Artificial intelligence influences healthcare, education, business, and communication by automating tasks, analyzing data, and improving decision-making. From virtual assistants to predictive algorithms, AI drives efficiency, personalization, and global innovation.
Artificial intelligence is reshaping nursing by streamlining documentation, predicting patient deterioration, and supporting clinical decisions. AI tools like virtual nursing and its influence, smart monitoring, and workload automation improve care quality and reduce nurse burnout.
Clinical Influence of AI in Nursing
AI is increasingly embedded in nursing workflows, offering powerful tools for diagnosis, care planning, and patient engagement. A 2025 integrative review found that AI-enabled systems significantly improved early detection of complications, reduced hospital stays, and enhanced patient safety.
- Diagnostic Support: AI algorithms analyze imaging and lab data to flag abnormalities, aiding nurses in early intervention.
- Predictive Analytics: Systems forecast patient deterioration, infection risk, or readmission likelihood, guiding proactive care.
- Virtual Nursing Assistants: Platforms like ThinkAndor® support remote monitoring, behavioral health consults, and infection control across thousands of hospital rooms.
Concepts, methods, and algorithms used in AI.
| Technology/Method/Algorithm | Description |
|---|---|
| Deep Learning | Deep Neural Networks improve disease diagnosis, medical imaging analysis such as X-rays and MRI scans, and prediction of treatment outcomes. |
| Natural Language Processing (NLP) | NLP systems extract and analyze information from patient records, medical reports, and other sources to assist nurses in decision-making. |
| Reinforcement Learning | Reinforcement learning models can learn optimal strategies for disease detection and treatment, guiding nurses in complex and rapid decision-making. |
| Medical Imaging Processing | Algorithms and techniques such as convolutional neural networks (CNNs) and hybrid networks are used for rapid medical image diagnosis. |
| Hospital Data Analytics | Data mining and machine learning-based analytics identify new patterns in hospital data, enhancing healthcare quality and optimizing hospital processes. |
| Patient Monitoring | Smart monitoring systems collect physiological data from patients using sensors and wearable devices, aiding nurses in better patient care. |
| Predictive Systems | Using patient data, medical history, and other factors, predictive systems forecast the risk of new diseases or exacerbation of existing ones, assisting nurses in care planning. |
| Decision Support Systems | These systems assist nurses in advanced and more effective decision-making by analyzing clinical data and information. |
Diagnosis and Early Diagnosis of Disease
Investigation & Prediction of Clinical Trends
The analysis and prediction of clinical patterns is another part of the effectiveness of AI. By examining the patient’s data, AI can forecast health issues and complications arising from the disease.
In Hospitals, it can give insight into the likelihood of infection during hospital stay, complications caused by medication administration, the individual’s response to treatment protocols, and even predict the recovery process
Management and Optimization of Resources in Healthcare
With the help of EMR system of categorizing and managing medical and nursing measures, we can carefully organize and manage the files, which reduces the need for paperwork.
Moreover, AI carefully categorizes the information and ensures that important information is registered can be done by placing an AI system next to the patient’s bed
Facilitating Communication between Nurses and Patients
AI can make it easier to exchange information between healthcare providers and their patients, especially in situations where language barriers exist
Benefits of Integrating AI into Nursing
1. Increased Accuracy in the Diagnosis of Diseases
AI can assist physicians in the analysis of medical images such as X-rays, magnetic resonance imaging (MRI), and computed tomography (CT) scans, thereby enhancing the precision in disease diagnosis.
After diagnosis, AI can provide appropriate recommendations based on the type of disease and the patient’s condition.
2.Reducing Fatigue and Job Burnout
AI-robots have the ability to facilitate patient transportation, deliver items next to the bed, assist with welfare services, and execute doctor’s orders.
Minimizing the nurse’s mobility time during transfer allows her to focus on addressing various matters at the bedside. The primary emphasis lies on the nurse’s bedside care
3.Optimizing Resources and Reducing Waste
Artificial Intelligence can be a better program for assigning nurses, reducing paper consumption and forgetting to write materials, appointment management, and scheduling so that patients’ waiting time is reduced and leads to improved nurses’ quality and health education and waste management
4.Following up and Adjusting the Meal Plan
AI can provide nutrition-related suggestions based on the patient’s physiological needs. Undoubtedly, patients with high blood pressure need low-sodium foods. Low-sugar diabetic patients and other patients also need a special diet to reduce complications
5.Improving the Quality of Healthcare
Through the use of data and preliminary sampling techniques, such as biopsy, AI is able to facilitate early detection and treatment of patients. By analyzing data collected from individual patients, AI can tailor personalized programs and care for each person, resulting in a person-centered approach.
By leveraging data analysis, AI can continuously enhance care processes, thereby contributing to the overall improvement of patient care through the use of predictive algorithms
6.Helping Nurses to Prevent Certain Diseases
By utilizing AI and AI-Bot technology, patient information is categorized upon admission to the hospital and communicated to staff regarding their specific illnesses, including Human Immunodeficiency Virus (HIV), Hepatitis C&B, and Human Papillomavirus (HPV), etc.
