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Chatbot for health care and oncology applications using artificial intelligence and machine learning: systematic review

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Xu, Lu, Sanders, Leslie, Li, Kay, Chow, James C L·2021·JMIR Cancer
DOI 10.2196/27850PMC PMC8669585

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Abstract

Background: Chatbot 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. Objective: This 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. Methods: A 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. Results: Even 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. Conclusions: Further 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.

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  • Recent studies demonstrated that generative AI tools tailor educational content based on patient demographics, clinical context, and informational needs, including evaluations of ChatGPT and other LLMs responding to cancer-related questions and decision-making scenarios.

    “Recent studies further extend this work by demonstrating how generative AI tools tailor educational content based on patient demographics, clinical context, and informational needs, including evaluations of ChatGPT and other large language models (LLMs) responding to cancer-related questions and decision-making scenarios [22-25].”

    From Empathic AI for Patient-Centered Cancer Care: A Scoping Review of Patient Navigation, Support, and Clinical Practice (2026) · cites this paper as [25]

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  • Implementation success depends as much on sociotechnical context as on model performance.

    “This highlights implementation success dependent as much on sociotechnical context as on model performance [25].”

    From Empathic AI for Patient-Centered Cancer Care: A Scoping Review of Patient Navigation, Support, and Clinical Practice (2026) · cites this paper as [25]

  • Challenges remain around privacy, information accuracy, and integration into provider workflows for virtual health assistants.

    “However, challenges remain around privacy, information accuracy, and integration into provider workflows [25].”

    From Generative AI in medical practice: in-depth exploration of privacy and security challenges (2024) · cites this paper as [25]

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Sorted by citations

Authors
Xu, L., Sanders, L., Li, K., & Chow, J. C. L.
Affiliations
  • Western University (CA)
  • York University (CA)
  • University Health Network (CA)
Corresponding author
Chow, James C L
ORCID
Venue
JMIR Cancer
Publisher
JMIR Publications Inc.
Published
2021-11-29
Volume
7
Issue
4
Pages
e27850
ISSN
2369-1999 (ISSN-L)
DOI
10.2196/27850
PMC
PMC8669585
OpenAlex
W3200742808
Cited by
571
References
28
Citations by year
2026: 124 · 2025: 150 · 2024: 161 · 2023: 115 · 2022: 20 · 2021: 1
FWCI
6.04
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JMIR Cancer (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
Canadian Institutes of Health Research
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
chatbots, oncology, cancer therapy, cancer diagnosis, patient monitoring, patient support, clinical workflow efficiency, health promotion, ethical and regulatory issues, chatbot security, clinical implementation, artificial intelligence in healthcare

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