Potential Applications Of Artificial Intelligence Research Articles

The Saudi Community View of the Use of Artificial Intelligence in Health Care

Abstract Objectives: Artificial intelligence (AI) holds the promise to revolutionize the field of medicine and enhance the well-being of countless patients. Its capabilities span various areas, including disease prevention, accurate diagnosis, and the development of innovative treatments. Moreover, AI has the potential to streamline health-care delivery and lower expenses. The community should be aware of the potential applications of AI in health care, so that they can advocate for its development and adoption. Hence, the objective of this study is to assess the community’s perspectives regarding the utilization of AI in health care. Methods: A cross-sectional, questionnaire-based study was conducted in Saudi Arabia during the period of June to October 2023. The questionnaire was distributed to people on various social media platforms using a convenience sampling method. The collected data were analyzed using Statistical Package for the Social Sciences. Results: The study included 771 individuals, with 42.5% having a positive outlook on the use of AI in health care, 31.8% having a neutral view, and 7.5% having a negative view. The only factor associated with a positive opinion was regional differences (P = 0.006). Moreover, participants who used medical apps or programs (P = 0.026), wearables (P = 0.027), felt more confident in using technology (P < 0.001), enjoyed using technology (P < 0.001), found it easier to familiarize themselves with new devices or programs (P < 0.001), and had more knowledge about AI (P < 0.001) had more positive opinions regarding the use of AI in health care. Conclusions: The study found that most Saudis, especially those who were familiar with the use of technology, support the use of AI in health care, with a positive or neutral view. Yet, targeted campaigns in certain regions are needed to educate the entire community about AI’s potential benefits.

Using artificial intelligence to improve poultry productivity – a review

Abstract A recent study investigated the potential applications of artificial intelligence (AI) in poultry farming. One area where AI can be helpful is in the early detection of diseases. By analyzing data from various sources, such as sensor readings and health records, AI algorithms can identify potential disease outbreaks or health risks in flocks, allowing farmers to take timely preventive measures. Another area where AI can be applied is in controlling the environmental conditions of farms. By analyzing data from sensors that monitor temperature, humidity, ventilation, and lighting conditions, AI algorithms can help farmers create a comfortable and healthy environment for birds, improving their growth and reducing their stress. AI can also optimize the management of healthcare supplies for poultry. By analyzing the nutritional requirements of birds and the availability and prices of different ingredients, AI algorithms can help farmers optimize feed formulations, reducing waste and environmental impacts. Finally, the study explored the use of robots in poultry care. Robots can be used for cleaning, feeding, and monitoring individual birds. By automating these tasks, farmers can reduce labor costs and improve the efficiency of their operations. Overall, the study highlights the potential benefits of using AI and robotics in poultry farming, including early disease detection, improved environmental conditions, optimized feed formulations, and increased automation.

The Potential Applications Of Artificial Intelligence In Medical Field

The Potential Applications Of Artificial Intelligence In Medical Field

Potential Applications of Artificial Intelligence (AI) in Managing Polypharmacy in Saudi Arabia: A Narrative Review.

Prescribing medications is a fundamental practice in the management of illnesses that necessitates in-depth knowledge of clinical pharmacology. Polypharmacy, or the concurrent use of multiple medications by individuals with complex health conditions, poses significant challenges, including an increased risk of drug interactions and adverse reactions. The Saudi Vision 2030 prioritises enhancing healthcare quality and safety, including addressing polypharmacy. Artificial intelligence (AI) offers promising tools to optimise medication plans, predict adverse drug reactions and ensure drug safety. This review explores AI's potential to revolutionise polypharmacy management in Saudi Arabia, highlighting practical applications, challenges and the path forward for the integration of AI solutions into healthcare practices.

Diagnostic power of ChatGPT 4 in distal radius fracture detection through wrist radiographs.

