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Cited by 3Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AIPalliative and Oncologic Care

Empathic and agentic artificial intelligence in nursing: perspectives on a human-centered framework for cancer care navigation in the United States of America

Open access
Girdwood, T., Kheirinejad, S., Kheirkhah, P., White, B., Davis, R., Schwartz, D., Shaban-Nejad, A.·2026·ESMO Real World Data and Digital Oncology
DOI 10.1016/j.esmorw.2026.100694PMC PMC13000476
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Abstract

For patients experiencing cancer, nurse navigation can ease the burden of complex care by enhancing coordination of health services and patient outcomes. However, in under-resourced areas, trained nurse navigators may be limited or non-existent. In the United States, artificial intelligence (AI)-enabled digital health tools are increasingly available and may help address gaps in care coordination; however, most are not designed to specifically support nursing. This perspective piece discusses a human-centered AI framework that integrates empathic and agentic approaches grounded in the American Nurses Association's code of ethics to support nurses in the United States in cancer care navigation. The framework could augment, not replace, human empathy and agency while improving nurse workflow, patient-clinician relationships, and care coordination services in under-resourced areas.

AI snapshot

Written from the paper’s full text.

TL;DR

This perspective proposes a human-centered AI framework for cancer care navigation in the United States, integrating empathic AI (detecting and responding to patients' emotional/psychosocial cues) and agentic AI (proactively handling documentation, care coordination, and communication) grounded in the American Nurses Association (ANA) Code of Ethics. The framework is intended to augment, not replace, human empathy and nursing agency, particularly in under-resourced areas lacking specialty-trained nurse navigators.

Key findings

  • Poor care coordination in the US is estimated to cost $27–78 billion annually in hospitalizations and readmissions, motivating AI-supported navigation support (cited estimate, not generated by this paper).
  • The proposed framework has three mutually reinforcing layers: (i) strict compliance of AI design, implementation, and governance with the ANA Code of Ethics (10 provisions covering compassion, dignity, advocacy, accountability, integrity); (ii) a dual empathic + agentic AI core; and (iii) intentional nurse oversight keeping clinicians in control of all patient-impacting decisions.
  • Six categories of existing oncology AI navigation platforms/tools (e.g., Care.AI, Fidari, MyEleanor, iNAV, MUSC Health) are catalogued as candidate testbeds for validating the framework.
  • The authors identify risks including data privacy/security breaches, algorithmic bias and inequitable behavior across cultural, linguistic, and socioeconomic groups, absence of established clinical guidelines or legal precedents for accountability in AI-assisted nursing decisions, and potential dehumanization of patient–clinician relationships.

Methods

  • Conceptual perspective piece: no empirical data collection, sample, or statistical analysis; the framework was developed from published literature on nursing, AI, and ethics.
  • Framework synthesis grounded in the ANA Code of Ethics, illustrated in a figure created with an AI tool ("Use.AI," per the generative-AI declaration).
  • Narrative mapping of existing commercial/health-system AI navigation platforms (Table 1) as possible settings for future framework validation.

Limitations

  • Stated by the authors: the framework requires formal validation testing in clinical navigation settings to identify misalignments or errors, data privacy vulnerabilities, algorithmic biases, and workflow inefficiencies before scaled adoption; and its translation to non-US healthcare systems with different workforce models and regulatory environments is unexamined.
  • Stated: no widely established clinical guidelines or legal precedents clearly define accountability for AI-assisted nursing decisions, leaving liability and scope-of-practice questions unresolved.
  • Evident from the text (not explicitly labeled a limitation): the piece is entirely conceptual — no empirical evidence is presented that the framework improves nurse workflow, patient outcomes, or care coordination; claims about benefits remain prospective.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1016/j.esmorw.2026.100694

Cites

21 of 21 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

32 of 61 cited sources checked · 2 supported · 0 flagged · accuracy not yet measured

  • Premisecitation

    Nurse navigators frequently lack sustainable funding.

    “However, navigators frequently lack institutional support, sustainable funding, and formal recognition of their specialized skillset.2”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [2] Chen, 2025, Toward bridging gaps in patient navigation: a study on the adoption of artificial intelligence technologies, J Oncol Navig Surviv source

      Section 1.5 (page unavailable) says patient navigators and their programs are underfunded, supporting the funding concern. It does not establish that nurse navigators specifically frequently lack sustainable funding; the Results section describes a broader mix of navigation professionals. (LLM, conf=0.84)

      Experimental — accuracy not yet measured
  • Premisecitation

    Nurse navigators frequently lack formal recognition of their specialized skillset.

