Evidence map›Paper›PMID 41868582›Full record

ReviewESMO real world data and digital oncology2026

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

T Girdwood, S Kheirinejad, P Kheirkhah, B White, R Davis, D Schwartz, A Shaban-Nejad

Abstract readReview
In one paragraph

Review in ESMO real world data and digital oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

T GirdwoodUniversity of Tennessee Health Science Center, College of Nursing, Memphis, USA.
S KheirinejadUniversity of Tennessee Health Science Center, Center for Biomedical Informatics, Department of Pediatrics, Memphis, USA.
P KheirkhahUniversity of Tennessee Health Science Center, Department of Radiation Oncology, Memphis, USA.
B WhiteUniversity of Tennessee Health Science Center, Center for Biomedical Informatics, Department of Pediatrics, Memphis, USA.
R DavisUniversity of Tennessee Health Science Center, Center for Biomedical Informatics, Department of Pediatrics, Memphis, USA.
D SchwartzUniversity of Tennessee Health Science Center, Department of Radiation Oncology, Memphis, USA.
A Shaban-NejadUniversity of Tennessee Health Science Center, Center for Biomedical Informatics, Department of Pediatrics, Memphis, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

artificial intelligencecancer carehuman-centered frameworknurse navigationunder-resourced

Identifiers

PMID41868582
PMCPMC13000476

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.