7.Arrhythmias Diagnosis
The Deep Rhythm AI (DRAI) software is used to detect arrhythmias and automatically analyze Electro- cardiograms (ECG). DRAI is a cloud-based AI algorithm that analyzes all the heartbeats in the processed ECG signal and based on that, classifies them as either correct or arrhythmic.
Apple Watch Atrial Fibrillation (AF) feature analyzes pulse rate data from the Apple Watch to identify irregular heart rhythms suggestive of AF, and provides an estimate of the time spent in AF .
Apple Irregular Rhythm Notification Feature (IRNF) app, which specializes in Irregular Rhythm Notification Feature, is a medical application that analyzes pulse rate data from the Apple Watch and notifies the wearer if AF is present. This system helps control patient care during post-discharge care, and recent news reports have reported that people have been saved in this way
8.Administration of Medication
Medication Administration, based on the doctor’s order, will not be without error, especially in developing or non-developed countries, due to the high number of patients relative to the treatment staff, which is uncontrollable.
The integration of an Artificial Intelligence system can validate medication dosage, particularly in cases of specialized ailments and pediatric care, identify potential side effects, and assess drug interactions, thus guaranteeing the administration of accurate drug dosages to patients
9.Analyses Sleep Monitoring
Automatic sleep stage scoring based on deep neural networks has come into focus for sleep researchers and physicians. As a reliable method able to objectively classify sleep stages, it would save human resources and simplify clinical routines.
Modern machine learning techniques are not just a tool to perform automatic sleep stage classification but are also a creative approach to find hidden properties of sleep physiology.
The patient’s sleep cycle is a crucial aspect of nursing. The patient’s sleep cycle is generally beneficial for nurses in providing better care and meeting their patients’ needs more effectively.
10.Controlling and Monitoring Patients
The patient is continuously monitored by an AI-based monitoring device for any defects or medical emergencies. If there are any problems, the nurses are notified and they investigate and fix them.
When there are shortages of staff in specialized departments, nurses may not be able to monitor patients simultaneously, but the AI system can address this limitation by alerting the nurse.
Influence of AI in Nursing:
AI-based programs have the potential to aid diverse students, particularly those in the nursing field, in examining intricate medical information, recognizing patterns related to health, and making informed decisions. It enhances the health of both the patient and the healthcare system
Continuous Glucose Monitoring (Especially in Pregnant Women with Type 1 Diabetes)
During Pregnancy, ensuring the care of both the mother and fetus is important for a safe delivery. Physiologic and psychological changes should be monitored for potential risks.
Type 1 diabetes increases the risk of adverse pregnancy outcomes, including higher rates of pre-eclampsia and caesarean section in mothers, and of congenital anomaly, preterm delivery, perinatal mortality, large for gestational age, and neonatal intensive care admission in infants.
The d-Nav System is a software-based, prescription-only product designed to provide the next insulin dose recommendation as an aid for personal insulin management.
Wound Healing
The Spectral MD Deep View Wound Imaging System provides images of blood flow in the microcirculation and provides an assessment of perfusion of both healthy and injured skin (eg, skin flaps, chronic wounds, decubitus ulcers, diabetic ulcers, and burns). Artificial intelligence can play an important role in wound care in nursing.
MIMOSA Imager is used in the ambulatory care setting to estimate the spatial distribution of percent oxygen saturation in tissue during the examination of patients with suspected circulatory compromise.
Among its applications are predicting wound risks, continuous monitoring of patients to identify changes in wounds and guidance for the development of appropriate care programs. This technology has the potential to enhance the quality of health care and reduce costs.
Development of Rehabilitation Programs
AI systems have the potential to assist in the development and assessment of rehabilitation programs for patients following specific medical procedures or therapies. It is evident that this precise algorithm has the ability to develop a more precise rehabilitation program, thus improving the effectiveness of the treatment process
Urinalysis
The Minuteful – kidney test is a kit and smartphone application that measures and displays albumin and creatinine in urine, along with the albumin-creatinine ratio. The ACR | LAB Urine Analysis Test System is a kit (including a color board and reagent strips) along with a smartphone application that measures albumin and creatinine in urine and displays the results, along with the albumin-creatinine ratio.
The analysis can provide useful information, including levels of salts, blood sugar, protein, and other elements in the urine that may indicate various diseases.
According to the results of the analysis, nurses can determine whether the patient needs special treatment, whether his or her overall condition is stable, if he or she needs changes in treatment، or whether kidney-related diseases, diabetes, etc. are developing.
Keeping the Patient Safe
The elderly are faced with many challenges, one of them being the risk of falling. An AI system is capable of effectively monitoring and detecting falls, which allows it to predict potential accidents. Moreover, this system provides valuable recommendations to nurses on the necessary precautions to take.
Sleep Apnea detection
With the sleep apnea feature, Samsung is taking the next step in its ongoing commitment to provide users with the best possible sleep tools to improve their sleep health habits. The new feature will detect signs of moderate to severe obstructive sleep apnea (OSA) in adults 22 years of age and older, empowering people to seek medical care.
