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3 The Future of Healthcare Technology

Laura K. Garner-Jones

 

Chapter 3 Overview

  • Artificial Intelligence definition and types of AI
  • The application of AI in healthcare
  • Robotics
  • Other emerging healthcare technologies

 

Introduction

Artificial Intelligence (AI) is rapidly transforming society as a whole. Once a concept confined to science fiction, AI is now embedded in many aspects of daily life, from streaming recommendations to advanced robotics. In healthcare, AI is enabling clinicians to deliver safer, more efficient, and more personalized care. With subfields such as machine learning, natural language processing, computer vision, and robotics, AI systems can process vast amounts of data, recognize patterns, and support decision-making in ways that were not previously possible. This section will explore the evolving role of AI and other emerging technologies such as 3-D printing, precision medicine, and predictive analytics, highlighting both their current capabilities and future potential in transforming healthcare delivery.

 

Watch this short video. What do you think about this video and where technology is leading us? What have you witnessed in your own clinical experiences?

 

 

Artificial Intelligence

Artificial Intelligence (AI) is a broad field of computer science focused on the development of algorithms and systems that enable machines or software to perform tasks typically requiring human intelligence (Texas State University, 2025). Examples of AI include:

    • A self-driving car navigating traffic
    • A fraud detection system at a bank
    • A recommendation engine on Netflix
    • A robot performing surgery

 

Artificial Intelligence (AI) has been around for many years, but has more recently moved into the headlines. AI computer systems can analyze millions of data points to find patterns, themes, connections, and relationships. The computer then uses that data to help make decisions, drive healthcare research, as well as diagnose disease or stages of disease, which would be difficult to do otherwise.

AI is an umbrella term that includes many subfields (Davies, 2023; Texas State University, 2025), some of which include:

    • Machine Learning (ML): Systems that learn from data to improve performance over time. An example of ML is spam detection in your email.
    • Natural Language Processing (NLP): Understanding, interpreting, and generating human language. An example of NLP includes machine translation (Google Translate).
    • Computer Vision: Enabling machines to interpret and understand visual information. Examples include facial recognition and medical image analysis.
    • Robotics: Designing and programming robots to perform physical tasks. Examples include warehouse automation (Amazon) and surgical or service robots.
    • Expert Systems: Mimics the decision-making abilities of a human expert using rule-based logic. This was discussed in Chapter 1. Clinical Support Systems used in EHRs are becoming more adept at moving knowledge to wisdom and offering diagnostic tools.
    • Generative AI: Creating new content (text, images, audio, video) based on training data. Examples of Generative AI include Large Language Models (LLMs) for text (ChatGPT or Copilot), and text-to-image or text-to-video technology.

 

Large Language Models (LLM)

  • LLMs are a type of Generative AI designed to process and produce text and have received significant attention since OpenAI’s release of ChatGPT in November of 2022 (Thacharodi et al., 2024). Since then, other software companies have followed suit. LLMs are rapidly being integrated into society, transforming how we access information and services.

  • At this time, limitations with LLMs include the replication of biases and the production of false or inaccurate information. Because LLMs’ accuracy is not guaranteed, the use of LLMs in clinical settings creates patient safety issues. In the future, LLMs are expected to significantly influence clinical practice, education, and research by developing models specific to medical use (Thacharodi et al., 2024).

 

AI in Healthcare

McBride and Tietze (2023) indicate that Artificial intelligence (AI) is becoming commonplace in healthcare, but there are concerns about the validity of AI. AI misinformation, specifically in research, needs to be vetted and verified before use (Wirth-Tomaszewski, 2025). Some practitioners are also concerned about how the use of AI could lead to misinformation about public health and could misrepresent physicians. Other healthcare providers view it differently, noting the strength of AI in analyzing large amounts of data or comparing a patient’s pathology to a database of images, and being able to make a diagnosis (Hudson, 2023). It should be noted that AI is a powerful tool, but it is only as good as its programming. If incomplete datasets are used to train AI, bias can be introduced by including existing prejudices around race or gender, for example. Addressing this ethical concern means that these programs must be free of errors. In addition, human oversight is needed since AI systems are not infallible (Juneja, 2022).Graphic of man in front of a computer

 

Integration of AI, using rule-based logic, or “expert systems” in the electronic health record (EHR) is common. Clinical Decision Support Systems used in EHRs are becoming more adept at moving knowledge to wisdom and offering diagnostic tools. AI also uses algorithms to prioritize tasks, automate communication between healthcare providers, and pull data, such as vital signs, from the EHR to determine, through the use of predictive analytics, the patient’s risk for deterioration (Wirth-Tomaszewski, 2025). These are examples of how AI is helping nurses provide efficient, effective, and safe care (McBride & Tietze, 2023) by automating tasks and using data to ensure patient safety (Wirth-Tomaszewski, 2025).