Distal radius fractures rank among the most prevalent fractures in humans, necessitating accurate radiological imaging and interpretation for optimal diagnosis and treatment. In addition to human radiologists, artificial intelligence systems are increasingly employed for radiological assessments. Since 2023, ChatGPT 4 has offered image analysis capabilities, which can also be used for the analysis of wrist radiographs. This study evaluates the diagnostic power of ChatGPT 4 in identifying distal radius fractures, comparing it with a board-certified radiologist, a hand surgery resident, a medical student, and the well-established AI Gleamer BoneView™. Results demonstrate ChatGPT 4's good diagnostic accuracy (sensitivity 0.88, specificity 0.98, diagnostic power (AUC) 0.93), surpassing the medical student (sensitivity 0.98, specificity 0.72, diagnostic power (AUC) 0.85; p = 0.04) significantly. Nevertheless, the diagnostic power of ChatGPT 4 lags behind the hand surgery resident (sensitivity 0.99, specificity 0.98, diagnostic power (AUC) 0.985; p = 0.014) and Gleamer BoneView™(sensitivity 1.00, specificity 0.98, diagnostic power (AUC) 0.99; p = 0.006). This study highlights the utility and potential applications of artificial intelligence in modern medicine, emphasizing ChatGPT 4 as a valuable tool for enhancing diagnostic capabilities in the field of medical imaging.

Artificial Intelligence (AI)-driven Transformation: Sustainable Development of Agro-based Industries in Bihar

Digital technologies particularly Artificial Intelligence (AI), are needed to transform agricultural sector in Bihar. There is a vast opportunity in adopting AI-driven solutions to enhance productivity and sustainability as well as increase the profitability of agriculture in Bihar. The current status and potential applications of AI in agriculture will be reviewed, challenges and opportunities specific to regions identified, together with development of strategies for maximum gain from AI technologies. We used the bibliographic methods and SWOT analysis. The growth of Bihar’s GDP as the adoption of Internet of Thing (IoT), precision farming and AI is projected at 5-6% per year that may create up to 30% more job opportunities in agricultural sector by 2024.Precision agriculture combined with crop yield forecasting is among key applications of AI in agro industries, labor optimization and efficiency; soil monitoring and management; disease detection and pest management; weather forecasting for climate risk management purposes among others include weed identification herbicide optimization supply chain optimization. SWOT analysis reveals strength such as enhanced productivity, cost reduction, improved decision-making ability on farm operations and climate resilience improvement strategy while helps farmers accessing information at their fingertips. However, weaknesses include infrastructure limitations, skill gaps and data accessibility issues. Opportunities arise from government support, research and development initiatives and market expansion. Threats encompass the digital divide, privacy and data security concerns, and dependence on external factors. The integration of AI holds substantial relevance for Bihar offering a pathway to overcome existing agricultural challenges and unlock new growth opportunities.

An insight into the Application of AI in maritime and Logistics toward Sustainable Transportation

This review article looks at the developing field of artificial intelligence and machine learning in maritime and marine environment management. The marine industry is increasingly interested in applying advanced AI and ML technologies to solve sustainability, efficiency, and regulatory compliance issues. This paper examines maritime and marine AI and ML applications using a deep literature review and case study analysis. Modeling ship fuel consumption, which impacts the environment and operating expenses, is a top responsibility. The study demonstrates that ML approaches such as Random Forest and Tweedie models can estimate ship fuel use. Statistical analysis demonstrates that the Random Forest model beats the Tweedie model regarding accuracy and consistency. For the training and testing datasets, the Random Forest model has high R2 values of 0.9997 and 0.9926, indicating a solid match. Low Root Mean Square Error (RMSE) and average absolute relative deviation (AARD) suggest that the model accurately reflects fuel use variability. While still performing well, the Tweedie model has lower R2 values and higher RMSE and AARD values, suggesting reduced accuracy and precision in fuel consumption prediction. These findings provide light on the potential applications of artificial intelligence and machine learning in maritime and marine environment management. Advanced analytics enables decision-makers to analyze fuel consumption patterns better, increase operational efficiency, and decrease environmental impact, thus improving maritime sustainability.

Application of Artificial Intelligence in Early Detection of Epidural Hematoma

Head injury is an emergency condition that is still the leading cause of death. Manifestations of head injury may be accompanied by intracranial hemorrhage, one of which is epidural hemorrhage or epidural hematoma (EDH). This study aims to explore the potential application of artificial intelligence in the early detection of epidural hematoma with the main objective of improving early diagnosis and management of this condition. This paper uses a literature review study method derived from the analysis of various references. The references used have inclusion criteria in the form of full text type, related to the topic of discussion using "Artificial Intelligence", "Deep learning", and "epidural hematome". The result of this study is that EWS finds the presence of EDH in two or more images from a set of CT-Scans, the system will send an email to the medical practitioner with an attachment of images showing EDH for immediate action. In previous tests, the system successfully diagnosed 13 out of 27 patients with EDH, with 85% of the diagnoses having a high level of importance. This shows that the EWS has the potential to improve early detection and management of EDH cases, as well as provide medical practitioners with important information for appropriate action.

