Revolutionizing nursing education and care: The role of artificial intelligence in nursing

Abstract

One of the primary applications of artificial intelligence (AI) in nursing is patient monitoring. AI-powered monitoring systems can continuously collect and analyze patient data, such as vital signs and patient behaviors.1, 2 These systems can detect subtle changes in a patient's condition, alerting nurses to potential issues before they become critical.2, 3 Remote monitoring allows nurses to keep a closer eye on patients, improving early intervention and reducing the risk of complications.4-6 The Internet of Things (IoT) is a new technology that has various applications in various fields, including medicine. It is possible to automatically connect sensors and devices to patients without human intervention through the IoT. A wireless body area network (WBAN) is an IoT subdomain which provides the possibility of monitoring vital signs remotely. In the medical field, WBAN consists of a small network of sensors, such as pulse oximeter, gyroscope, spirometer, global positioning system, and electrooculography. RPMs are used to continuously receive clinical data from patients through internal and external sensors and help physicians make appropriate decisions. The main stages of RPMs include the following: (1) Data collection: data such as (vital signs, EEG, ECG, blood pressure, heart rate, etc.) continuously using non-invasive techniques and Invasive techniques are reviewed. (2) Data transfer and storage: All data is collected and transferred to the cloud for analysis, sorting and processing. (3) Support systems: Data are analyzed and used to help clinicians make decisions. Implanted sensors: sensors that are implanted inside the patient's body (under the patient's skin). External sensors: sensors that are directly attached to the patient's skin. Cloud Computing and Fog Computing are the two systems used in storage server. RPMs have many advantages, which can be mentioned as follows: Provide patient assurance, Increase patient awareness and responsibility, Provision of low-cost solutions.5 The application of RPMs is mostly in the field of cardiac arrhythmias, hemodynamics and vital signs.6 Remote patient monitoring systems can be used in relation to various chronic diseases such as heart disease, fall detection, mental health, diabetes. In the field of heart diseases, it is possible to measure and collect heart rate, breathing rate, ECG, breathing rate through wearable sensors. Studies presented a fall detection system based on wearable and environmental sensors, which is of great importance in the elderly. In case of abnormal data, health care professionals and family would be notified. In the field of mental illnesses, systems that remind patients of the dosage of drugs at a certain time, and monitoring adherence to drugs were used. These systems have been used in Alzheimer's and bipolar patients. In the field of diabetes, these systems were used to evaluate the patient's blood glucose level, the amount of food consumed, and the patient's blood pressure.5 AI is also improving the management of electronic health records (EHRs). AI algorithms can extract valuable insights from these records, aiding nurses in making informed decisions about patient care. AI-driven predictive analytics can identify trends and risk factors, helping nurses anticipate patient needs and tailor interventions accordingly.5-7 During the last decade, the use of AI has grown in health data, which are mainly multimodal (EHR, medical imaging, multi-omics and environmental data). Artificial intelligence has led to transformation in various fields such as health education reaserch. Thanks to the advances in AI and machine learning (ML) models, multimodal data fusion with different features can be achieved. Integrating imaging data with specific laboratory test results and demographic data leads to improved outcomes.8 As EHR data is complex and contains diagnosis, scans, laboratory test results, administrative notes doctor's signature it is difficult in working with EHR data. Because of this complexity, researchers use the combination of AI and EHR. Artificial intelligence components such as natural language processing and deep learning are mainly used in this field. Artificial intelligence can be used to analyze medical history, family history and diseases related to genetics.9 For example, AI in cardiology was used with EHR data for early diagnosis of heart failure, to predict the onset of congestive heart failure, and to improve risk assessment in patients with suspected coronary artery disease.10 The large volume of electronic clinical and imaging information creates a great dilemma for physicians in decision-making.11 With the advent of modern technologies such as AI, doctors are able to interpret heterogeneous medical data and make more accurate and efficient decisions. The availability of large collections of EHR data and the widespread adoption of EHR systems by health care organizations have made its application more feasible.12 Many hospitals are already using AI technology and trying to become “smart” hospitals.13 The combination of clinical data in EHRs, combined with the ability to analyze this data with AI, leads to a better understanding of the disease and better decisions.14 Administering the correct medication at the right time is crucial in nursing. AI-powered systems can assist in medication management by verifying prescriptions, checking for potential drug interactions, and even automating the medication administration process. This reduces the likelihood of errors and enhances patient safety.5 AI-based decision support systems at all stages (e.g., prescribing and dispensing) have already been proven to enhance patient safety by enabling error detection, patient