How can physicians adopt AI-based applications in the United Arab Emirates to improve patient outcomes?

Objective: The enabling and derailing factors for using artificial intelligence (AI)-based applications to improve patient care in the United Arab Emirates (UAE) from the physicians' perspective are investigated. Factors to accelerate the adoption of AI-based applications in the UAE are identified to aid implementation. Methods: A qualitative, inductive research methodology was employed, utilizing semi-structured interviews with 12 physicians practicing in the UAE. The collected data were analyzed using NVIVO software and grounded theory was used for thematic analysis. Results: This study identified factors specific to the deployment of AI to transform patient care in the UAE. First, physicians must control the applications and be fully trained and engaged in the testing phase. Second, healthcare systems need to be connected, and the AI outcomes need to be easily interpretable by physicians. Third, the reimbursement for AI-based applications should be settled by insurance or the government. Fourth, patients should be aware of and accept the technology before physicians use it to avoid negative consequences for the doctor-patient relationship. Conclusions: This research was conducted with practicing physicians in the UAE to determine their understanding of enabling and derailing factors for improving patient care through AI-based applications. The importance of involving physicians as the accountable agents for AI tools is highlighted. Public awareness regarding AI in healthcare should be improved to drive public acceptance.

Deep learning ensemble approach with explainable AI for lung and colon cancer classification using advanced hyperparameter tuning.

Lung and colon cancers are leading contributors to cancer-related fatalities globally, distinguished by unique histopathological traits discernible through medical imaging. Effective classification of these cancers is critical for accurate diagnosis and treatment. This study addresses critical challenges in the diagnostic imaging of lung and colon cancers, which are among the leading causes of cancer-related deaths worldwide. Recognizing the limitations of existing diagnostic methods, which often suffer from overfitting and poor generalizability, our research introduces a novel deep learning framework that synergistically combines the Xception and MobileNet architectures. This innovative ensemble model aims to enhance feature extraction, improve model robustness, and reduce overfitting.Our methodology involves training the hybrid model on a comprehensive dataset of histopathological images, followed by validation against a balanced test set. The results demonstrate an impressive classification accuracy of 99.44%, with perfect precision and recall in identifying certain cancerous and non-cancerous tissues, marking a significant improvement over traditional approach.The practical implications of these findings are profound. By integrating Gradient-weighted Class Activation Mapping (Grad-CAM), the model offers enhanced interpretability, allowing clinicians to visualize the diagnostic reasoning process. This transparency is vital for clinical acceptance and enables more personalized, accurate treatment planning. Our study not only pushes the boundaries of medical imaging technology but also sets the stage for future research aimed at expanding these techniques to other types of cancer diagnostics.

The implementation of robotic dogs in automatic detection and surveillance of red imported fire ant nests.

BACKGROUND: The Red Imported Fire Ant (RIFA), scientifically known as Solenopsis invicta, is a destructive invasive species causing considerable harm to ecosystems and generating substantial economic costs globally. Traditional methods for RIFA nests detection are labor-intensive and may not be scalable to larger field areas. This study aimed to develop an innovative surveillance system that leverages artificial intelligence (AI) and robotic dogs to automate the detection and geolocation of RIFA nests, thereby improving monitoring and control strategies. RESULTS: The designed surveillance system, through integrating the CyberDog robotic platform with a YOLOX AI model, demonstrated RIFA nest detection precision rates of >90%. The YOLOX model was trained on a dataset containing 1118 images and achieved a final precision rate of 0.95, with an inference time of 20.16 ms per image, indicating real-time operational suitability. Field tests revealed that the CyberDog system identified three times more nests than trained human inspectors, with significantly lower rates of missed detections and false positives. CONCLUSION: The findings underscore the potential of AI-driven robotic systems in advancing pest management. The CyberDog/YOLOX system not only matched human inspectors in speed, but also exceeded them in accuracy and efficiency. This study's results are significant as they highlight how technology can be harnessed to address biological invasions, offering a more effective, ecologically friendly, and scalable solution for RIFA detection. The successful implementation of this system could pave the way for broader applications in environmental monitoring and pest control, ultimately contributing to the preservation of biodiversity and economic stability. © 2024 Society of Chemical Industry.