New possibilities of artificial intelligence in medicine: a narrative review

   The purpose of the narrative review is to provide a descriptive analysis of the emerging capabilities of artificial intelligence (AI) to improve the diagnosis, prevention and treatment of various diseases.   The article discusses which modern AI tools can be used in clinical practice, healthcare organization and medical education. The paper considers various aspects of medical AI systems, which are mainly computer support systems for medical decision-making in the process of clinical work. Much attention is paid to the possibilities of generative AI in medicine. Potential applications of AI in clinical practice have been investigated, highlighting promising prospects for both practitioners and their patients. The limitations associated with the use of AI in various fields of medicine are described, and possible ways of solving them are suggested. The problems of information security and ethical constraints associated with the introduction of AI are outlined. The broad integration of AI into public health will enhance clinical and management decision support, speed up disease diagnosis, and improve the overall quality and accessibility of healthcare services.

Shaping the future of AI in healthcare through ethics and governance

The purpose of this research is to identify and evaluate the technical, ethical and regulatory challenges related to the use of Artificial Intelligence (AI) in healthcare. The potential applications of AI in healthcare seem limitless and vary in their nature and scope, ranging from privacy, research, informed consent, patient autonomy, accountability, health equity, fairness, AI-based diagnostic algorithms to care management through automation for specific manual activities to reduce paperwork and human error. The main challenges faced by states in regulating the use of AI in healthcare were identified, especially the legal voids and complexities for adequate regulation and better transparency. A few recommendations were made to protect health data, mitigate risks and regulate more efficiently the use of AI in healthcare through international cooperation and the adoption of harmonized standards under the World Health Organization (WHO) in line with its constitutional mandate to regulate digital and public health. European Union (EU) law can serve as a model and guidance for the WHO for a reform of the International Health Regulations (IHR).

Living review framework for better policy design and management of hazardous waste in Australia

The significant increase in hazardous waste generation in Australia has led to the discussion over the incorporation of artificial intelligence into the hazardous waste management system. Recent studies explored the potential applications of artificial intelligence in various processes of managing waste. However, no study has examined the use of text mining in the hazardous waste management sector for the purpose of informing policymakers. This study developed a living review framework which applied supervised text classification and text mining techniques to extract knowledge using the domain literature data between 2022 and 2023. The framework employed statistical classification models trained using iterative training and the best model XGBoost achieved an F1 score of 0.87. Using a small set of 126 manually labelled global articles, XGBoost automatically predicted the labels of 678 Australian articles with high confidence. Then, keyword extraction and unsupervised topic modelling with Latent Dirichlet Allocation (LDA) were performed. Results indicated that there were 2 main research themes in Australian literature: (1) the key waste streams and (2) the resource recovery and recycling of waste. The implication of this framework would benefit the policymakers, researchers, and hazardous waste management organisations by serving as a real time guideline of the current key waste streams and research themes in the literature which allow robust knowledge to be applied to waste management and highlight where the gap in research remains.

Reviewing the Potential Role of Artificial Intelligence in Delivering Personalized and Interactive Pain Medicine Education for Chronic Pain Patients.

The integration of artificial intelligence (AI) in patient pain medicine education has the potential to revolutionize pain management. By harnessing the power of AI, patient education becomes more personalized, interactive, and supportive, empowering patients to understand their pain, make informed decisions, and actively participate in their pain management journey. AI tailors the educational content to individual patients' needs, providing personalized recommendations. It introduces interactive elements through chatbots and virtual assistants, enhancing engagement and motivation. AI-powered platforms improve accessibility by providing easy access to educational resources and adapting content to diverse patient populations. Future AI applications in pain management include explaining pain mechanisms, treatment options, predicting outcomes based on individualized patient-specific factors, and supporting monitoring and adherence. Though the literature on AI in pain medicine and its applications are scarce yet growing, we propose avenues where AI may be applied and review the potential applications of AI in pain management education. Additionally, we address ethical considerations, patient empowerment, and accessibility barriers.

Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review.

Healthcare-associated infections (HAIs) are the most common adverse events in healthcare and constitute a major global public health concern. Surveillance represents the foundation for the effective prevention and control of HAIs, yet conventional surveillance is costly and labor intensive. Artificial intelligence (AI) and machine learning (ML) have the potential to support the development of HAI surveillance algorithms for the understanding of HAI risk factors, the improvement of patient risk stratification as well as the prediction and timely detection and prevention of infections. AI-supported systems have so far been explored for clinical laboratory testing and imaging diagnosis, antimicrobial resistance profiling, antibiotic discovery and prediction-based clinical decision support tools in terms of HAIs. This review aims to provide a comprehensive summary of the current literature on AI applications in the field of HAIs and discuss the future potentials of this emerging technology in infection practice. Following the PRISMA guidelines, this study examined the articles in databases including PubMed and Scopus until November 2023, which were screened based on the inclusion and exclusion criteria, resulting in 162 included articles. By elucidating the advancements in the field, we aim to highlight the potential applications of AI in the field, report related issues and shortcomings and discuss the future directions.

The Washington Climate Assembly: note-taking modalities as deliberative guidance in an online citizens’ assembly

ABSTRACT In this essay, we use applied rhetorical criticism to analyze how facilitators navigate online deliberation in the Washington Climate Assembly, a public deliberation process held virtually in January and February 2021. We draw on interviews with organizers and facilitators and from observational notes taken by the research team during Assembly deliberations. This analysis focuses on the Assembly's online note-taking practices. Previous research has recognized note-taking as an important element to the design of democratic processes, and we argue that note-taking modalities constitute a form of deliberative guidance. We examine the Assembly’s use of note-taking modalities for deliberative guidance across spatial and temporal dimensions, thus expanding the concept of deliberative guidance into other forms of communication. Additionally, results revealing shortcomings in note-taking suggest employing augmented note-taking practices during online public deliberation processes, with implications for scoping, staffing, and budgeting of such processes, as well as the potential application of artificial intelligence.

Designing AI for mental health diagnosis: challenges from sub-Saharan African value-laden judgements on mental health disorders

Recently clinicians have become more reliant on technologies such as artificial intelligence (AI) and machine learning (ML) for effective and accurate diagnosis and prognosis of diseases, especially mental health disorders....

Artificial intelligence and neurosurgery: a revolution in the field

Artificial Intelligence (AI) is being used in the field of neurosurgery for improving patient outcomes, reducing the risk of complications, and increasing the efficiency of surgical procedures. AI algorithms can analyze patient data, plan surgical procedures, guide surgical instruments, monitor brain activity, and improve post-operative care. The benefits of incorporating AI into neurosurgical practice include pre-operative planning, intraoperative navigation, real-time monitoring, and post-operative care. AI is already being used in neurosurgery for image segmentation, surgical planning, intraoperative navigation, real-time monitoring, and predictive analytics. The potential applications of AI in neurosurgery include personalized medicine, virtual reality, robotic surgery, predictive analytics, and medical imaging. However, the challenges of incorporating AI into neurosurgical practice are data quality, data privacy and security, regulatory frameworks, and training and education. In short, AI has the potential to completely transform the discipline of neurosurgery, but there is a need to address the challenges associated with its incorporation into neurosurgical practice.

Basic principles of artificial intelligence in dermatology explained using melanoma.

The use of artificial intelligence (AI) continues to establish itself in the most diverse areas of medicine at an increasingly fast pace. Nevertheless, many healthcare professionals lack the basic technical understanding of how this technology works, which severely limits its application in clinical settings and research. Thus, we would like to discuss the functioning and classification of AI using melanoma as an example in this review to build an understanding of the technology behind AI. For this purpose, elaborate illustrations are used that quickly reveal the technology involved. Previous reviews tend to focus on the potential applications of AI, thereby missing the opportunity to develop a deeper understanding of the subject matter that is so important for clinical application. Malignant melanoma has become a significant burden for healthcare systems. If discovered early, a better prognosis can be expected, which is why skin cancer screening has become increasingly popular and is supported by health insurance. The number of experts remains finite, reducing their availability and leading to longer waiting times. Therefore, innovative ideas need to be implemented to provide the necessary care. Thus, machine learning offers the ability to recognize melanomas from images at a level comparable to experienced dermatologists under optimized conditions.

