Evidence map›Paper›PMID 34847056›Full record

ReviewJMIR cancer2021

Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review.

Lu Xu, Leslie Sanders, Kay Li, James C L Chow

Registry-linked trialAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in JMIR cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07410689 (SAFE.AI), which is not on this map. Cited by 184 papers, 9 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
184citing papers in PubMed, 9 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.

NCT07410689 nanot yet recruitingnot on this mapstarted 2026, after this paper: background citation

SAFE.AI: Developing and Testing an AI-based Hybrid Chatbot for Financial Empowerment in Rural Cancer Care

TypeinterventionalSponsorUniversity of UtahRan2026 to 2027Enrolled60ConditionsCancer, Financial ToxicityArmsRule-Based Chatbot, Hybrid Chatbot
3 · Its place in the literature

Who cites it

184 citing papers in PubMed, 9 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Pooled it
  7. Pooled it
  8. Pooled it
  9. Pooled it
  10. Trial
  11. Trial
  12. Trial
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Review
  19. Article
  20. Article

124 more citing papers are in PubMed but not listed here.

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

4 authors.

Lu XuInstitute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-8485-9968
Leslie SandersDepartment of Humanities, York University, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-6990-7722
Kay LiDepartment of English, York University, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-5765-1635
James C L ChowDepartment of Medical Physics, Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0003-4202-4855

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChatbot is a timely topic applied in various fields, including medicine and health care, for human-like knowledge transfer and communication. Machine learning, a subset of artificial intelligence, has been proven particularly applicable in health care, with the ability for complex dialog management and conversational flexibility.

objectiveThis review article aims to report on the recent advances and current trends in chatbot technology in medicine. A brief historical overview, along with the developmental progress and design characteristics, is first introduced. The focus will be on cancer therapy, with in-depth discussions and examples of diagnosis, treatment, monitoring, patient support, workflow efficiency, and health promotion. In addition, this paper will explore the limitations and areas of concern, highlighting ethical, moral, security, technical, and regulatory standards and evaluation issues to explain the hesitancy in implementation.

methodsA search of the literature published in the past 20 years was conducted using the IEEE Xplore, PubMed, Web of Science, Scopus, and OVID databases. The screening of chatbots was guided by the open-access Botlist directory for health care components and further divided according to the following criteria: diagnosis, treatment, monitoring, support, workflow, and health promotion.

resultsEven after addressing these issues and establishing the safety or efficacy of chatbots, human elements in health care will not be replaceable. Therefore, chatbots have the potential to be integrated into clinical practice by working alongside health practitioners to reduce costs, refine workflow efficiencies, and improve patient outcomes. Other applications in pandemic support, global health, and education are yet to be fully explored.

conclusionsFurther research and interdisciplinary collaboration could advance this technology to dramatically improve the quality of care for patients, rebalance the workload for clinicians, and revolutionize the practice of medicine.

Indexed as

artificial intelligencecancer therapychatbotcommunicationdiagnosisethicshealthmachine learningmedical biophysicsmedicinemobile phone

Identifiers

PMID34847056
PMCPMC8669585

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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.