Advancements in computer vision and pathology: Unraveling the potential of artificial intelligence for precision diagnosis and beyond.

The integration of computer vision into pathology through slide digitalization represents a transformative leap in the field's evolution. Traditional pathology methods, while reliable, are often time-consuming and susceptible to intra- and interobserver variability. In contrast, computer vision, empowered by artificial intelligence (AI) and machine learning (ML), promises revolutionary changes, offering consistent, reproducible, and objective results with ever-increasing speed and scalability. The applications of advanced algorithms and deep learning architectures like CNNs and U-Nets augment pathologists' diagnostic capabilities, opening new frontiers in automated image analysis. As these technologies mature and integrate into digital pathology workflows, they are poised to provide deeper insights into disease processes, quantify and standardize biomarkers, enhance patient outcomes, and automate routine tasks, reducing pathologists' workload. However, this transformative force calls for cross-disciplinary collaboration between pathologists, computer scientists, and industry innovators to drive research and development. While acknowledging its potential, this chapter addresses the limitations of AI in pathology, encompassing technical, practical, and ethical considerations during development and implementation.

Exploring explainable AI features in the vocal biomarkers of lung disease.

This review delves into the burgeoning field of explainable artificial intelligence (XAI) in the detection and analysis of lung diseases through vocal biomarkers. Lung diseases, often elusive in their early stages, pose a significant public health challenge. Recent advancements in AI have ushered in innovative methods for early detection, yet the black-box nature of many AI models limits their clinical applicability. XAI emerges as a pivotal tool, enhancing transparency and interpretability in AI-driven diagnostics. This review synthesizes current research on the application of XAI in analyzing vocal biomarkers for lung diseases, highlighting how these techniques elucidate the connections between specific vocal features and lung pathology. We critically examine the methodologies employed, the types of lung diseases studied, and the performance of various XAI models. The potential for XAI to aid in early detection, monitor disease progression, and personalize treatment strategies in pulmonary medicine is emphasized. Furthermore, this review identifies current challenges, including data heterogeneity and model generalizability, and proposes future directions for research. By offering a comprehensive analysis of explainable AI features in the context of lung disease detection, this review aims to bridge the gap between advanced computational approaches and clinical practice, paving the way for more transparent, reliable, and effective diagnostic tools.

Role of Artificial Intelligence (AI) in Patient Education and Communication in Dentistry.

Effective patient education and communication are integral components of quality dental care, contributing to informed decision-making, treatment compliance, and positive clinical outcomes. However, traditional methods face challenges such as language barriers, anxiety, and information retention issues. Artificial intelligence (AI) presents innovative solutions to enhance patient engagement and communication in dentistry. This review explores the transformative role of AI in redefining patient education and communication strategies, focusing on applications, benefits, challenges, and future directions. A literature search identified articles from 2018 to 2024, encompassing empirical evidence and conceptual frameworks related to AI in dental patient engagement and communication. Key findings reveal AI's potential to offer personalized educational materials, virtual consultations, language translation tools, and virtual reality simulations, improving patient understanding and experience. Despite advancements, concerns about overreliance, accuracy, implementation costs, patient acceptance, privacy, and regulatory compliance persist. Future implications suggest AI's ability to track patient progress, analyze feedback, streamline administrative processes, and provide ongoing support, enhancing oral health outcomes. However, ethical, regulatory, and equity considerations require attention for responsible AI deployment and widespread adoption. Overall, AI holds promise for revolutionizing dental patient education, communication, and care delivery, emphasizing the need for comprehensive strategies to address emerging challenges and maximize benefits.

Integrating Text and Image Analysis: Exploring GPT-4V's Capabilities in Advanced Radiological Applications Across Subspecialties.

This study demonstrates that GPT-4V outperforms GPT-4 across radiology subspecialties in analyzing 207 cases with 1312 images from the Radiological Society of North America Case Collection.

Predictive analytics support for complex chronic medical conditions: An experience-based co-design study of physician managers' needs and preferences.

