Evolving Foundations

Artificial Intelligence: Basics, Impact, and How Nurses Can Contribute

Britney Starr

Erin Dickman

Joni L. Watson
artificial intelligence, machine learning, deep learning, technology
CJON 2023, 27(6), 595-601. DOI: 10.1188/23.CJON.595-601

Applying artificial intelligence (AI) to cancer care has the potential to transform and enhance nursing practice and patient outcomes, from cancer prevention and screening through treatment, survivorship, and end-of-life care. As the largest healthcare workforce, nurses record a significant amount of patient data used to train healthcare AI tools and are a large percentage of AI end users. Educational opportunities are available to assist nurses in understanding the benefits, limitations, and ethical considerations of this technology and how AI results are directly affected by the quality of nursing documentation. Applying nursing clinical knowledge and critical thinking skills throughout the AI life cycle will enhance nursing workflows and increase positive patient outcomes.

AT A GLANCE

  • Oncology nurses require basic knowledge about AI to keep pace with healthcare technology advancements. 
  • AI applied to health care has benefits, limitations, and ethical considerations.
  • Oncology nurses’ expertise and partnership with technology teams throughout the AI development life cycle can improve end products and results.

Jump to a section

    Artificial intelligence (AI) experts have deemed AI a “revolutionary technology” (Mills, 2020, para. 2) that is projected to radically change health care (Zhou et al., 2023). The healthcare AI market is a $14.6 billion enterprise and is expected to reach $102.7 billion by 2028 (MarketsandMarkets, 2023). AI has the potential to greatly affect oncology care, including cancer prevention and screening, treatment, survivorship, and end-of-life care (Tawfik et al., 2023). Nurses comprise the largest healthcare workforce, record a significant amount of patient data, and are a large percentage of AI end users. Professional organizations, such as the American Nursing Informatics Association, the American Nurses Association (ANA), and the Nursing and Artificial Intelligence Leadership Collaborative, acknowledge the ever-intertwining relationship between nursing and technology, highlighting AI and its importance. These organizations advocate for nurses to learn about AI, claim a seat at the development table, and establish a knowledge base about how these solutions can enhance their practice (ANA, 2022; American Nursing Informatics Association, n.d.; Ronquillo et al., 2021). Nurses are essential participants in healthcare technology development; however, in a study of U.S. nurses, 70% of nurses indicated that they had little to no knowledge of AI technologies or uses (Swan, 2021). To expertly implement AI solutions that support health care and optimize workflow and patient outcomes, nurses can build a foundation of AI knowledge (Ronquillo et al., 2021).

    Although they are often invisible to clinicians, many AI tools have been integrated into health care, such as clinical decision-making support models, remote patient monitoring and virtual sitters, ambulatory scheduling optimization applications, predictive staffing models, inpatient operational flow platforms, and flags highlighting suspicious areas on radiology images (Ronquillo et al., 2021; Swan, 2021). Nursing actions and involvement can directly affect healthcare AI outcomes. When nurses are not involved in the AI design process, AI-supported technology can be frustrating to use, complicating workflows and interfering with the delivery of optimal patient care (Ronquillo et al., 2021). This article discusses foundational AI concepts, provides a brief overview of AI applications in cancer care, and identifies nurses as active participants in AI development.

    The Basics of AI

    As a broad term, AI is used “to classify machines that mimic human intelligence and human cognitive functions like problem-solving and learning” (IBM Data and AI Team, 2023, para. 6). AI is used when a large amount of data is available and the goal is to recognize patterns to provide insights and make predictions (IBM Design for AI, 2022a). This technology is largely rooted in probability mathematics and is data hungry, meaning the more high-quality data provided, the better the results. Using AI is helpful in contexts where the rules that apply to a situation are complex and difficult to identify, such as in cancer diagnosis (IBM Design for AI, 2022a).

