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Cited by 75Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Ethical considerations in human-centered AI: advancing oncology chatbots through large language models

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Chow, James C L, Li, Kay·2024·JMIR Bioinformatics and Biotechnology
DOI 10.2196/64406PMC PMC11579624

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Abstract

The integration of chatbots in oncology underscores the pressing need for human-centered artificial intelligence (AI) that addresses patient and family concerns with empathy and precision. Human-centered AI emphasizes ethical principles, empathy, and user-centric approaches, ensuring technology aligns with human values and needs. This review critically examines the ethical implications of using large language models (LLMs) like GPT-3 and GPT-4 (OpenAI) in oncology chatbots. It examines how these models replicate human-like language patterns, impacting the design of ethical AI systems. The paper identifies key strategies for ethically developing oncology chatbots, focusing on potential biases arising from extensive datasets and neural networks. Specific datasets, such as those sourced from predominantly Western medical literature and patient interactions, may introduce biases by overrepresenting certain demographic groups. Moreover, the training methodologies of LLMs, including fine-tuning processes, can exacerbate these biases, leading to outputs that may disproportionately favor affluent or Western populations while neglecting marginalized communities. By providing examples of biased outputs in oncology chatbots, the review highlights the ethical challenges LLMs present and the need for mitigation strategies. The study emphasizes integrating human-centric values into AI to mitigate these biases, ultimately advocating for the development of oncology chatbots that are aligned with ethical principles and capable of serving diverse patient populations equitably.

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Cited by

Sorted by citations

Authors
Chow, J. C. L., & Li, K.
Affiliations
  • University Health Network (CA)
  • University of Toronto (CA)
Corresponding author
Chow, James C L
ORCID
Venue
JMIR Bioinformatics and Biotechnology
Publisher
JMIR Publications Inc.
Published
2024-11-06
Volume
5
Pages
e64406
ISSN
2563-3570 (ISSN-L)
DOI
10.2196/64406
PMC
PMC11579624
OpenAlex
W4402830391
Cited by
75
References
23
Citations by year
2026: 30 · 2025: 42 · 2024: 3
FWCI
7.01
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JMIR Bioinformatics and Biotechnology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Canadian Institutes of Health Research (CIHR PCS-168296)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
Keywords
large language models, human-centered AI, AI ethics, algorithmic bias, GPT-3, GPT-4, fine-tuning, training data bias, health equity, marginalized communities, empathy in AI, cancer patient support

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