PURPOSE: The literature suggests predictive technology applications in health care would benefit from physician and manager input during design and development. The aim was to explore the needs and preferences of physician managers regarding the role of predictive analytics in decision support for patients with the highly complex yet common combination of multiple chronic conditions of cardiovascular (Heart) and kidney (Nephrology) diseases and diabetes (HND). METHODS: This qualitative study employed an experience-based co-design model comprised of three data gathering phases: 1. Patient mapping through non-participant observations informed by process mining of electronic health records data, 2. Semi-structured experience-based interviews, and 3. A co-design workshop. Data collection was conducted with physician managers working at or collaborating with the HND center, Danderyd University Hospital (DSAB), in Stockholm, Sweden. HND center is an integrated practice unit offering comprehensive person-centered multidisciplinary care to stabilize disease progression, reduce visits, and develop treatment strategies that enables a transition to primary care. RESULTS: Interview and workshop data described a complex challenge due to the interaction of underlying pathophysiologies and the subsequent need for multiple care givers that hindered care continuity. The HND center partly met this challenge by coordinating care through multiple interprofessional and interdisciplinary shared decision-making interfaces. The large patient datasets were difficult to operationalize in daily practice due to data entry and retrieval issues. Predictive analytics was seen as a potentially effective approach to support decision-making, calculate risks, and improve resource utilization, especially in the context of complex chronic care, and the HND center a good place for pilot testing and development. Simplicity of visual interfaces, a better understanding of the algorithms by the health care professionals, and the need to address professional concerns, were identified as key factors to increase adoption and facilitate implementation. CONCLUSIONS: The HND center serves as a comprehensive integrated practice unit that integrates different medical disciplinary perspectives in a person-centered care process to address the needs of patients with multiple complex comorbidities. Therefore, piloting predictive technologies at the same time with a high potential for improving care represents an extreme, demanding, and complex case. The study findings show that health care professionals' involvement in the design of predictive technologies right from the outset can facilitate the implementation and adoption of such technologies, as well as enhance their predictive effectiveness and performance. Simplicity in the design of predictive technologies and better understanding of the concept and interpretation of the algorithms may result in implementation of predictive technologies in health care. Institutional efforts are needed to enhance collaboration among the health care professionals and IT professionals for effective development, implementation, and adoption of predictive analytics in health care.

Success of ChatGPT, an AI language model, in taking the French language version of the European Board of Ophthalmology examination: A novel approach to medical knowledge assessment.

PURPOSE: The purpose of this study was to evaluate the performance of ChatGPT, a cutting-edge artificial intelligence (AI) language model developed by OpenAI, in successfully completing the French language version of the European Board of Ophthalmology (EBO) examination and to assess its potential role in medical education and knowledge assessment. METHODS: ChatGPT, based on the GPT-4 architecture, was exposed to a series of EBO examination questions in French, covering various aspects of ophthalmology. The AI's performance was evaluated by comparing its responses with the correct answers provided by ophthalmology experts. Additionally, the study assessed the time taken by ChatGPT to answer each question as a measure of efficiency. RESULTS: ChatGPT achieved a 91% success rate on the EBO examination, demonstrating a high level of competency in ophthalmology knowledge and application. The AI provided correct answers across all question categories, indicating a strong understanding of basic sciences, clinical knowledge, and clinical management. The AI model also answered the questions rapidly, taking only a fraction of the time needed by human test-takers. CONCLUSION: ChatGPT's performance on the French language version of the EBO examination demonstrates its potential to be a valuable tool in medical education and knowledge assessment. Further research is needed to explore optimal ways to implement AI language models in medical education and to address the associated ethical and practical concerns.

Ethical Perspectives on Implementing AI to Predict Mortality Risk in Emergency Department Patients: A Qualitative Study.