    AI includes numerous branches and subsets (see Figure 1). One subset is machine learning (ML), which involves training a machine on how to accomplish a specific task using a large amount of data without providing exact rules or instructions (i.e., coding or programming) (Gupta et al., 2021; IBM Design for AI, 2022b). This made ML revolutionary in the field of computer science. Like humans, ML can use a variety of learning methods including supervised, unsupervised, semisupervised, and reinforcement learning (Gupta et al., 2021; IBM Design for AI, 2022b). One of the subsets of ML is deep learning, which uses artificial neural networks to process large amounts of unstructured data and find meaningful patterns or clusters (IBM Data and AI Team, 2023). This technology performs several rounds of math on input data until it predicts an output. Deep refers to the number of neural networks, or layers, that are used (IBM Data and AI Team, 2023).

    FIGURE1

    Unstructured human language (e.g., text and voice data) creates challenges in data analytics because of the lack of standardization and unpredictability (e.g., slang terms, misspellings). In an effort to improve the ability to analyze this type of information, natural language processing uses deep learning to transform unstructured data into quantitative data (i.e., data that can be counted or measured) (Shreve et al., 2022). Sophisticated large language models, such as ChatGPT (OpenAI), Bard (Google), Bing Image Creator (Microsoft), and AudioCraft (Meta), use natural language processing to analyze large amounts of human input data and produce insights in a variety of forms (e.g., text, photos, music) (Eramo, 2023; Kung et al., 2023; Martineau, 2023). Large language models that provide text output can compile content that resembles human-written essays. There are even AI models that have taken the United States Medical Licensing Examination and “performed at or near the passing threshold of 60% accuracy” (Kung et al., 2023, p. 1). Large language models are sometimes called “generative AI” because they have the ability to generate new insights in an objective way based on existing data (Eramo, 2023).

    AI has been increasingly interwoven throughout daily life. Digital voice assistants (e.g., Siri [Apple], Alexa [Amazon]), personalized recommendation algorithms (used by Amazon, Google, and others), predictive text in emails, chatbots, and self-driving vehicles are just a few examples. Table 1 provides additional key AI terms, definitions, and examples to support AI literacy.

    TABLE1

    Impact on Oncology Care

    As the global cancer burden increases, oncology care can benefit from AI advancement (Luchini et al., 2022; Zhou et al., 2023). With the assistance of AI, researchers are discovering new approaches for drug development, precision oncology, cancer detection, and screening (Luchini et al., 2022). Within oncology imaging, a large number of U.S. Food and Drug Administration–approved AI devices are using advanced vision-assisted image analysis (Luchini et al., 2022). This technology flags concerning areas in radiographic imaging to assist radiologists as they detect, diagnose, and monitor cancer (Cheng et al., 2021). Using an AI tool, Koch et al. (2023) completed a retrospective analysis of breast cancer screening samples and found that 40% of breast cancer cases could have been detected earlier by using AI. Another application example includes an AI tool developed to rapidly analyze skin photos to identify early-stage melanoma. The AI tool’s performance in assessing skin lesions was comparable to assessment by board-certified dermatologists (Soenksen et al., 2021).

    Precision oncology that supports individualized cancer care is gaining more attention from healthcare providers. Using the power of natural language processing, advancements in precision oncology have been achieved, such as matching patients with appropriate clinical trials quickly or identifying potential adverse drug reactions (Shreve et al., 2022). An emerging strategy for personalized early cancer detection care is pan-cancer screening, which uses AI to analyze whole blood samples and identify circulating tumor DNA. This method may be cost-effective because of the potential to identify rare cancers sooner (Shreve et al., 2022).

    Massive amounts of structured (e.g., vital signs, laboratory values) and unstructured (e.g., clinician notes) healthcare data are stored in electronic health records (EHRs). Today, 96% of U.S. hospitals use EHRs (Diaz, 2023). AI can enhance clinician decision-making support tools by analyzing EHR data to predict future acute events and prompt clinicians to act, which can lead to better patient outcomes (Hong et al., 2020; Shreve et al., 2022). For example, System for High-Intensity Evaluation During Radiation Therapy is a radiation oncology ML solution that identifies patients at high risk for complications and advises mandatory twice-weekly clinic visits by analyzing a patient’s pretreatment EHR history and treatment plan. Hong et al. (2020) found that System for High-Intensity Evaluation During Radiation Therapy reduced rates of acute care events during treatment by 10%.

