Evidence map›Paper›PMID 39378420›Full record

ArticleJMIR research protocols2024

Investigating Data Diversity and Model Robustness of AI Applications in Palliative Care and Hospice: Protocol for Scoping Review.

Selen Bozkurt, Soraya Fereydooni, Irem Kar, Catherine Diop Chalmers, Sharon L Leslie, Ravi Pathak, Anne Walling, Charlotta Lindvall, Karl Lorenz, Tammie Quest and 2 more

Abstract read
In one paragraph

Article in JMIR research protocols, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Large language models in neuro-ophthalmology diseases: ChatGPTInternational journal of ophthalmology · 2025
    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

12 authors.

Selen BozkurtDepartment of Biomedical Informatics, Emory University, Atlanta, GA, United States.ORCID 0000-0003-1234-2158
Soraya FereydooniYale School of Medicine, New Haven, CT, United States.ORCID 0000-0002-1264-6461
Irem KarDepartment of Biostatistics, Ankara University, Ankara, Turkey.ORCID 0000-0002-1747-4312
Catherine Diop ChalmersWinship Cancer Institute, Emory University, Atlanta, GA, United States.ORCID 0009-0006-5582-3372
Sharon L LeslieWoodruff Health Sciences Center Library, Emory University, Atlanta, GA, United States.ORCID 0000-0002-6597-977X
Ravi PathakDivision of Palliative Medicine, Department of Family and Preventive Medicine, Emory University, Atlanta, GA, United States.ORCID 0009-0008-6623-2791
Anne WallingDepartment of Medicine, Veterans Affairs Greater Los Angeles Health System, Los Angeles, CA, United States.ORCID 0000-0001-6026-3249
Charlotta LindvallHarvard Medical School, Boston, MA, United States.ORCID 0000-0003-2090-2039
Karl LorenzPrimary Care and Population Health, School of Medicine, Stanford University, Stanford, CA, United States.ORCID 0000-0002-2468-2566
Tammie QuestDivision of Palliative Medicine, Department of Family and Preventive Medicine, Emory University, Atlanta, GA, United States.ORCID 0000-0002-9448-4688
Karleen Giannitrapani *Primary Care and Population Health, School of Medicine, Stanford University, Stanford, CA, United States.ORCID 0000-0003-0987-6204
Dio Kavalieratos *Division of Palliative Medicine, Department of Family and Preventive Medicine, Emory University, Atlanta, GA, United States.ORCID 0000-0001-5283-0792

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) has become a pivotal element in health care, leading to significant advancements across various medical domains, including palliative care and hospice services. These services focus on improving the quality of life for patients with life-limiting illnesses, and AI's ability to process complex datasets can enhance decision-making and personalize care in these sensitive settings. However, incorporating AI into palliative and hospice care requires careful examination to ensure it reflects the multifaceted nature of these settings.

objectiveThis scoping review aims to systematically map the landscape of AI in palliative care and hospice settings, focusing on the data diversity and model robustness. The goal is to understand AI's role, its clinical integration, and the transparency of its development, ultimately providing a foundation for developing AI applications that adhere to established ethical guidelines and principles.

methodsOur scoping review involves six stages: (1) identifying the research question; (2) identifying relevant studies; (3) study selection; (4) charting the data; (5) collating, summarizing, and reporting the results; and (6) consulting with stakeholders. Searches were conducted across databases including MEDLINE through PubMed, Embase.com, IEEE Xplore, ClinicalTrials.gov, and Web of Science Core Collection, covering studies from the inception of each database up to November 1, 2023. We used a comprehensive set of search terms to capture relevant studies, and non-English records were excluded if their abstracts were not in English. Data extraction will follow a systematic approach, and stakeholder consultations will refine the findings.

resultsThe electronic database searches conducted in November 2023 resulted in 4614 studies. After removing duplicates, 330 studies were selected for full-text review to determine their eligibility based on predefined criteria. The extracted data will be organized into a table to aid in crafting a narrative summary. The review is expected to be completed by May 2025.

conclusionsThis scoping review will advance the understanding of AI in palliative care and hospice, focusing on data diversity and model robustness. It will identify gaps and guide future research, contributing to the development of ethically responsible and effective AI applications in these settings. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/56353.

Indexed as

Artificial IntelligenceHospice CarePalliative CareHumansScoping Reviews as TopicAIartificial intelligenceclinical settingsdata diversitydecision supportdisseminationend-of-life careethical frameworkshospicehospice environmentsmethodologymodel robustnesspalliative carethematic analysis

Identifiers

PMID39378420
PMCPMC11496913

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.