Artificial intelligence (AI) is predicted to improve health care, increase efficiency and save time and recourses, especially in the context of emergency care where many critical decisions are made. Research shows the urgent need to develop principles and guidance to ensure ethical AI use in healthcare. This study aimed to explore healthcare professionals' perceptions of the ethical aspects of implementing an AI application to predict the mortality risk of patients in emergency departments. The analysis used an abductive qualitative content analysis based on the principles of medical ethics (autonomy, beneficence, non-maleficence, and justice), the principle of explicability, and the new principle of professional governance, that emerged from the analysis. In the analysis, two conflicts and/or considerations emerged tied to each ethical principle elucidating healthcare professionals' perceptions of the ethical aspects of implementing the AI application in emergency departments. The results were related to aspects of sharing information from the AI application, resources versus demands, providing equal care, using AI as a support system, trustworthiness to AI, AI-based knowledge, professional knowledge versus AI-based information, and conflict of interests in the healthcare system.

Two Singapore public healthcare AI applications for national screening programs and other examples.

This article explains how two AI systems have been incorporated into the everyday operations of two Singapore public healthcare nation-wide screening programs. The first example is embedded within the setting of a national level population health screening program for diabetes related eye diseases, targeting the rapidly increasing number of adults in the country with diabetes. In the second example, the AI assisted screening is done shortly after a person is admitted to one of the public hospitals to identify which inpatients-especially which elderly patients with complex conditions-have a high risk of being readmitted as an inpatient multiple times in the months following discharge. Ways in which healthcare needs and the clinical operations context influenced the approach to designing or deploying the AI systems are highlighted, illustrating the multiplicity of factors that shape the requirements for successful large-scale deployments of AI systems that are deeply embedded within clinical workflows. In the first example, the choice was made to use the system in a semi-automated (vs. fully automated) mode as this was assessed to be more cost-effective, though still offering substantial productivity improvement. In the second example, machine learning algorithm design and model execution trade-offs were made that prioritized key aspects of patient engagement and inclusion over higher levels of predictive accuracy. The article concludes with several lessons learned related to deploying AI systems within healthcare settings, and also lists several other AI efforts already in deployment and in the pipeline for Singapore's public healthcare system.

[EMPAIA-ecosystem for pathology diagnostics with AI assistance]. / EMPAIA ­ Ökosystem zur Nutzung von KI in der Pathologie.

Applications of deep learning and other artificial intelligence techniques play an increasing role in pathological research. In contrast to research, applications in clinical routine are rare so far, although the first certified solutions have already been established (analysis of prostate sections, ER, PR, and Her2 in breast cancer). Besides the still low use of virtual microscopy in practice, there are a number of hurdles that stand in the way of a rapid diffusion of AI applications. The EMPAIA project has a goal of removing these hurdles. The path taken to build an ecosystem for this purpose is intended to exemplify that the introduction of AI applications in image-based diagnostics is possible on a broad basis if the existing hurdles are removed in a joint, moderated process. The components of the EMPAIA ecosystem and its strategy will be described, and reference will be made to the technical solution approaches.

Evaluation of artificial intelligence clinical applications: Detailed case analyses show value of healthcare ethics approach in identifying patient care issues.

This paper is one of the first to analyse the ethical implications of specific healthcare artificial intelligence (AI) applications, and the first to provide a detailed analysis of AI-based systems for clinical decision support. AI is increasingly being deployed across multiple domains. In response, a plethora of ethical guidelines and principles for general AI use have been published, with some convergence about which ethical concepts are relevant to this new technology. However, few of these frameworks are healthcare-specific, and there has been limited examination of actual AI applications in healthcare. Our ethical evaluation identifies context- and case-specific healthcare ethical issues for two applications, and investigates the extent to which the general ethical principles for AI-assisted healthcare expressed in existing frameworks capture what is most ethically relevant from the perspective of healthcare ethics. We provide a detailed description and analysis of two AI-based systems for clinical decision support (Painchek® and IDx-DR). Our results identify ethical challenges associated with potentially deceptive promissory claims, lack of patient and public involvement in healthcare AI development and deployment, and lack of attention to the impact of AIs on healthcare relationships. Our analysis also highlights the close connection between evaluation and technical development and reporting. Critical appraisal frameworks for healthcare AIs should include explicit ethical evaluation with benchmarks. However, each application will require scrutiny across the AI life-cycle to identify ethical issues specific to healthcare. This level of analysis requires more attention to detail than is suggested by current ethical guidance or frameworks.

Considerations for development and use of AI in response to COVID-19.