triage, and medication management. The use of intelligent algorithms leads to the reduction of medication errors due to human errors. Among the interventions, the machine category “computerized decision support system” is the most used, which is a technological software that uses patient data (including treatments and results) for clinical decision making.15 AI and ML techniques have enormous benefits for medication management, both in the hospital and in the community setting, from clinical decision support (CDS) to drug safety and toxicity. With their capacity to summarize large amounts of data, ML techniques can be used to support a medication management process such as prescription verification, by flagging aberrant prescriptions.16 Artificial intelligence creates new tools to adhere to the treatment regimen with the aim of improving the patient's health.17 AI smartphone applications (“apps”), smart phone have been used as valuable tools to assess and encourage medication adherence in studies. These software can be used to remind the dose of medicine and to provide the method and instructions for taking the medicine and lead to improved adherence and detection of non-adherence. Reminder systems have been used for medication reminders. For example, a reminder via SMS which leads to the support and empowerment of patients. Several trials showed increased medication adherence in patients who used technology compared to usual care. Increasing medication adherence leads to improved clinical outcomes in patients.18 Patient participation, generally defined as the ability and availability of patients to take a leading role in their health care; that e-health is often able to promote patient participation. Which is one of the health care priorities in the world today. Patients who actively participate in the process of managing their disease get better results. Because engaged patients are more likely than others to engage in preventive behaviors, self-manage their symptoms and treatments, and seek health information. The strategy of patient participation should be considered in the design of new technologies for health care. Otherwise, it will lead to patients not accepting new health technologies. Patients typically interact with dedicated web portals, online social networks, or mobile apps that help them manage their disease and consult with health professionals.19 Patient engagement platforms are digital health applications used to reach and engage patients throughout the self-care process. Common personal education plans include patient portals, mobile applications for Android/iOS platforms, and messaging chatbots that are easily accessible via smartphone, tablet, or computer. Patient engagement platforms can be used in various areas such as: providing educational content, sending reminders to follow treatment protocols, reporting pain scores and mobility levels, collecting or managing wound images, and recording and monitoring health outcomes.20 Patient portals examples are: MyChart Aethna communicator Mobile health applications examples are: GetWellLoop SeamlessMD MyMobility Force Twistle Pattern health Mobomo WellBe Conversa, tap cloud Chatbots examples are: STREAMD Conversa Memora health20 Nurse staffing and resource allocation can be challenging in healthcare settings. AI algorithms can analyze patient data, hospital census, and nurse schedules to optimize staffing levels. This ensures that the right number of nurses with the appropriate skills are available to provide high-quality care.7 The digitalization of health systems with AI affects the labor market and the way employees are managed. Such technologies provide the opportunity to achieve high levels of efficiency in managing and screening candidates or reduce the time to hire and overall recruitment costs. Such technologies offer the opportunity to achieve high levels of efficiency in managing and screening candidates or to reduce recruitment time and overall recruitment costs with such technologies. Digital technologies affect recruitment management practices. By providing a large amount of data and automating many steps, they help all stages of human resource management. On the other hand, AI tools greatly reduce the time spent on preparing the salary and employee benefits plan and allow them to focus on more strategic issues. Digitalization and AI lead to better economic and financial results. Despite the high cost of these technologies, they improve the overall performance of the organization, increasing productivity, employee retention, overall satisfaction, and efficiency and optimization of tasks. Optimization of resource allocation and cost containment is at the heart of the healthcare management debate. In this vein, new technologies play a key role in enabling companies to minimize costs.21 Transforming AI and big data into safe and efficient healthcare applications, services, and procedures involves significant costs and risks. While AI holds great promise in nursing, there are challenges and ethical considerations to address. Data privacy and security must be maintained, and nurses need adequate training to use AI tools effectively. There is also the concern of AI replacing human interaction in healthcare, which requires careful balancing.5, 22 One of the main reasons that can compromise patient data is cyber attacks.23 There are various ethical concerns that need to be answered. Who owns the data being mined? Are adequate measures taken to ensure the safety and confidentiality of patient data? Are users aware of the regulations and guidelines for the use of data and results generated by AI? Could the use of AI lead to greater disparities in health care based on racial or economic factors.18 Other things that can be addressed in the field of AI include: informed