ArticleJMIR bioinformatics and biotechnology2024
Ethical Considerations in Human-Centered AI: Advancing Oncology Chatbots Through Large Language Models.
Article in JMIR bioinformatics and biotechnology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- AI-driven troubleshooting for TrueBeam systems: Development and testing of a GPT-4.1 Chatbot.Technical innovations & patient support in radiation oncology · 2026Article
- An Acceptance Criteria Framework for Determining the Implementation Fit of Custom Large Language Models in Public Health Interventions.Journal of medical Internet research · 2026Article
- Portraying Ethical Risks of Medical AI: Mixed Methods Study From Connotation Definition to a Survey on Physicians' Cognition.Journal of medical Internet research · 2026Article
- Large Language Models for Breast and Cervical Cancers Communication: Mixed Methods Evaluation Study Assessing Linguistic Quality, Safety, and Accessibility.JMIR cancer · 2026Article
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- Performance of Large Language Models in Answering Healthcare Delivery Questions: A Quantitative Cross-Sectional Study.Health science reports · 2026Article
- Artificial intelligence in radiation treatment planning: a narrative review from automation to clinical decision support.Translational cancer research · 2026Review
- Empathic AI for Patient-Centered Cancer Care: A Scoping Review of Patient Navigation, Support, and Clinical Practice.JMIR cancer · 2026Article
- Transformation of lung cancer patient education paradigm driven by large language models: a multidimensional performance study.International journal of surgery (London, England) · 2026Article
- An Ubuntu-guided large language model framework for cognitive behavioral mental health dialogue.Frontiers in digital health · 2026Article
- Machine learning in cancer imaging for enhanced precision in diagnosis and therapy.Discover computing · 2026Review
- Large language models in emergency and critical care medicine: a comprehensive review of applications, challenges, and future directions.Burns & trauma · 2026Review
- A structured framework for effective and responsible generative artificial intelligence chatbot prompt engineering throughout the scientific process: a comprehensive guide for the health and medical researcher.Frontiers in artificial intelligence · 2026Review
- Prompt engineering for generative artificial intelligence chatbots in health research: A practical guide for traditional, complementary, and integrative medicine researchers.Integrative medicine research · 2025Article
- A case-specific psychological first aid AI recommendations app for rescue and first responders.European journal of psychotraumatology · 2025Article
- Evaluation of Cancer Survivors' Experience of Using AI-Based Conversational Tools: Qualitative Study.JMIR cancer · 2025Article
- A Prompt Engineering Framework for Large Language Model-Based Mental Health Chatbots: Conceptual Framework.JMIR mental health · 2025Article
- Ethical implications of using general-purpose LLMs in clinical settings: a comparative analysis of prompt engineering strategies and their impact on patient safety.BMC medical informatics and decision making · 2025Article
- Review
Corrections and comments
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2 authors.
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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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