Mitigating Ethical Issues for Large Language Models in Oncology: A Systematic Review
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View abstract on PubMed
Summary
This summary is machine-generated.Large language models (LLMs) offer oncology benefits but raise ethical concerns. This review identifies six key challenges and evaluates technical solutions for responsible AI deployment in cancer care.
Area Of Science
- Artificial Intelligence in Oncology
- Medical Informatics
- Bioethics
Background
- Large language models (LLMs) show promise in oncology tasks like cancer staging.
- Ethical issues including data privacy, bias, and transparency hinder LLM adoption in high-stakes medical settings.
Purpose Of The Study
- To explore ethical challenges of LLM applications in oncology.
- To evaluate emerging techniques for addressing these ethical issues.
Main Methods
- Systematic review following PRISMA guidelines.
- Searched eight academic databases (Jan 2019-Dec 2024).
- Included 65 relevant publications from 4,319 retrieved articles.
Main Results
- Identified six prevalent ethical challenges: trust, equity, privacy, transparency, nonmaleficence, and accountability.
- Evaluated technical solutions to mitigate these ethical concerns.
- Summarized metrics for assessing solution effectiveness.
Conclusions
- Provides actionable recommendations for responsible LLM deployment in oncology.
- Aims to ensure ethical AI adherence and improve patient outcomes.
- Establishes a framework for ethical AI in oncology and identifies future research directions.
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