Artificial Intelligence

Track 15: Artificial Intelligence

Artificial intelligence in nursing

Artificial intelligence (AI) encompasses a wide range of healthcare technologies that are transforming nurses‘ roles and improving patient care. In healthcare, AI typically refers to a computer’s ability to convert data into knowledge on its own to guide decisions or autonomous actions. However, precisely defining AI can be difficult due to its wide range of applications, which include risk prediction algorithms, robots, and speech recognition—all of which augment nursing practice and are rapidly changing healthcare as a whole.

Clinical decision support, mobile health and sensor-based technologies, voice assistants, and robotics are all examples of nursing AI tools. (Visit for an introduction to AI, including definitions of machine learning, deep learning, and other related terms.

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Clinical decision support

Clinical decision support tools (such as EHR alerts, clinical practice guidelines, order sets, reports, and dashboards) improve nurses’ ability to make clinical decisions. They may provide information to the end user or actionable options based on the data. Clinical decision support may also be integrated into other tools, such as mobile health applications, in addition to the EHR. When combined with AI, clinical decision support can make predictions and recommendations with greater accuracy and specificity than humans. To prevent catheter-associated urinary tract infections, AI-based clinical decision support includes automatically generated nursing diagnoses, fall risk prediction, and guided decision trees.

These tools’ concepts are not novel. For example, fall risk prediction entails regular assessment and fall precaution implementation. Manual risk calculation, on the other hand, is time-consuming and prone to human error, resulting in inaccurate predictions. Over traditional methods, AI has three advantages:

  • The ability to consider large amounts of data quickly in risk prediction
  • increased intervention specificity (accurately flagging patients most at-risk)
  • Variable selection and calculation are automated.
  • AI correctly identifies at-risk patients by taking into account more diverse patient information from the EHR and other data sources.

Mobile health and sensor-based technologies

The COVID-19 pandemic altered patient care delivery by necessitating the retrieval of data from patients remotely and between clinic visits. Mobile health (health) and sensor-based technologies have the potential to reshape a nurse’s ability to deliver care and monitor patients, which account for more than 75% of healthcare spending in the United States.

Mobile health technologies (smartphones, smartphone apps, and wearable technologies) aid in the management of chronic illnesses by receiving and transmitting data directly between patients and providers, resulting in a comprehensive picture of a patient’s health in their daily environments.

Voice assistants and robotics

Voice assistants (such as Amazon Alexa and Google Assistant) may have a future in EHR applications, gathering patient data in the home and delivering interventions to supplement care. Consider the following scenario: a nurse uses Alexa to remind older adults to take their medications and to check their blood pressure. Alexa then enters patient information into the EHR for the nurse to review. Because of their voice-based interaction, these tools may be especially useful for older adults and patients with certain disabilities, such as poor eyesight. The value of voice assistants is dependent on nurse involvement in technology selection, implementation, and patient care.

Researchers have been using AI for several decades, but its application in practice is still relatively new. When nurses use artificial intelligence, such as clinical decision tools, they can quickly process large amounts of data to identify risks, recommend interventions, and streamline workflow. However, in order for AI to truly transform nursing practice, limitations must be addressed with nurse input.

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Sub-tracks of Artificial intelligence in nursing

  • Smartphones and wearables.
  • At-home or portable diagnostics
  • Smart or implantable drug delivery mechanisms
  • Digital therapeutics and immersive technologies
  • Genome sequencing
  • Artificial Intelligence
  • Robotics and automation
  • The connected community


  • ai
  • DataRobot
  • ai
  • Sift
  • Vicarious
  • pymetrics
  • ai
  • SupportLogic

List of the 10 best Artificial intelligence in nursing Association in the World

  • Google Health/Deep Mind
  • IBM Watson Health
  • Oncora Medical
  • Cloud MedX Health
  • Babylon Health
  • Corti
  • Butterfly Network
  • Arterys
  • Caption Health
  • Enlitic

