media, still visual media, moving visual media, audio visual media and many others. It depends on each university you want to study. SPK is designed to assist decision makers in solving both semi-structured and/or unstructured problems and focuses on presenting information that can later be used as the best choice/alternative for decision making. Online learning was first known due to the influence of the development of electronic-based learning (e-learning) introduced by the University of Illinois through a computer-based learning system. Simple Multi-Attribute Rating (SMART) is a comprehensive decision-making model that considers qualitative and quantitative aspects. The aim of this research is to make it easier for LLDIKITI IX to get information about the best online learning media, so that it can be a recommendation to universities that must be used in the online teaching and learning process

Palliative Care Acceptability for Older Adults with Advanced CKD: A Qualitative Study of Patients and Nephrologists

Older adults in the United States often receive kidney therapies that do not align with their goals. Palliative care (PC) specialists are experts in assisting patients with the goals of care discussions and decision support, yet views and experiences of older patients who have received PC while contemplating kidney therapy decisions and their nephrologists remain unexplored. We evaluated the acceptability of CKD-EDU, a PC-based kidney therapy decision support intervention for adults≥75 years of age. Qualitative study. Two trained research coordinators interviewed patients and nephrologists participating in the CKD-EDU study. Three coders analyzed the qualitative data using a thematic analysis approach to identify salient themes pertaining to intervention acceptability. Patients (n=19; mean age: 80 years) viewed the PC intervention favorably, noting PCphysicians' excellent communication skills, whole-person care, and decision-making support, including comprehension of prognostic information. Nephrologists (n=24; mean age) welcomed PC assistance in decision making, support for conservative kidney management, and symptom management; a minority voiced concerns about third-party involvement in their practice. Single-center study. Overall, patients and nephrologists generally found the PC intervention to beacceptable. Future testing of the current PC-based decision support intervention in a larger randomized controlled trial for older people navigating kidney therapy decisions is needed.

Ecological Evaluation of Land Resources in the Yangtze River Delta Region by Remote Sensing Observation

The evaluation of land ecological security (LES) evaluates how human activity and land use affect land ecosystems. Its ultimate objective is to provide guidance and assistance for decision making in order to preserve and restore the efficacy and health of terrestrial ecosystems. The assessment model presented in this article is comprehensive and integrates the advantages of both subjective and objective weighting techniques. This study extends the “Pressure–State–Response” (PSR) model to “Driver–Pressure–State-Impact–Response” (DPSIR) and combines it with TOPSISI to determine the weights of each contributing component. Furthermore, the geographical and temporal distribution patterns of regional land ecological security levels were investigated using GIS geostatistical approaches. According to this study, (1) the Yangtze River Delta region’s LES index, with a mean value in the fairly safe range, is generally safe. The year 2019 marks an inflection point for the index, with the highest level of ecological safety on land. The primary element is the modification of environmental policies that are enacted by the government. (2) The LES status is divided into two stages during the course of this study. The Yangtze River Delta region’s LES quickly develops throughout the first stage (2012–2019), which sees a shift in the safety rating from IV to II. The second stage (2019–2023) sees a progressive improvement in the LES index and a shift in the safety category from Class II to Class I. (3) Important variables influencing the geographical distribution of LES in the Yangtze River Delta region include barrier elements, including soil and water erosion areas, flood disaster areas, grain planting areas, urban green covering areas, and effective irrigation areas of farmland.

Dilated lenticulostriate artery on whole-brain vessel wall imaging differentiates pathogenesis and predicts clinical outcomes in single subcortical infarction.