AI assists healthcare providers in analyzing medical images, vital signs, and laboratory tests, and facilitates rapid, earlier, and more accurate diagnoses. AI can also help reduce healthcare providers’ workload and burden, aid in decision-making, and facilitate more meaningful interactions and time with patients (Thacharodi et al., 2024). AI cannot replace training, skills, and cognitive abilities. However, it does simplify the healthcare provider’s role by synthesizing large amounts of data to solve complex problems and help innovate better solutions (McBride & Tietze, 2023).

 

Other Examples of AI in Healthcare

  • Decrease Documentation Burden: Charting tasks make up 40% of a nurse’s average workday (US Surgeon General, 2022). AI tools can record and transcribe nurse-patient interactions, generate nursing notes, and populate EHRs (Wei et al., 2025).
  • Optimizing Scheduling and Staffing: AI-driven scheduling software can reduce the time to complete a staff schedule by 40-50% (Morin, 2025). Advanced algorithms analyze staff availability, patient acuity, and historical workload patterns and create optimized schedules, improve staff satisfaction, and reduce overtime (Wei et al., 2025).
  • Manage Patient Inquiries: AI chatbots and virtual nursing assistants can manage routine patient inquiries, provide information, and answer frequently asked questions (Wei et al., 2025).

 

Robotics: A Deeper Understanding

Robots are machines or automated technologies capable of performing a series of actions, everything from driving cars to performing surgery. Robots have existed in the workplace for years, but their presence in healthcare is increasing, as are their capabilities. Today’s robots are designed to work alongside and move amongst human workers.

Robots are increasingly recognized within the surgical arena. Since its inception in 2000, the da Vinci surgical system has been utilized globally in over 6 million surgical procedures. Advantages of robot-assisted surgery include fewer incisions, less blood loss, and a quicker recovery. Robotics could potentially replace traditional endoscopy, directing small robots to specific areas to obtain a biopsy. Microrobots could be used within blood vessels to deliver radiation therapy to a narrow, targeted area. In addition, robotic capsules could be swallowed by patients, where data and diagnostic information can be collected and compiled as the robotic capsule passes through the digestive system. An emerging area of robotics revolves around nanorobots, advanced technology that could be equipped with receptors that bacteria can attach to, replacing the need for antibiotics (Thacharodi et al., 2024).

The nursing shortage is slated to exceed 12 million globally by 2034 (ANA, 2020). Nurses spend significant time on tasks that are not related to patients (Ohneberg et al., 2023), which takes nurses away from the bedside. Robotics, a subfield of AI, can address nursing shortages and understaffing (Soriano et al., 2022; Ohneberg et al., 2023) by performing high-repetition, non-clinical, low-risk, and low-intelligence tasks (ANA, 2020; Soriano et al., 2022; Vasquez et al., 2023).

 

Service robots use sensors to navigate corridors and elevators, and perform routine activities like: 

  • Delivering lab samples or medications to/from the pharmacy
  • Retrieve, restock, and deliver supplies
  • Dispose of trash or biohazard materials
  • Deliver meals, water, and linens to rooms

 

Photo of a nurse assisting robot
SMS by Clubfirst

Service robots can positively impact patient safety by ensuring that nurses stay on the unit, spend more time with their patients, and directly decrease distractions that nurses routinely experience (ANA, 2020). Robots can also improve patient outcomes by reducing staff workload & patient wait times (ANA, 2020; Soriano et al., 2022). In addition, robots can be used to support quality and safety initiatives, like monitoring those who have moderate or high fall risk and ensuring that slippers, signage, and a fall risk bracelet are in place (ANA, 2020).

In the future, service robots could potentially help with things like mobility (Ohneberg et al., 2023), taking vital signs, social stimulation, therapeutic play, room disinfection, and medicine administration (Soriano et al., 2022).

 

Watch this video on how robots are helping nurses in acute care hospitals

 

 

 

Other Emerging Healthcare Technologies

Advancements in technology are transforming the future of healthcare, leading to more personalized, efficient, and proactive approaches to patient care. Among the most groundbreaking developments are 3-D and 4-D printing, precision medicine, and predictive medicine. 3-D and 4-D printing enable the rapid creation of customized medical devices, anatomical models, and even living tissues. Precision medicine takes person-centered care a step further by using an individual’s genetic, environmental, and lifestyle information to guide treatment decisions. Likewise, predictive medicine leverages genetic testing and big data analytics to forecast disease risk and recommend interventions before symptoms appear. Together, these innovations can mark a shift from reactive to proactive and utilize the transformative power of technology.

 

3-D & 4-D Printing

While still in the early stages of development, 3-D printing technology is being utilized within the healthcare arena. The 3-D printer uses a digital blueprint, filament, and ultraviolet light to create a three-dimensional solid object. One of the major benefits of 3-D printing is that it does not require expensive equipment, something utilized in traditional manufacturing, which significantly reduces the time it takes to create the product. For example, hearing aids can be produced in 1 day, rather than one week (American Hospital Association, 2025).