Artificial Intelligence: Organizations
American College of Radiology Data Science Institute
Working with stakeholders to develop and implement radiology-related artificial intelligence applications.
American Medical Informatics Association
A professional organization interested in the intersection of informatics and healthcare.
Association for the Advancement of Artificial Intelligence
A nonprofit scientific society devoted to advancing the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines.
Canadian Artificial Intelligence Association
The Canadian arm of the Association for the Advancement of Artificial Intelligence.
Data Science Association
A non-profit professional association of data scientists.
European Association for Artificial Intelligence
Promotes the study, research, and application of Artificial Intelligence in Europe
A professional organization for engineering, computing, and technology information around the globe.
International Neural Network Society
An organization of researchers studying neural networks and computational science
Machine Intelligence Research Institute
A research nonprofit studying the mathematical underpinnings of intelligent behavior.
National Artificial Intelligence Initiative
A coordinated program across the entire Federal government to accelerate AI research.

why it’s important of Artificial Intelligence

Artificial Intelligence (AI) is important for a variety of reasons across different domains and industries. Here are some key factors that highlight the importance of AI:

  1. Automation and Efficiency:
    • AI enables automation of repetitive tasks and processes, leading to increased efficiency and productivity. This is particularly valuable in industries such as manufacturing, logistics, and customer service, where routine tasks can be handled by AI systems.
  2. Data Analysis and Insights:
    • AI has the capability to analyze vast amounts of data quickly and derive meaningful insights. In fields like healthcare, finance, and marketing, AI-powered analytics can help organizations make data-driven decisions and predictions.
  3. Innovation and Problem Solving:
    • AI fosters innovation by providing new ways to approach problem-solving. Machine learning algorithms, for example, can identify patterns and solutions that may not be immediately apparent to humans, leading to breakthroughs in various scientific and technological domains.
  4. Personalization and User Experience:
    • AI is instrumental in delivering personalized experiences to users. In applications like recommendation systems, virtual assistants, and personalized marketing, AI algorithms analyze user behavior to tailor services and content to individual preferences.
  5. Healthcare Advancements:
    • AI is transforming healthcare through applications like diagnostic imaging, drug discovery, and personalized medicine. AI algorithms can analyze medical images, identify patterns, and assist healthcare professionals in diagnosing and treating diseases more effectively.
  6. Natural Language Processing (NLP):
    • NLP allows machines to understand, interpret, and generate human language. This technology is critical for applications like virtual assistants, chatbots, and language translation, improving communication between humans and machines.
  7. Autonomous Systems:
    • AI plays a key role in the development of autonomous systems, including self-driving cars, drones, and robots. These systems rely on AI algorithms to perceive their environment, make decisions, and navigate without human intervention.
  8. Cybersecurity:
    • AI is used in cybersecurity to detect and respond to threats in real-time. Machine learning algorithms can analyze patterns in network traffic and identify unusual behavior, helping to prevent cyberattacks and enhance the security of digital systems.
  9. Financial Services and Fraud Detection:
    • In the financial industry, AI is used for fraud detection, risk assessment, and algorithmic trading. AI systems can analyze financial data at a speed and scale that would be challenging for humans, improving the accuracy of decision-making.
  10. Human-Robot Collaboration:
    • AI enables collaboration between humans and robots in various industries, such as manufacturing and healthcare. Collaborative robots, or cobots, can work alongside humans, performing tasks that require precision, strength, or repetitive actions.
  11. Climate Change and Sustainability:
    • AI is being applied to address environmental challenges, including climate change. It can optimize energy consumption, monitor and manage environmental data, and contribute to sustainable practices in industries like agriculture and transportation.
  12. Education and Personalized Learning:
    • AI is increasingly used in education to provide personalized learning experiences. Adaptive learning platforms can assess individual student needs and tailor educational content and strategies accordingly, enhancing the learning process.