This study aimed to investigate the dilation of lenticulostriate artery (LSA) identified by whole-brain vessel wall imaging (WB-VWI) in differentiating the etiologic subtypes of single subcortical infarction (SSI) and to determine whether the appearance of dilated LSA was associated with 90-day clinical outcomes in parental atherosclerotic disease (PAD)-related SSI. Patients with acute SSI were prospectively enrolled and categorized into PAD-related SSI and cerebral small-vessel disease (CSVD)-related SSI groups. The imaging features of LSA morphology (branches, length, dilation, and tortuosity), plaques (burden, remodeling index, enhancement degree, and hyperintense plaque), and CSVD (white matter hyperintensity, lacunes, cerebral microbleed, and enlarged perivascular space) were evaluated. The logistic regression was performed to determine the association of dilated LSA with PAD-related SSI and 90-day clinical outcomes. In total, 131 patients (mean age, 52.2 ± 13.2 years; 99 men) were included. The multivariate logistic regression analysis revealed that the presence of dilated LSAs (odds ratio (OR), 7.40; 95% confidence interval (CI): 1.88-29.17; p = 0.004)) was significantly associated with PAD-related SSI. Moreover, after adjusting for confounding factors, the association of poor outcomes with the total length of LSAs (OR, 0.94; 95% CI: 0.90-0.99; p = 0.011), dilated LSAs (OR, 0.001; 95% CI: 0.0001-0.08; p = 0.002), and plaque burden (OR, 1.35; 95% CI: 1.11-1.63; p = 0.002) remained statistically significant. The dilation of LSA visualized on WB-VWI could differentiate various subtypes of SSI within LSA territory and was a prognostic imaging marker for 90-day clinical outcomes for PAD-related SSI. Evaluation of LSA morphology based on WB-VWI can differentiate the pathogenesis and predict clinical outcomes in SSI, providing crucial insights into the etiologic mechanisms, risk stratification, and tailored therapies for these patients. The prognosis of SSIs within lenticulostriate territory depend on the etiology of the disease. LSA dilation on WB-VWI was associated with parental atherosclerosis and better 90-day outcomes. Accurately identifying the etiology of SSIs in lenticulostriate territory assists in treatment decision-making.

Using the Discharge Decision-Making System to Improve the Pressure on Family Members of Ventilator-Dependent Patients.

The relevant regulations for preparing ventilator-dependent patients for discharge to institutions or home care are cumbersome and there are numerous health and education materials, which make family members confused and make the decision to discharge very difficult. The purpose of this study is to explore the use of ventilator-dependent family members in critical care units. Discussion on the pressure before and after "Discharge Decision Assistance System web APP".

Optimisation of Evacuation Route Determination in an Earthquake Natural Disaster Scenario Using an Excel Solver

Earthquake natural disasters can cause significant infrastructure damage, hindering community access and mobility. In such a situation, determining the shortest path for evacuation becomes very important. This research uses linear programming by utilising Excel Solver to determine the shortest evacuation route for earthquake natural disasters. Excel Solver is a feature in Microsoft Excel that serves as an analytical tool to solve linear optimisation problems. Excel solver can be used to find solutions that maximise or minimise the objective function, with respect to predetermined constraints. This research utilises relevant earthquake natural disaster simulation data as constraints in the optimisation model. The results show that the model can generate shortest paths that fulfil various constraints and criteria. The model can also be used to predict the time required to reach the destination. This research makes an important contribution to the development of tools and methods for shortest path optimisation in natural disaster scenarios. The optimisation model developed in this research can be used to assist decision makers in improving the efficiency and effectiveness of evacuation and disaster relief operations.

Emerging Applications of Picture Archiving and Communication Systems and Their Impact on Research and Education: A Literature Review.