Some of the ways 3-D printing has been effective are:

      1. Implants and prosthetics: The FDA has approved 3-D technology to develop bones, cartilage, and affordable prosthetics.
      2. Anatomical models: 3-D printers have been highly successful in surgical applications, specifically in creating detailed anatomical models and surgical guides. These models and guides have been connected to a reduction of surgical time and, as a result, a reduction in cost.
      3. Medical equipment: Fabrication of instruments like forceps, clamps, hemostats, and retractors is the most common use of 3-D printing, helping to mitigate supply chain shortages. A key benefit is that precise design modifications can be made rapidly based on surgeon feedback (American Hospital Association, 2025). During COVID-19, 3-D printing technology was used to develop low-cost ventilators. It took 3-8 hours and less than $10 of material to print each ventilator, which was then made operational with the addition of low-cost springs available at any hardware store (Oregon Health & Science University, 2020).

 

Research into additional ways 3-D printing can benefit healthcare and patients is also showing promise. Research has been, and continues to be, conducted on 3-D printing being utilized to develop bio-printed tissues like blood vessels, bones, and organs (American Hospital Association, 2025), such as a collarbone, a cornea, and heart valves (Thacharodi et al., 2024).

4-D printing is emerging as a future healthcare technology. 4-D printing adds a “fourth dimension,” enabling the creation of products that can change shape or function over time. 4-D printing also allows for organ component customization that matches a patient’s unique anatomy, which has been utilized to create earlobes, eyeglasses, windpipes, and jawbones (Thacharodi et al., 2024).

 

Precision Medicine

Precision medicine is an “emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person” (Garrido et al., 2018). The goal of precision medicine is to tailor healthcare treatment plans to the genetic uniqueness of each patient, thereby achieving improved outcomes. At the core of precision medicine is the integration of genomic information, where an individual’s genetic makeup is analyzed to unveil insights into disease susceptibility, response to medications, and inherent genetic factors influencing health (University of Alabama at Birmingham & The Office of the National Coordinator for Health Information Technology, n.d.).

For example, the anticoagulant Plavix works well for most patients, but not all. In order for clopidogrel to work effectively, a protein called CYP2C19 has to convert the medication to an active form, and how well this protein works is based on genetics (University of Florida Health, 2014). If healthcare providers know that a patient has a gene that decreases Plavix effectiveness, rather than using Plavix, a different treatment plan could be selected immediately, rather than wasting time with Plavix treatment. Not only is this less risky for the patient, but it is likely to save costs as well (University of Alabama at Birmingham & The Office of the National Coordinator for Health Information Technology, n.d.).

Precision medicine allows healthcare professionals to develop personalized treatment plans. By tailoring interventions to the specific needs of each patient, precision medicine aims to optimize treatment effectiveness, minimize adverse effects, and improve overall health outcomes. Precision medicine holds great promise in revolutionizing healthcare, moving away from the traditional, one-size-fits-all model toward a more individualized and targeted approach to medical care. This approach is particularly promising in areas such as cancer treatment, where targeted therapies based on genetic profiles can lead to more effective and less toxic interventions.

 

Predictive Medicine

Predictive medicine is the science of accurately identifying an individual’s risk for developing a disease within a specific time frame. Historically, predictive capabilities have revolved around genetics, for example, testing for Down Syndrome or breast cancer (Jen et al., 2025). The goal in genetic testing is to use biomarkers to predict an individual’s risk for developing a clinical disorder, predict the most effective treatment, and then intervene before the patient develops the condition.

The scope of predictive medicine is expanding to include conditions beyond genetic predictions, ushering in a new era of predictive medicine based on “big data,” huge quantities of data obtained from a variety of sources (Jen et al., 2025). Considering the massive amounts of healthcare data that are collected, stored, and processed, and the new analytical techniques available, predictive medicine is one of the most promising applications of informatics. Predictive tools based on big data have the potential to help clinicians better predict who will develop illness and when, and how best to intervene. Though predictive medicine using big data is still in its infancy within healthcare, big data has been utilized in business. For example, the major retailer, Target Corporation, has already developed a big-data informatics system that predicts when a customer is pregnant and subsequently tailors its marketing efforts accordingly (Jen et al., 2025).

Using the power of big data, the future of predictive medicine is bright. Providers may soon be able to individualize the risk for a variety of health outcomes and determine individualized treatment options for patients. For predictive medicine to be successful, however, significant efforts will be needed to purchase, develop, and refine the necessary information technology infrastructure.

 

 

Conclusion

Artificial Intelligence and other evolving healthcare technologies are not just enhancing healthcare; they are redefining it. From reducing the documentation burden and improving diagnostic accuracy to enabling personalized treatments and advancing surgical precision, AI offers powerful tools to address pressing healthcare challenges, including workforce shortages and the rising complexity of care. As these technologies continue to evolve, they must be guided by ethical oversight, transparency, and a commitment to patient safety. While AI cannot, and should not, replace the human connection central to nursing and healthcare, it can empower healthcare providers to focus more on what matters most: meaningful patient care. Embracing innovation while maintaining professional judgment and compassion will be essential as we move into the future.

 

 

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The Future of Healthcare Technology Copyright © 2025 by Laura K. Garner-Jones is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, except where otherwise noted.

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