    Implications for Nursing

    Nurses interact daily with AI tools (e.g., clinical decision support models). These tools include best practice alerts, mobile health and sensor-based technologies (e.g., remote telemetry monitoring), voice assistants, robots performing non-nursing tasks (e.g., Moxi by Diligent Robotics), and workflow optimization models (e.g., iQueue®), among others (Douthit et al., 2022). The application of AI may reduce labor-intensive nursing workloads and provide technology to better support nurses who have been affected by the nursing shortage (Zhou et al., 2023).

    Nurses can contribute to all stages of AI development by identifying clinical issues that may be delegated to AI (e.g., ambulatory patient scheduling), providing insight into what specific documentation should be included in training data, and providing crucial feedback (e.g., “This best practice alert is firing too frequently, causing alert fatigue”) (ANA Center for Ethics and Human Rights, 2022; Ronquillo et al., 2021). Nursing feedback provides AI experts with information that can be used to retrain AI models and improve the accuracy of results (Ronquillo et al., 2021). Combined with evidence-based clinical nursing knowledge, AI’s efficiency and power has the potential to further elevate nursing practice and improve patient outcomes (ANA, 2022). Professional nursing responsibility includes building a knowledge base about using healthcare data in the AI development life cycle and advocating for patient-centric technology solutions to improve outcomes and workflows (ANA Center for Ethics and Human Rights, 2022; Ronquillo et al., 2021). 

    An example of nurses identifying a clinical problem with an AI solution was when nurses recognized that a significant number of patients with breast cancer were unsatisfied with prechemotherapy treatment education (Tawfik et al., 2023). They conducted a randomized controlled trial comparing an AI chatbot versus nurse-led prechemotherapy patient education. The results suggested a chatbot was a “useful and cost-effective tool” to provide pretreatment education and address simple questions. Patients appreciated the around-the-clock chatbot’s individualized care versus timed sessions with a nurse’s one-size-fits-all approach (Tawfik et al., 2023). For additional examples, see Figure 2, which presents two case studies highlighting the involvement of nursing team members in the development and implementation of AI tools.

    FIGURE2

    One of the biggest contributions nurses can provide in AI development is accurate and high-quality documentation (Ronquillo et al., 2021). Because nurses generate a significant amount of data within the EHR, understanding the concept of the technology phrase “garbage in, garbage out” is important. This phrase emphasizes that no matter how accurate the AI model is, the quality of the result is only as good as the input data (Awati, 2023). Therefore, missing or incomplete nursing documentation can cause AI results to fall short, possibly complicating workflows and adversely affecting patient outcomes (Ronquillo et al., 2021). Even if current EHR data are not being processed by an AI tool, nursing documentation could be used in the future to train AI models, highlighting the importance of complete and accurate charting. Seeking educational opportunities provided by informatics and AI experts will further strengthen nurses’ understanding of this concept (Ronquillo et al., 2021). Figure 3 provides a list of AI education and resources.

    FIGURE3

    Although healthcare AI tools are showing promise, AI is not a solution for every problem (M. Connor, Y. Chung Jr., & K. Fessele, personal communication, July 26, 2023). AI may be a solution if the problems identified have appropriate supporting electronic data to build and train AI programs. Informatics nurses and technology team experts assist in determining whether AI is the right solution to specific problems (ANA, 2022; M. Connor, Y. Chung Jr., & K. Fessele, personal communication, July 26, 2023).

    Ethical Considerations

    New technology and innovation can present ethical considerations. For example, two ethical considerations related to AI are the potential for bias and the absence of full regulation (Igoe, 2021). Because AI learns from training data generated by humans with implicit and explicit biases, unintentional bias can occur, exacerbating social inequities (Igoe, 2021). Creating a diverse development team can help address this bias. Healthcare professionals, including nurses, have a unique understanding of patients and the healthcare system. Their input can help ensure training data are representative of the issue and target audience, ultimately playing a part in preventing unintentional bias (Ronquillo et al., 2021).