In recent times, technological advancements have remarkably improved picture archiving and communication system(PACS) capabilities beyond their conventional use in radiology departments. Researchers and instructors have started employing PACS functionalities to improve medical research processes, promote interdisciplinary collaborations, and facilitate learning. To illustrate this point further, the PACS enables researchers to handle and analyze huge amounts of imaging data with superior precision and speed, supporting innovative studies in areas like disease progression, treatment outcomes, and imaging modalities. Moreover, a PACSintegrated with artificial intelligence (AI) algorithms leads to significant improvements in image processing, diagnostic accuracy, and personalized treatment, thus marking a new approach to medical imaging. The PACSsupported by AI is mostly transformative since they allow for improved early disease detection capabilities as well as automated image processing and decision assistance, which increase diagnostic accuracy and clinical outcomes. Such systems can rapidly process large quantities of visual data with an accuracy rate surpassing earlier endeavors. In medical research, however, combining PACSwith AI allows challenging imaging datasets to be examined, thereby making findings that were not previously possible. The capacity to combine imaging outcomes with clinical information is valuable for medical students and professionals in the field of education. They can access extensive medical image collections and case studies using PACS. This link is critical for teaching and learning as it allows students to interact with concrete events and improve their diagnostic accuracy in a controlled environment. This review discusses how the PACS affects educational courses and clinical outcomes based on the available literature. Our aim was not only to outline recent research or developments but also to present a comprehensive overview regarding the growing role played by PACS in the modern healthcare system and academics. Similarly, we look at the challenges and opportunities associated with the wide adoption of PACS, highlighting possible future areas of study or teaching methodologies. Issues such as data security, interoperability, and the need for defined protocols are included to give an exhaustive understanding of what PACS can and cannot do. Through this study, we stress PACS's revolutionary potential in advancing research methodology and educational practices, eventually contributing to enhanced patient care and knowledge dissemination in healthcare areas. The continual growth of PACS technology and its applications is expected to reshape the landscape of medical research and education, making it a vital component in the quest for medical excellence. By knowing the present trends and future potential, stakeholders in healthcare and education may better employ PACS to reach their objectives and boost overall results.

A systematic review and research recommendations on artificial intelligence for automated cervical cancer detection

AbstractEarly diagnosis of abnormal cervical cells enhances the chance of prompt treatment for cervical cancer (CrC). Artificial intelligence (AI)‐assisted decision support systems for detecting abnormal cervical cells are developed because manual identification needs trained healthcare professionals, and can be difficult, time‐consuming, and error‐prone. The purpose of this study is to present a comprehensive review of AI technologies used for detecting cervical pre‐cancerous lesions and cancer. The review study includes studies where AI was applied to Pap Smear test (cytological test), colposcopy, sociodemographic data and other risk factors, histopathological analyses, magnetic resonance imaging‐, computed tomography‐, and positron emission tomography‐scan‐based imaging modalities. We performed searches on Web of Science, Medline, Scopus, and Inspec. The preferred reporting items for systematic reviews and meta‐analysis guidelines were used to search, screen, and analyze the articles. The primary search resulted in identifying 9745 articles. We followed strict inclusion and exclusion criteria, which include search windows of the last decade, journal articles, and machine/deep learning‐based methods. A total of 58 studies have been included in the review for further analysis after identification, screening, and eligibility evaluation. Our review analysis shows that deep learning models are preferred for imaging techniques, whereas machine learning‐based models are preferred for sociodemographic data. The analysis shows that convolutional neural network‐based features yielded representative characteristics for detecting pre‐cancerous lesions and CrC. The review analysis also highlights the need for generating new and easily accessible diverse datasets to develop versatile models for CrC detection. Our review study shows the need for model explainability and uncertainty quantification to increase the trust of clinicians and stakeholders in the decision‐making of automated CrC detection models. Our review suggests that data privacy concerns and adaptability are crucial for deployment hence, federated learning and meta‐learning should also be explored.This article is categorized under: Fundamental Concepts of Data and Knowledge > Explainable AI Technologies > Machine Learning Technologies > Classification

Who says “Ps get Degrees”? Examining the Profile of Undergraduate Students Maintaining High Achievement at University