    “However, navigators frequently lack institutional support, sustainable funding, and formal recognition of their specialized skillset.2”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [2] Chen, 2025, Toward bridging gaps in patient navigation: a study on the adoption of artificial intelligence technologies, J Oncol Navig Surviv source

      Section 1.5 describes patient navigators and their programs as “under supported,” “underfunded,” and “undervalued.” That supports the general concern about recognition, but it does not specifically establish that nurse navigators frequently lack formal recognition of their specialized skillset. (LLM, conf=0.85)

      Experimental — accuracy not yet measured
  • Premisecitation

    Nurse navigators frequently lack institutional support.

    “However, navigators frequently lack institutional support, sustainable funding, and formal recognition of their specialized skillset.2”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [2] Chen, 2025, Toward bridging gaps in patient navigation: a study on the adoption of artificial intelligence technologies, J Oncol Navig Surviv source

      Section 1.5 says patient navigators and their programs are under-supported, underfunded, and undervalued. The Discussion identifies a lack of institutional support as a possible reason for limited technology use. Neither passage establishes how frequently nurse navigators specifically lack institutional support, nor does it address sustainable funding or formal recognition of their specialized skills. (LLM, conf=0.82)

      Experimental — accuracy not yet measured
  • Premisecitation

    AI-enabled digital health tools are maturing rapidly.

    “AI-enabled digital health tools are maturing rapidly and can serve to supplement care access where there are fewer providers physically available, particularly in under-resourced regions.3”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [3] Huhulea, 2025, Artificial intelligence advancements in oncology: a review of current trends and future directions, Biomedicines source

      The Introduction and Conclusions describe rapid advances in oncology AI and its potential to expand access to cancer care. Section 6 also discusses healthcare disparities. None of these passages addresses supplementing care where providers are physically scarce, particularly in under-resourced regions; the paper’s support is narrower than the full citing sentence. (LLM, conf=0.84)

      Experimental — accuracy not yet measured
  • Premisecitation

    AI-powered chatbots or virtual assistants can reach out to patients autonomously between clinical visits.

    “For example, AI-powered chatbots or virtual assistants can reach out to patients autonomously between clinical visits, detect early indicators of anxiety or depression, and escalate concerns so nurses can intervene proactively and efficiently.13,14”
    Quote in the paperSubstantial claim2 of 2 sources checked
    • cites [13] Gill, 2024, Empathy and AI: cognitive empathy or emotional (affective) empathy?, AI Soc source

      In the paper’s main text (page not available), Gill discusses robotic assistants in healthcare and patient homes, and machine algorithms used to monitor patients. It does not say that chatbots or virtual assistants autonomously contact patients between clinical visits. (LLM, conf=0.82)

      Experimental — accuracy not yet measured
    • cites [14] Mohanasundari, 2023, Can artificial intelligence replace the unique nursing role?, Cureus source

      The Review (page unavailable) says AI-powered virtual assistants can provide continuous patient support, including reminders and monitoring. It does not specify that they autonomously initiate contact between clinical visits. (LLM, conf=0.83)

      Experimental — accuracy not yet measured
  • May combine two claims. 5
  • Classified as probably not a substantial claim. 6

  • May combine two claims.

    Premisecitation

    Algorithmic bias and inequitable system behavior are issues in AI-enabled care.

    “Equally concerning is the issue of algorithmic bias and inequitable system behavior.”
    Quote in the paperSubstantial claim1 of 3 sources checked
    • cites [5] Wei, 2025, The integration of AI in nursing: addressing current applications, challenges, and future directions, Front Med source
    • cites [21] Raza, 2024, Exploring bias and prediction metrics to characterise the fairness of machine learning for equity-centered public health decision-making: a narrative review, IEEE Access source
    • cites [22] Shaban-Nejad, 2021, Explainability and interpretability: keys to deep medicine source

  • Classified as probably not a substantial claim.

    Premisecitation

    The ANA Code of Ethics is the standard for ethical nursing practice.

    “The ANA Code of Ethics is the standard for ethical nursing practice and includes 10 provisions that encompass compassion, respect for human dignity, commitment to care, trust, advocacy, responsibility, accountability, and integrity.10”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [10] Kerasidou, 2020, Artificial intelligence and the ongoing need for empathy, compassion and trust in healthcare, Bull World Health Organ source

      The “Empathy, compassion and trust” section describes values of good health-care practice, but does not discuss the ANA Code of Ethics or identify it as the standard for ethical nursing practice. Searches for the ANA Code and its provisions found no passage addressing that claim in our copy. (LLM, conf=0.76)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The ANA Code of Ethics includes 10 provisions that encompass compassion, respect for human dignity, commitment to care, trust, advocacy, responsibility, accountability, and integrity.