consent, Protecting human autonomy, Promoting human well-being and safety and the public interest, Ensuring transparency, explainability and intelligibility, Fostering responsibility and accountability, Ensuring inclusiveness and equity, Promoting AI that is responsive and sustainable.24 Artificial intelligence is reshaping patient care in nursing by improving monitoring, enhancing EHR management, ensuring medication safety, engaging patients, and optimizing resource allocation. As technology continues to advance, nurses and healthcare institutions must embrace AI as a valuable tool to provide safer, more efficient, and patient-centered care. Although it is necessary to consider the limitations and ethical issues of this technology. With responsible implementation and ongoing research, AI has the potential to significantly improve patient outcomes and the nursing profession as a whole. In an era of rapid technological advancement, AI has emerged as a transformative force in various fields, and nursing education is no exception. The integration of AI into nursing education is revolutionizing the way future healthcare professionals are trained and prepared for the complexities of the healthcare industry. Traditional nursing education often relies on textbooks, lectures, and clinical experiences, which, while valuable, can have limitations in terms of personalization and efficiency. AI, on the other hand, offers several advantages that are reshaping nursing education.25, 26 AI-powered educational platforms can assess the strengths and weaknesses of individual students and tailor learning experiences accordingly. This personalized approach ensures that students receive the support and resources they need to succeed. Education according to students' abilities and developing their talents is a necessary thing that cannot be achieved through education in the traditional way. The concept of personal training comes from “precision medicine,” which terms such as “precision training” and “personal training and learning” are used to refer to it. Personalized learning involves predicting student performance and providing feedback based on analysis of student learning profiles and retention patterns. Specific learning abilities, learning requirements and study goals are identified and analyzed in a personalized learning system, then content is presented based on them. This method is used to identify and better understand heterogeneity among learners with special difficulties and to adapt educational content to students' needs. Problem analysis to examine student needs is a key component of personalized learning. It is possible to design educational content that matches students' learning needs and preferences based on identifying and diagnosing their problems. Also, educational materials can be provided based on the past progress of students and educational gaps. The advent of computers and AI has greatly contributed to the expansion of personal learning. The advent of computers and AI has greatly contributed to the expansion of personal learning. This method can help eliminate students' learning problems and increase productivity in teaching and learning.27, 28 AI-driven simulations and virtual reality environments provide nursing students with realistic scenarios to practice their skills in a safe and controlled setting. This hands-on experience enhances clinical competence and confidence. Nursing educators can use virtual avatar applications, virtual game programs, and virtual teacher chatbots to deliver nursing education in academic settings. They can use these tools to simulate interactive clinical scenarios and increase students' understanding of specific nursing concepts. The use of these technologies leads to the fact that students can improve their communication skills with patients and the treatment team before entering the clinical environment, and it leads to an increase in their self-efficacy and self-confidence.25 Artificial intelligence can be integrated into nursing simulation training to increase realism and interaction and personalize the learning experience for students. Artificial intelligence can be used in various aspects of nursing simulation, such as virtual patient models, intelligent reporting systems, adaptive learning platforms, and CDS systems. Learners can self-learn without the need to be in the training place and without the need for the instructor to be present through simulation. The virtual patient model can mimic real clinical scenarios and it can show realistic physiological responses such as changes in vital signs, symptoms and behaviors. In this way, CDS can be provided for nursing students. Through these systems, students can be helped to strengthen critical thinking and clinical judgment skills by being exposed to complex and realistic patient care scenarios. These methods have countless advantages, including increasing realism and loyalty, improving student participation and active learning, personal learning, and an efficient mechanism for student evaluation and feedback. AI algorithms can tailor simulations to each student's individual learning needs by adjusting difficulty levels, providing personalized feedback, and tracking progress. Using these methods ultimately leads to saving time.29 AI can analyze vast amounts of healthcare data to identify trends, best practices, and areas of improvement. Nursing students can access these insights to stay updated on the latest research and evidence-based practices. Successful applications of AI have been made possible by increasing access to healthcare data and the rapid development of big data analysis methods. By means of AI techniques, it is possible to extract clinically relevant information hidden in a huge amount of data, which leads to better clinical decision