Artificial intelligence ( first part ) بالانجليزية

 

       Artificial intelligence (AI) is poised to revolutionize nursing practice in various areas such as administration, clinical care, education, policy, and research. It is seen as a valuable tool that can help nurses in synthesizing information quickly, making clinical decisions, enhancing the delivery of care, and ultimately improving patient outcomes. By combining these solutions and educating nurses on how to effectively utilize them, there are endless possibilities for enhancing efficiency, capacity, quality, and transforming healthcare in the future. Therefore, it is crucial for nurses to actively involved in the development and application of AI. In healthcare AI is defined as the ability of machines to perform intelligent human tasks like problem-solving and reasoning. It typically involves software programs that analyse data from multiple sources and turn data into knowledge to guide decisions or actions in clinical and operational settings. 

           There are three forms of artificial intelligence technology (AIT) used in the healthcare field. These include machine learning, deep learning, and natural language processing. Machine learning involves using algorithms (computer programs) to analyse data and create instructions for tasks. Machine learning and predictive analytics are commonly used in healthcare today, involving the analysis of historical data to predict future events. In nursing, examples of this type of AI include electronic health record (EHR) clinical decision support tools, radiology image recognition, and disease progression prediction. 

            Deep learning, a subcategory of machine learning, aims to achieve the same outcomes with less human involvement. It expands neural networks, which are algorithms designed to mimic human thought processes and are utilized to recognize patterns in extensive datasets such as text and images. Examples including predicting diseases such as diabetes and heart disease, detecting sepsis at an early stage, using automated techniques to diagnose medical images like tumours, discovering new drugs, detecting disease outbreaks, providing personalized healthcare based on genomics like drug therapy. In contrast, natural language processing focuses on creating algorithms that can understand and analyse unstructured text and speech data. Some applications include confirming diagnoses, developing clinical terminology, analysing content qualitatively. 

        Another form of artificial intelligence is the combination of natural language processing (NLP) with Automated Speech Recognition (ASR), which helps computers to better comprehend and analyse human languages. When paired with predictive analytics and machine learning (ML), NLP and ASR allow researchers to create algorithms for tasks such as language translation, semantic comprehension, and text summarization. This makes it easier to interpret and process large amounts of text with minimal effort. Some examples of NLP and ASR applications in healthcare industry like extracting information from medical records and utilizing speech-activated devices. 

          Nursing artificial intelligence tools include clinical decision support, mobile health and sensor-based technologies, and voice assistants and robotics. Clinical decision support tools can help nurses make better decisions by providing information and options based on data. It can assist nurses in advocating for patients, identifying care gaps, and predicting fall risks, providing nursing diagnosis, guiding decisions, recommending interventions, and making workflow more efficient. For instance, when evaluating the risk of pressure injuries or falls, AI tools take into consideration risk factors over time and adjust their calculations for better accuracy. In contrast, traditional tools only consider a limited number of factors at a single moment, and they are unable to accommodate individual differences. 

          Mobile health (mHealth) and sensor-based technologies offer new ways for nurses to provide care and monitor patients, especially when resources are limited. These tools are particularly helpful for managing chronic diseases. Mobile health technologies such as smartphones, smartphone apps, and wearable devices allow patients to share information with their healthcare providers, giving a more complete understanding of their health status in their daily lives. Sensors placed in homes or hospitals can help nurses communicate with patients, track movement, and collect various health data. These technologies can be used throughout a patient's journey from hospital to home care. Smartphones are widely accessible and can improve healthcare access for people of all backgrounds. Wearable sensors for monitoring activity, sleep, and heart health are becoming more affordable, allowing nurses to track patient data in real-time for effective diagnosis and monitoring. The use of mobile health and sensor-based technologies has become increasingly important during the COVID-19 pandemic. This has enabled remote monitoring and data collection from patients outside of traditional clinic visits.  

          Another example highlighting the benefits of mobile health is when diabetic individuals utilize mobile devices to monitor their health, healthcare providers can analyse the data to recognize patterns and encourage patients to make changes based on trends. This analysis can also help identify patients who require additional care and self-management. Nurses can use this data into clinic visits to show patients their daily activities and physical changes. Voice assistants like Amazon Alexa and Google Assistant may potentially be employed in the future to gather patient data at home and offer assistance. For instance, caregivers could utilize Alexa to remind elderly patients to take their medications, check their blood pressure, and keep track of their health. As robotics technology advances, robots can serve as care companions and tools for remote care. Hospitals are increasingly using telepresence robots to aid in patient care.

          Lastly, artificial intelligence in healthcare has the potential to have a positive impact on patient care, population health, health equity, healthcare costs, satisfaction and well-being of healthcare professionals, and overall productivity. AI can also enhance the management and analysis of electronic health records, streamline administrative processes, and improve communication among healthcare teams. This is particularly beneficial for nurses as it allows them to spend more time with patients and less time on paperwork. Additionally, AI offers several advantages in healthcare, including remote monitoring with telemedicine, virtual health assistants for patient engagement, early disease detection with advanced medical image analysis, personalized treatment plans based on genetic and lifestyle data, predictive analytics for disease outbreaks and patient admissions, early warning systems for identifying deteriorating patient conditions, and the use of AI-powered robots in surgery to reduce the risk of human error. AI also plays a crucial role in drug discovery, streamlining administrative tasks, and data analysis in clinical trials and research. In nursing simulation training, AI can increase realism and interaction, personalized learning experiences, and provide various tools such as virtual patient models, intelligent reporting systems, adaptive learning platforms, and clinical decision support systems (CDS). By using these systems, students can enhance their critical thinking and clinical judgment abilities through exposing to complex and realistic patient care situations. 

       Although AI has its advantages, a major downside in healthcare is the potential for errors and biases in algorithms, as well as concerns regarding data privacy and ethical issues. The opacity of some AI algorithms, known as "black box AI", can lead to inaccurate results, requiring healthcare professionals to interpret and validate recommendations to ensure patient safety and legal compliance. Careful design, testing, and regulation are necessary to address these concerns and maximize the benefits of AI in healthcare.

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