Ethical Considerations in Human-Centered AI: Advancing Oncology Chatbots Through Large Language Models.

Area of Science:

• Artificial Intelligence in Medicine
• Bioethics
• Oncology Patient Support

Background:

• The increasing use of AI chatbots in oncology necessitates a focus on human-centered design.
• Ethical AI must prioritize empathy, user needs, and alignment with human values.

Purpose of the Study:

• To critically review the ethical implications of using large language models (LLMs) in oncology chatbots.
• To identify and address potential biases in AI-driven oncology patient support systems.

Main Methods:

• Literature review examining ethical considerations of LLMs (e.g., GPT-3, GPT-4) in oncology.
• Analysis of how LLM training data and methodologies can introduce demographic and cultural biases.
• Identification of strategies for mitigating bias in AI development.

Main Results:

• LLMs can replicate human-like language, posing ethical challenges in AI design.
• Biases in training datasets (e.g., Western medical literature) can lead to inequitable AI outputs.
• Fine-tuning processes may exacerbate existing biases, potentially marginalizing certain populations.

Conclusions:

• Developing ethical oncology chatbots requires integrating human-centric values to mitigate LLM biases.
• Mitigation strategies are crucial for creating AI systems that serve diverse patient populations equitably.
• Future AI development must prioritize ethical principles and inclusivity in healthcare applications.