    In addition to the progressiveness of AI, the speed of advancement raises security, quality, and safety concerns (Chou, 2023). Therefore, regulations should focus on ensuring rigorous testing and security measures while limiting burdens on the development process (Chou, 2023). Nurses can advocate at the federal, state, and local levels for the profession and patients by raising awareness of responsible AI development and usage.

    Conclusion

    As healthcare systems continue to move toward efficient and personalized care, AI solutions may be used more frequently, which will directly affect nurses. Professional nursing organizations acknowledge the gap between technology development and nursing and request that nurses join the AI discussion and development process. Educational opportunities focusing on AI assist nurses in building foundational knowledge and understanding of how they can contribute. Applying nursing clinical knowledge and critical thinking skills throughout the AI life cycle can elevate nursing practice and aid in the development of products that will meaningfully improve patient outcomes.

    The authors gratefully acknowledge Monica Munaretto, MSN, RN, OCN®, from City of Hope Medical Center in Duarte, CA, and MaryAnn Connor, MSN, RN-BC, CPHIMS, FAMIA, FHIMSS, “Jay” You Chung Jr., MSN, RN-BC, CCRN-K, and Kristen Fessele, PhD, RN, ANP-BC, AOCN® from Memorial Sloan Kettering Cancer Center for sharing their experiences with artificial intelligence.

    About the Authors

    Britney Starr, BSN, RN, OCN®, and Erin Dickman, DNP, RN, OCN®, are oncology clinical specialists at the Oncology Nursing Society in Pittsburgh, PA; and Joni L. Watson, DNP, MBA, RN, OCN®, is the chief vision officer at the Creating Collective in Dallas, TX, and a consulting associate faculty member in the School of Nursing at Duke University in Durham, NC. The author takes full responsibility for this content and did not receive honoraria or disclose any relevant financial relationships. Starr can be reached at bstarr@ons.org, with copy to CJONEditor@ons.org.

    References

    American Nurses Association. (2022). Nursing informatics: Scope and standards of practice (3rd ed.).

    American Nursing Informatics Association. (n.d.). Strategic plan. https://www.ania.org/about-us/strategic-plan

    ANA Center for Ethics and Human Rights. (2022). The ethical use of artificial intelligence in nursing practice. https://www.nursingworld.org/~48f653/globalassets/practiceandpolicy/nur…

    Awati, R. (2023). Garbage in, garbage out (GIGO). TechTarget. https://www.techtarget.com/searchsoftwarequality/definition/garbage-in-…

    Cheng, P.M., Montagnon, E., Yamashita, R., Pan, I., Cadrin-Chênevert, A., Romero, F.P., . . . Tang, A. (2021). Deep learning: An update for radiologists. RadioGraphics, 41(5), 1427–1445.

    Chou, D. (2023, July 26). Big tech’s AI commitment falls short for healthcare. Forbes. https://www.forbes.com/sites/davidchou/2023/07/26/big-techs-ai-commitme…

    Diaz, N. (2023, March 7). 96% of US hospitals have EHRs, but barriers remain to interoperability, ONC says. Becker’s Health IT. https://www.beckershospitalreview.com/ehrs/96-of-us-hospitals-have-ehrs…

    Douthit, B.J., Shaw, R.J., Lytle, K.S., Richesson, R.L. & Cary, M.P., Jr. (2022, January 11). Artificial intelligence in nursing. American Nurse. https://www.myamericannurse.com/ai-artificial-intelligence-in-nursing

    Eramo, L.A. (2023, June 27). Leveraging ChatGPT and generative AI in healthcare analytics. Journal of AHIMA. https://journal.ahima.org/page/leveraging-chatgpt-and-generative-ai-in-…

    Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R.K., & Kumar, P. (2021). Artificial intelligence to deep learning: Machine intelligence approach to drug discovery. Molecular Diversity, 25(3), 1315–1360. https://doi.org/10.1007/s11030-021-10217-3