The circumstances associated with high levels of achievement in undergraduate studies has not been thoroughly explored in the Australian context. This study investigated factors predicting high academic achievement, defined as maintaining a Distinction average, at undergraduate level. Findings revealed several factors that predicted achievement after two semesters of study. These factors included essay writing skills, with students having satisfactory writing 5.16 times more likely to maintain a Distinction average compared to those with below satisfactory writing; faculty, with students enrolled in Health Sciences 4.63 times more likely compared to students from other faculties; language background, with English-speaking background students 1.67 times more likely compared to English as an additional language or dialect (EAL/D) students; gender, with females 1.45 more likely compared to males; and age, with older students 1.03 times more likely to achieve high performance compared to younger students. Creating a profile of students likely to excel academically assists decision makers in allocating resources to students less likely to achieve. This research opens the door to further studies investigating whether these factors play a role in predicting student achievement at university.

Small Municipalities in the Amazon under the Risk of Future Climate Change

The focus of this work is on small municipalities (population below 50 thousand inhabitants) that cover around 87% of the territory of the Brazilian Legal Amazon (BLA). Based on a comprehensive integrated analysis approach using the three components hazard (climate extremes from CMIP6 future scenarios), exposure (directly affected population), and vulnerability (subdimensions of susceptibility and coping/adaptive capacity by using multidimensional indicators), the latter two using current datasets provided by the official Census IBGE 2022, we document a quantitative assessment of the risk R of natural disasters in the BLA region. We evidenced a worrying and imminent intensification of the curve of R in most Amazonian municipalities over the next two 25-year periods. The overall results of the highest proportions of R (total municipalities affected) pointed out the Amazonas, Roraima, Pará, and Maranhão as the main states, presenting projected categories of R high in the near future (2015 to 2039) and very high in the far future (2040 to 2064). The detailed assessment of the susceptibility and coping/adaptive capacity allowed us to elucidate the principal indicators that aggravate the degree of vulnerability: economy, the precariousness of urban infrastructure, medical services, communication, and urban mobility, whose combined factors, unfortunately, reveal a widespread poverty profile along the small Amazonian municipalities. Our scientific findings can assist decision makers in targeted strategies planning and public policies to minimize and mitigate ongoing and future climate change.

Estimating crop evapotranspiration of wheat-maize rotation system using hybrid convolutional bidirectional Long Short-Term Memory network with grey wolf algorithm in Chinese Loess Plateau region

Accurate estimation of crop evapotranspiration (ET) is essential for the efficient utilization of agricultural water resources, crop production enhancement, and sustainable agricultural development. However, direct measurement of ET is highly expensive, intricate, and time-consuming, highlighting the imperative of establishing a novel model to accurately estimate ET in agricultural ecosystems. To address the above problems, this study proposed a novel model (GWA-CNN-BiLSTM), which incorporates Grey Wolf Algorithm (GWA), Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory network (BiLSTM) as a hyperparameter adjuster, feature extractor, and regression component, respectively, to estimate ET built upon various input combinations comprising net solar radiation (Rn), vapor pressure deficit (VPD), average air temperature (Ta), soil water content (SWC), and leaf area index (LAI) about winter wheat-spring maize rotation system during 2012–2020 in the Loess Plateau. Besides, following a comparative assessment within GWA-CNN-BiLSTM, Convolutional Bidirectional Long Short-Term Memory network (CNN-BiLSTM), BiLSTM, Long Short-Term Memory network (LSTM), and Shuttleworth-Wallace (SW) models, the results revealed that GWA-CNN-BiLSTM under varied inputs obtained the superior performance, ranging from 0.562 to 0.957 in determination coefficient (R2), 8.4–41.5 % in relative root mean square error (RRMSE), 0.349 mm d−1 to 1.521 mm d−1 in mean absolute error (MAE), −3.26 % to 14.11 % in percent bias (PBIAS), and 0.820–1.091 in regression coefficient (b0), respectively. Moreover, while the accuracy of BiLSTM over LSTM was evident, its performance was notably improved by the incorporation of the CNN module. Additionally, LSTM-type models under complete input combination present better precision than SW by 29.7−51.4 % in R2, 44.2−76.1 % in RRMSE, and 33.6−63.4 % in MAE, respectively. Furthermore, the accuracy of all models under varied inputs exhibited excellence in winter wheat compared to spring maize, and corresponding improvements ranged 1.4−4.3 % in R2, 5.1−20.1 % in RRMSE, and 3.1−17.9 % in MAE, respectively. Besides, the meteorological factors (Rn, VPD, Ta) proved to be the most important inputs for ET estimation in winter wheat and spring maize. Wherein the importance of SWC exceeded that of LAI in winter wheat, while the opposite trend was observed in spring maize. In brief, GWA-CNN-BiLSTM is the highly recommended model to estimate ET of winter wheat-spring maize rotation system under diverse input data scenarios in the Loess Plateau, which can facilitate to offer valuable assistance in regional agriculture water management decisions.