    “The ANA Code of Ethics is the standard for ethical nursing practice and includes 10 provisions that encompass compassion, respect for human dignity, commitment to care, trust, advocacy, responsibility, accountability, and integrity.10”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [10] Kerasidou, 2020, Artificial intelligence and the ongoing need for empathy, compassion and trust in healthcare, Bull World Health Organ source

      The “Empathy, compassion and trust” section describes those values as fundamental to health care, but does not connect them to the ANA Code of Ethics. Searches for the ANA Code, its ten provisions, and the listed nursing obligations found no passage addressing the code or its contents. (LLM, conf=0.76)

      Experimental — accuracy not yet measured

  • May combine two claims.

    Premisecitation

    Nurse navigation improves patient satisfaction and health outcomes.

    “Nurse navigation is a specialized service that addresses barriers to care, enhances care coordination across the cancer pathway, reduces healthcare costs, and improves patient satisfaction and health outcomes.1”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [1]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    Together, the framework's layers create a continuous, human-in-the-loop process where human empathy guides AI action and amplifies nursing capacity.

    “Together, these layers create a continuous, human-in-the-loop process where human empathy guides AI action and amplifies nursing capacity.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: In under-resourced areas in the United States, patient access to specialty-trained nurse navigators may be limited or unavailable.

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Interpretationcitation

    Cancer care navigation could uniquely benefit from novel integration of human-centered AI tools.

    “Cancer care navigation could uniquely benefit from novel integration of human-centered AI tools.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.

    Interpretationreasoning

    The framework could augment, not replace, human empathy and agency while improving nurse workflow, patient-clinician relationships, and care coordination services in under-resourced areas.

    “The framework could augment, not replace, human empathy and agency while improving nurse workflow, patient–clinician relationships, and care coordination services in under-resourced areas.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: In under-resourced areas in the United States, patient access to specialty-trained nurse navigators may be limited or unavailable.

  • May combine two claims.

    Premisecitation

    AI in cancer care navigation has crucial limitations and serious ethical risks.

    “However, there remain crucial limitations and serious ethical risks that must be acknowledged and proactively managed.20”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [20] Badawy, 2025, Navigating ethical considerations in the use of artificial intelligence for patient care: a systematic review, Int Nurs Rev source

  • May combine two claims.

    Interpretationreasoning

    Breaches, unauthorized access, or secondary data misuse could lead to significant patient harm, erosion of trust, and violation of regulatory standards like HIPAA.

    “Breaches, unauthorized access, or secondary data misuse could lead to significant patient harm, erosion of trust, and violation of regulatory standards like the Health Insurance Portability and Accountability Act.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    There is a potential for dehumanization of care in AI-enabled nursing.

    “Moreover, the potential for dehumanization of care must be proactively addressed.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Empathic AI systems lack genuine empathy, moral reasoning, or relational awareness.
10 of 10

  • The paper relies on this without support.

    In under-resourced areas in the United States, patient access to specialty-trained nurse navigators may be limited or unavailable.

    “In under-resourced areas in the United States (defined as settings lacking access to essential health resources like rural areas, communities experiencing significant health disparities, and underserved populations), patient access to specialty-trained nurse navigators may be limited or unavailable.”

  • The paper relies on this without support.

    Currently, there are no widely established clinical guidelines or legal precedents to clearly define accountability for AI-assisted decisions in nursing practice.

    “Currently, there are no widely established clinical guidelines or legal precedents to clearly define accountability for AI-assisted decisions in nursing practice.”

  • The paper relies on this without support.

    Empathic AI systems lack genuine empathy, moral reasoning, or relational awareness.

    “Although empathic AI systems can simulate emotional understanding, they lack genuine empathy, moral reasoning, or relational awareness.”

  • The paper relies on this without support.

    AI systems rely on large volumes of sensitive patient information like medical and social risk histories.

    “AI systems rely on large volumes of sensitive patient information like medical and social risk histories”
4 of 4

Cites

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Cited by

Still being checked.

Authors
Girdwood, T., Kheirinejad, S., Kheirkhah, P., White, B., Davis, R., Schwartz, D., & Shaban-Nejad, A.
Affiliations
University of Tennessee Health Science Center (US)
Corresponding author
Girdwood, T.
ORCID
Venue
ESMO Real World Data and Digital Oncology
Publisher
Elsevier BV
Published
2026-06
Volume
12
Pages
100694
ISSN
2949-8201 (ISSN-L)
DOI
10.1016/j.esmorw.2026.100694
PMC
PMC13000476
OpenAlex
W7135183121
Semantic Scholar
02aee9c9cc0f7918489016aa17d05a26c7952314
Cited by
3
References
21
Citations by year
2026: 3
FWCI
5.64
Type
journal article
Language
en
Open access
Licence
Locations
  • ESMO Real World Data and Digital Oncology (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • PubMed Central (submittedVersion, cc-by-nc-nd)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Palliative and Oncologic Care
Keywords
artificial intelligence in nursing, nurse navigation, empathic AI, agentic AI, American Nurses Association Code of Ethics, care coordination, digital health tools, patient-clinician relationship

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