    Hong, J.C., Eclov, N.C.W., Dalal, N.H., Thomas, S.M., Stephens, S.J., Malicki, M., . . . Palta, M. (2020). System for high-intensity evaluation during radiation therapy (SHIELD-RT): A prospective randomized study of machine learning-directed clinical evaluations during radiation and chemoradiation. Journal of Clinical Oncology, 38(31), 3652–3661. https://doi.org/10.1200/jco.20.01688

    IBM Data and AI Team. (2023, July 6). AI vs. machine learning vs. deep learning vs. neural networks: What’s the difference? IBM. https://ibm.co/3tUgMIJ

    IBM Design for AI. (2022a). Artificial intelligence. https://www.ibm.com/design/ai/basics/ai

    IBM Design for AI. (2022b). Machine learning. https://www.ibm.com/design/ai/basics/ml

    Igoe, K.J. (2021). Algorithmic bias in health care exacerbates social inequities—How to prevent it. Harvard T.H. Chan School of Public Health. https://www.hsph.harvard.edu/ecpe/how-to-prevent-algorithmic-bias-in-he…

    Koch, H.W., Larsen, M., Bartsch, H., Kurz, K.D., & Hofvind, S. (2023). Artificial intelligence in BreastScreen Norway: A retrospective analysis of a cancer-enriched sample including 1254 breast cancer cases. European Radiology, 33(5), 3735–3743. https://doi.org/10.1007/s00330-023-09461-y

    Kung, T.H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., . . . Tseng, V. (2023). Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLOS Digit Health, 2(2), e0000198. https://doi.org/10.1371/journal.pdig.0000198

    Luchini, C., Pea, A., & Scarpa, A. (2022). Artificial intelligence in oncology: Current applications and future perspectives. British Journal of Cancer, 126(1), 4–9.

    MarketsandMarkets. (2023). Artificial intelligence in healthcare market by offering (hardware, software, services), technology (machine learning, NLP, context-aware computing, computer vision), application, end user and region—Global forecast to 2028. https://www.marketsandmarkets.com/Market-Reports/artificial-intelligenc…

    Martineau, K. (April 20, 2023). What is generative AI? IBM. https://research.ibm.com/blog/what-is-generative-AI

    Mills, T. (2020, January 15). How AI is revolutionizing health care. Forbes. https://www.forbes.com/sites/forbestechcouncil/2020/01/15/how-ai-is-rev…

    Ronquillo, C.E., Peltonen, L.-M., Pruinelli, L., Chu, C.H., Bakken, S., Beduschi, A . . . Topaz, M. (2021). Artificial intelligence in nursing: Priorities and opportunities from an international invitational think tank of the Nursing and Artificial Intelligence Leadership Collaborative. Journal of Advanced Nursing, 77(9), 3707–3717. https://doi.org/10.1111/jan.14855

    Shreve, J.T., Khanani, S.A., & Haddad, T.C. (2022). Artificial intelligence in oncology: Current capabilities, future opportunities, and ethical considerations. American Society of Clinical Oncology Educational Book, 42, 1–10. https://doi.org/10.1200/EDBK_350652

    Soenksen, L.R., Kassis, T., Conover, S.T., Marti-Fuster, B., Birkenfeld, J.S., Tucker-Schwartz, J., . . . Gray, M.L. (2021). Using deep learning for dermatologist-level detection of suspicious pigmented skin lesions from wide-field images. Science Translational Medicine, 13(581).

    Swan, B.A. (2021). Assessing the knowledge and attitudes of registered nurses about artificial intelligence in nursing and health care. Nursing Economics, 39(3), 139–143.

    Tawfik, E., Ghallab, E., & Moustafa, A. (2023). A nurse versus a chatbot—The effect of an empowerment program on chemotherapy-related side effects and the self-care behaviors of women living with breast cancer: A randomized controlled trial. BMC Nursing, 22(1), 102.

    Zhou. T., Luo, Y., Li, J., Zhang, H., Meng, Z., Xiong, W., & Zhang, J. (2023). Application of artificial intelligence in oncology nursing: A scoping review. Cancer Nursing.