Application of Fuzzy Decision Support Systems in Risk Assessment of Southeast Asian Labor Market

The labor market in Southeast Asia has several complicated issues, including the quick changes in the economy, the various behaviors of employees, and the shifting regulatory settings. To foster sustainable regional development and make decisions based on accurate information, a comprehensive risk assessment is important. Conventional methodologies, on the other hand, often fail to reflect the multifaceted personality of issues about the labor market. In light of recent economic changes and the complexity of applicable policies, this paper recommends a fuzzy decision support system to enhance risk assessment (FDSS-ERA) that could enhance risk assessment in Southeast Asian labor markets. The model methodically assesses hazards, using a mixture of fuzzy logic sense and decision assistance, incorporating changes in employment trends and demographics. The results highlight fuzzy logic’s role in decision-making for effective risk management and policy interventions, showing improved risk comprehension. While educated policy choice making can lead to equitable development in Southeast Asia’s labor work marketplace, FDSS is a potential method. In conclusion, to overcome the many obstacles that stand in the way of chance assessment in the labor market in Southeast Asia, FDSS provides a powerful strategy. Because it offers more advanced analytical tools, this research assists decision-makers in the region in developing more effective guidelines, proactively reducing risks, and achieving projects that promote sustainable development.

Multi-Objective Optimization: a Mathematical Model to Help Manage the Crop-Livestock-Forestry Integration System

Objective: The objective of this study is to develop a mathematical model based on multi-objective optimization to assist farmers who have adopted the Crop-Livestock-Forest Integration (ILPF) system, aiming to maximize production with resource limitations. Theoretical Framework: In this topic, sustainable agriculture, rural management, and mathematical optimization tools are discussed, providing a solid basis for understanding the research context. Method: It comprises an applied approach, considering the main inputs, costs and production revenues necessary to power the model. Microsoft Corporation's Excel Solver computational tool, version 7.3.5.2(x64)/LibreOffice Community, was used due to its accessibility and practicality for rural producers. Results and Discussion: The proposed mathematical model provides assistance in decision making, considering the distribution of areas for different productions with a view to achieving efficient results. In the discussion, these results are contextualized in light of the theoretical framework, highlighting the implications and relationships identified. Discrepancies and limitations of the study are also considered. Research Implications: The results can be applied to rural management and sustainable agriculture. These implications may include optimizing resources and improving the productive efficiency of farmers who use the ILPF system. Originality/Value: Considers the balance of economic, social and environmental aspects in the use of the ILPF system. The relevance and value of this research are evidenced by its potential to improve decision-making and maximize the efficient use of resources in sustainable agriculture.

Navigating autonomy and decision-making capacity: Legal and ethical considerations in Medical Assistance in Dying for individuals with mental disorders in Portugal

Portugal has recently amended its absolute prohibition on euthanasia and assisted suicide that now permits it conditionally and exonerates those providing this practice. People with a mental disorder or medical condition that affects their decision-making capacity (DMC), regardless of the mental disorder, its contemporaneity, or its connection to the decision itself are expressly excluded from this service unless they are assessed and deemed capacitous. In the absence of any statute concerning capacity or assisted decision-making, this legislation raises concerns about potential discrimination, conflicting with the presumption of capacity principle. A shift toward a DMC model is proposed. This model allows for the assessment of capacity for specific decisions, addressing the ethical implications of respecting or overriding a terminally ill person’s autonomous will for medical assistance in dying. This review paper examines the challenges of assessing DMC in depression and examines several questions of ethical and legislative nature for future consideration.