Utility and Comparative Performance of Current Artificial Intelligence Large Language Models as Postoperative Medical Support Chatbots in Aesthetic Surgery.

Large Language Models (LLMs) have revolutionized the way plastic surgeons and their patients may access and leverage artificial Intelligence (AI). The present study aims to comparatively assess the performance of two current publically-available and patient-accessible LLMs in the potential application of AI as postoperative medical support chatbots in an aesthetic surgeon's practice. Twenty-two simulated postoperative patient presentations following aesthetic breast plastic surgery were devised and expert-validated. Complications varied in their latency within the postoperative period, as well as urgency of required medical attention. In response to each patient-reported presentation, Open AI's ChatGPT and Google's Bard, in their unmodified and freely available versions, were objectively assessed for their comparative accuracy in generating an appropriate differential diagnosis, most likely diagnosis, suggested medical disposition, treatments or interventions to begin from home, and/or red flag signs/symptoms indicating deterioration. ChatGPT cumulatively and significantly outperformed Bard across all objective assessement metrics examined (66% vs. 55%, respectively; p < 0.05). Accuracy in generating an appropriate differential diagnosis were 61% for ChatGPT, and 57% for Bard (p = 0.45). ChatGPT asked an average of 9.2 questions on history, relative to 6.8 questions by Bard (p < 0.001), following which, accuracies of 91% vs. 68% at arriving at the most-likely diagnosis were noted, respectively (p < 0.01). Appropriate medical dispositions were suggested with an accuracy of 50% by ChatGPT, and 41% by Bard (p = 0.40); relevant home interventions/treatments with an accuracy of 59% and 55% (p = 0.94), and red flag signs/symptoms with accuracies of 79% and 54% (p < 0.01), respectively. Detailed and comparative performance breakdowns according to complication latency and urgency are presented herein. ChatGPT represents the superior LLM for the potential application of AI technology in postoperative medical support chatbots. Imperfect performance and limitations identified herein may guide the necessary refinement to facilitate adoption.

Study of the role that AI can play in the Sustainable Fashion Business

This study investigates the potential application of artificial intelligence (AI) in the field of sustainable fashion by streamlining the production of clothing and incorporating trend analysis. By researching current fashion trends and consumer preferences to identify the most well-liked apparel patterns and designs, the article seeks to optimise supply and demand while reducing surplus production. Fashion garment production can be optimised to fulfil customer demand while minimising waste and lowering costs by incorporating trend research into the manufacturing process. Companies may now invest in clothing concepts that will sell thanks to AI trend forecasting, which reduces some of the uncertainty and human error that now hinder trend forecasting. The fashion industry is infamous for its detrimental effects on society and the environment, including creation of waste and pollution. While the fashion industry has started to adopt more sustainable practises, AI has the ability to quicken the process by offering creative answers to some of the sector's most pressing problems. The study looks at a number of AI-related case studies in the fashion sector, including the use of predictive analytics to cut waste and streamline supply chains, computer vision to enhance textile recycling, and natural language processing to encourage openness and moral work practises. The paper also examines how AI could revolutionise the fashion business and hasten the shift to a more ethical and sustainable future. The report also highlights the necessity for careful evaluation of these concerns as the fashion industry develops by posing significant ethical and social implications for AI.

The potential application of artificial intelligence in veterinary clinical practice and biomedical research.

Artificial intelligence (AI) is a fast-paced technological advancement in terms of its application to various fields of science and technology. In particular, AI has the potential to play various roles in veterinary clinical practice, enhancing the way veterinary care is delivered, improving outcomes for animals and ultimately humans. Also, in recent years, the emergence of AI has led to a new direction in biomedical research, especially in translational research with great potential, promising to revolutionize science. AI is applicable in antimicrobial resistance (AMR) research, cancer research, drug design and vaccine development, epidemiology, disease surveillance, and genomics. Here, we highlighted and discussed the potential impact of various aspects of AI in veterinary clinical practice and biomedical research, proposing this technology as a key tool for addressing pressing global health challenges across various domains.