The feature, a software-only mobile medical app, uses smartwatch built-in sensors to monitor the user’s sleep for significant breathing disruptions associated with OSA. Users may track their sleep twice for more than four hours within a 10-day period to utilize the feature.
According to Samsung, the feature is not intended to replace diagnosis and treatment by a clinician, nor is it intended to assist clinicians in diagnosing sleep disorders. It is also not intended for those who have previously been diagnosed with OSA. Snore detection is an AI software that diagnoses obstructive sleep apnea.
Seizure Identification
The Embrace is a device that provides an adjunct to seizure monitoring in-home or healthcare facilities. The device senses electrodermal activity and motion data and detects patterns potentially associated with generalized tonic-clonic seizures.
When a seizure event is identified, a paired wireless device will initiate an alert to a designated caregiver. The data is stored for subsequent review by the clinician.
Estimation of Blood Loss
The Triton System is a software application used on an Apple iPad that captures images of used surgical sponges to estimate blood loss and assist operating room personnel in managing surgical sponges after surgical use.
Nurses can provide the best possible care for their patients by using artificial intelligence to estimate the amount of blood lost in the operating room. Nurses can react more accurately and quickly when blood loss requires immediate intervention with this information.
Early Alarming of Deterioration of Patient Status
AI, by analyzing patient data in anesthesia and ICU, can detect patterns of early changes in patient vital indicators and provide warnings to nurses. This accurate and early information will allow nurses to begin treatment or emergency procedures more quickly, prevent more serious problems, and help improve the patient’s progress.
It is possible to develop predictive algorithms and proper disease management that aid in improving overall care and reducing mortality rates
Robots in Patient Care
The application of AI in nursing through the use of robots is an attractive aspect. These robots can perform tasks such as distributing medications, performing basic tests, and even providing additional information to patients.
Professional Ethics
Human compassion is a vital component of this discourse. AI implementation has the ability to take arbitrary actions that could result in a patient’s death, only to save money and other factors. Nurses’ innate empathy and selflessness are contradicted by such actions. In other words, in the face of complex decisions related to the treatment of patients, humanity, pity, and professional ethics must continue to be maintained.
Summarizes the ethical considerations and obstacles of implementing AI in nursing.
| Challenges | Summary |
|---|---|
| Privacy and data security | Implementing AI in nursing raises concerns about patient privacy. Protocols and guidelines must be established to handle and protect confidential information effectively. |
| Interpersonal communication in healthcare | Human communication is crucial in healthcare, affecting the quality of care. Establishing strong bonds between nurses, staff, and patients accelerates recovery. Communication also motivates patients. |
| Addressing bias in AI algorithms | Balancing AI and human experience is essential. Final decisions require human input to address biases in care plans, such as gender or ethnicity biases. |
| Excessive dependence on technology | Overreliance on AI may compromise nurses’ human skills. It’s vital to strike a balance between AI utilization and the application of human skills. |
| Ensuring trust in AI results | Despite AI’s algorithmic nature, results may not be 100% accurate. Human guidance is necessary to interpret and validate AI-produced results accurately. |
| Professional ethics | AI implementation may clash with professional ethics, as arbitrary actions could harm patients. Human compassion and ethical considerations must guide complex decisions in patient treatment. |
The Future of AI in Nursing
The future of AI in nursing is promising because it is continuing to evolve and advance, as is the case with other domains. The future for this technology in nursing looks bright because of the advancement of robotics and the ability to improve care plans with individual data.
Development and Future Advancements in Technologies related to AI
The advancement of technology is a continuous process that we are facing day by day. Providing algorithms and AI analysis is getting better day by day and undoubtedly will help the care services and medical treatment sector in the (near future) not too distant future. Remember that we also require a nursing group for this development. To effectively develop an AI system to assist medical staff, the presence of experienced personnel is indispensable for organizing and managing this system.
Greater Impact on Improving Healthcare
AI has had a positive impact on improving health care by optimizing the treatment process, reducing unemployment, robotics care, continuous education, etc., with the help of the therapy community, especially nurses, who are more likely to be at the patient’s bedside. The positive and significant impact that AI has on healthcare is largely due to the involvement of the medical community, which includes nurses who care for patients at their bedside.
Development of New Skills and Training for Nurses
AI is a system that is always up-to-date and can impart (teach) the latest care methods to anyone worldwide. The integrated global education system in this sector requires the most up-to-date training, and this training will increase the skills of nurses.
Reduction of Workload
The future of AI technology will involve robotic care that is deeply intertwined with AI. Through the use of an artificial intelligence system, Chat-Bot has the potential to alleviate the burden on nurses’ workload, allowing them to allocate additional time towards patient communication and the delivery of enhanced care.
REFERENCES
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- Ronquillo CE, Peltonen LM, Pruinelli L, et al. Artificial intelligence in nursing: Priorities and opportunities from an international invitational think‐tank of the Nursing and Artificial Intelligence Leadership Collaborative. J Adv Nurs 2021; 77(9): 3707-17.
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- Mahmoudi H, Moradi M. The Progress and Future of Artificial Intelligence in Nursing Care: A Review. Open Public Health J, 2024; 17: e18749445304699. http://dx.doi.org/10.2174/0118749445304699240416074458
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