Artificial intelligence (AI) is playing a key supporting role in the fight against COVID-19 and perhaps will contribute to solutions quicker than we would otherwise achieve in many fields and applications. Since the outbreak of the pandemic, there has been an upsurge in the exploration and use of AI, and other data analytic tools, in a multitude of areas. This paper addresses some of the many considerations for managing the development and deployment of AI applications, including planning; unpredictable, unexpected, or biased results; repurposing; the importance of data; and diversity in AI team membership. We provide implications for research and for practice, according to each of the considerations. Finally we conclude that we need to plan and carefully consider the issues associated with the development and use of AI as we look for quick solutions.

Application of Artificial Intelligence in Diabetes Education and Management: Present Status and Promising Prospect.

Despite the rapid development of science and technology in healthcare, diabetes remains an incurable lifelong illness. Diabetes education aiming to improve the self-management skills is an essential way to help patients enhance their metabolic control and quality of life. Artificial intelligence (AI) technologies have made significant progress in transforming available genetic data and clinical information into valuable knowledge. The application of AI tech in disease education would be extremely beneficial considering their advantages in promoting individualization and full-course education intervention according to the unique pictures of different individuals. This paper reviews and discusses the most recent applications of AI techniques to various aspects of diabetes education. With the information and evidence collected, this review attempts to provide insight and guidance for the development of prospective, data-driven decision support platforms for diabetes management, with a focus on individualized patient management and lifelong educational interventions.

Artificial Intelligence (AI) applications for COVID-19 pandemic.

BACKGROUND AND AIMS: Healthcare delivery requires the support of new technologies like Artificial Intelligence (AI), Internet of Things (IoT), Big Data and Machine Learning to fight and look ahead against the new diseases. We aim to review the role of AI as a decisive technology to analyze, prepare us for prevention and fight with COVID-19 (Coronavirus) and other pandemics. METHODS: The rapid review of the literature is done on the database of Pubmed, Scopus and Google Scholar using the keyword of COVID-19 or Coronavirus and Artificial Intelligence or AI. Collected the latest information regarding AI for COVID-19, then analyzed the same to identify its possible application for this disease. RESULTS: We have identified seven significant applications of AI for COVID-19 pandemic. This technology plays an important role to detect the cluster of cases and to predict where this virus will affect in future by collecting and analyzing all previous data. CONCLUSIONS: Healthcare organizations are in an urgent need for decision-making technologies to handle this virus and help them in getting proper suggestions in real-time to avoid its spread. AI works in a proficient way to mimic like human intelligence. It may also play a vital role in understanding and suggesting the development of a vaccine for COVID-19. This result-driven technology is used for proper screening, analyzing, prediction and tracking of current patients and likely future patients. The significant applications are applied to tracks data of confirmed, recovered and death cases.

Artificial Intelligence vs. Natural Stupidity: Evaluating AI readiness for the Vietnamese Medical Information System.

This review paper presents a framework to evaluate the artificial intelligence (AI) readiness for the healthcare sector in developing countries: a combination of adequate technical or technological expertise, financial sustainability, and socio-political commitment embedded in a healthy psycho-cultural context could bring about the smooth transitioning toward an AI-powered healthcare sector. Taking the Vietnamese healthcare sector as a case study, this paper attempts to clarify the negative and positive influencers. With only about 1500 publications about AI from 1998 to 2017 according to the latest Elsevier AI report, Vietnamese physicians are still capable of applying the state-of-the-art AI techniques in their research. However, a deeper look at the funding sources suggests a lack of socio-political commitment, hence the financial sustainability, to advance the field. The AI readiness in Vietnam's healthcare also suffers from the unprepared information infrastructure-using text mining for the official annual reports from 2012 to 2016 of the Ministry of Health, the paper found that the frequency of the word "database" actually decreases from 2012 to 2016, and the word has a high probability to accompany words such as "lacking", "standardizing", "inefficient", and "inaccurate." Finally, manifestations of psycho-cultural elements such as the public's mistaken views on AI or the non-transparent, inflexible and redundant of Vietnamese organizational structures can impede the transition to an AI-powered healthcare sector.