The role of chat generative pre-trained transformer in facilitating decision-making and the e-learning process in higher education

Digital technology and artificial intelligence technologies have been progressing rapidly, thus giving rise to intelligent chatbots such as chat generative pre-trained transformer (ChatGPT). These chatbots make searching for information more efficient and provide higher education institutions with assistance in decision-making. The goal of this research is to explore the capabilities of ChatGPT technology and its role in enhancing the e-learning process. Moreover, it seeks to determine whether ChatGPT can provide useful suggestions to improve the decision-making process in higher education. ChatGPT is effective in an e-learning environment for the following reasons: it facilitates personalized learning experiences, offers real-time support, and enhances decision-making by leveraging natural language processing capabilities. As suggested by the findings, ChatGPT has significant potential in higher education, as demonstrated by its ability to improve interactive participation, educational strategies, and educational outcomes. This study highlights the importance of incorporating ChatGPT into higher education settings to improve e-learning and decision-making.

Trust and reliance on AI — An experimental study on the extent and costs of overreliance on AI

Decision-making is undergoing rapid changes due to the introduction of artificial intelligence (AI), as AI recommender systems can help mitigate human flaws and increase decision accuracy and efficiency. However, AI can also commit errors or suffer from algorithmic bias. Hence, blind trust in technologies carries risks, as users may follow detrimental advice resulting in undesired consequences. Building upon research on algorithm appreciation and trust in AI, the current study investigates whether users who receive AI advice in an uncertain situation overrely on this advice — to their own detriment and that of other parties. In a domain-independent, incentivized, and interactive behavioral experiment, we find that the mere knowledge of advice being generated by an AI causes people to overrely on it, that is, to follow AI advice even when it contradicts available contextual information as well as their own assessment. Frequently, this overreliance leads not only to inefficient outcomes for the advisee, but also to undesired effects regarding third parties. The results call into question how AI is being used in assisted decision making, emphasizing the importance of AI literacy and effective trust calibration for productive deployment of such systems.

Research of Methods of Collision Warning and Avoidance Assistant Decision Making for the Ship in Typical Inland TSS Waters

Research of Methods of Collision Warning and Avoidance Assistant Decision Making for the Ship in Typical Inland TSS Waters

Revolutionizing Healthcare: The Transformative Power of Artificial Intelligence

Abstract: The field of radiology is changing due to artificial intelligence (AI), which presents hitherto unheard-of chances to improve diagnostic efficiency and accuracy. The transformational potential of AI in radiology is examined in this research, with particular attention to how it might expedite clinical workflows and completely change picture interpretation. The first section of the abstract emphasizes the rising need for radiological services as well as the difficulties radiologists have in organizing massive amounts of patient data while maintaining prompt and precise diagnosis. The article goes on to discuss AI as a potent tool that radiologists may use to analyze pictures more confidently, spot abnormalities, and make clinical choices more quickly. The transformational potential of artificial intelligence (AI) in radiology is examined in this research, with a focus on how AI might enhance diagnostic efficiency and accuracy. It presents the potential of artificial intelligence (AI) to transform radiological practice by highlighting its strengths in image interpretation, anomaly detection, and clinical decision assistance. But there are drawbacks to using AI in radiology, including concerns about data privacy and algorithm transparency. In order to guarantee patient safety and confidence in AI-enabled radiological techniques, the study highlights the significance of responsible AI implementation. The study concludes by highlighting the revolutionary effects of AI on radiology and highlighting its potential as a tool to improve healthcare delivery and diagnostic accuracy.