making. By means of AI, it is possible to extract features from a large volume of health-related data and then use the obtained insight for clinical practice. Insights from AI can help doctors make better clinical decisions and judgments.30 The digitization of data has had a great impact on the responsibilities and work of healthcare professionals. In the new era, a lot of data is generated in the field of health, which is difficult to analyze. Therefore, powerful automated algorithms are needed to analyze and process useful information from healthcare data. This information helps health care professionals and doctors in finding the cause of diseases and providing better and cost-effective treatment to patients. Various AI technologies such as classification, regression, clustering, and correlation are used to analyze healthcare data to enhance the healthcare provider's ability to make decisions about the health of patients.31 Insights obtained from AI are used in healthcare, supporting clinical decision-making by facilitating complex, impractical or time-consuming tasks including prediction, diagnosis, treatment and follow-up.32 AI-powered educational tools are available round the clock, allowing students to learn at their own pace and convenience. This flexibility is especially valuable for working professionals pursuing further education. Artificial intelligence has played a huge role in education, including increasing access, which has increased even more with the Covid pandemic.33 Covid-19 has caused a global shutdown of several activities, including educational activities, and this has resulted in universities turning to online learning as an educational platform.34 The use of AI in education has created an opportunity to break down the physical barriers created by national and international borders, because educational materials are now accessible on the Internet and the World Wide Web for online learning or using web-based learning platforms and the content is accessible to people all over the world. Better access to learning with AI is possible by removing barriers to learning, automating management and administrative functions in academic institutions, optimizing instruction and learning, as well as enhancing experimental or evidence-based decisions and initiatives in education.35 Smart tutoring platforms for distance learning are growing and along with the expansion of mobile technology, it provides exciting opportunities for students and professors.3 Artificial intelligence can also augment teachers' ability to monitor and support their students. AI can continuously assess student progress, providing real-time feedback and helping educators identify struggling students who may need additional support.26 Students are afraid of answering questions in the presence of others in the classroom environment and receiving feedback. With AI, students can feel comfortable making the mistakes necessary to learn and receive the necessary feedback individually and personally.3 Feedback is very important to identify learning goals and knowledge gaps. Students need to know how they are performing in order to take steps to improve their performance.1 AI allows students to track their progress, identify weaknesses, and receive immediate guidance for improvement. AI-assisted learning assessment can lead to a more objective, faster, more cost-effective assessment process and provide more extensive individualized feedback.36, 37 AI can evaluate students without bias and with high speed and accuracy.3 On the other hand, Assessment of assignments and tests usually takes a lot of time from professors who can use this time to prepare for class and respond to students. These platforms can tell us what action should be taken and when to improve the performance of a particular student.38 Despite these significant benefits, the integration of AI in nursing education is not without challenges. Ensuring data privacy and security, addressing concerns about job displacement, adapting to rapidly evolving AI technologies, long-term effects of AI-assisted learning on student performance, evaluating the impact of AI-driven tools on instructor student interactions, and exploring the ethical considerations of incorporating AI technologies in nursing education are among the key hurdles that educators and institutions must navigate.37 In conclusion, AI is reshaping the landscape of nursing education. By harnessing the power of AI, nursing programs can better prepare students for the dynamic and demanding healthcare industry. While challenges exist, the potential to enhance the quality of education and ultimately improve patient care makes AI a valuable tool in the future of nursing education. As technology continues to advance, embracing AI in nursing education is not just an option; it's a necessity for the next generation of healthcare professionals. This research is a commentary study and I thank all the students who participated in the compilation and analysis of the findings. The authors declare that they have no competing interests. Golnar Ghane, PhD in Nursing, Assistant Professor, Medical surgical department, School of Nursing and Midwifery, Tehran University of Medical Sciences, Tehran, Iran. Shahrzad Ghiyasvandian, PhD in Nursing, Full Professor, Medical surgical department, School of Nursing and Midwifery, Tehran University of Medical Sciences, Tehran, Iran. Amir Mohammad Chekeni, Student in Nursing, Medical surgical department, School of Nursing and Midwifery, Tehran University of Medical Sciences, Tehran, Iran. Raoofeh Karimi, MSc student of Medical Surgical Nursing, School of Nursing and Midwifery, Tehran University of Medical Science, Tehran, Iran. The datasets used during the current study are available from the corresponding author on reasonable request.