The Future of Intelligent Healthcare: A Systematic Analysis and Discussion on the Integration and Impact of Robots Using Large Language Models for Healthcare
arXiv:2411.03287 · doi:10.3390/robotics13080112
Abstract
The potential use of large language models (LLMs) in healthcare robotics can help address the significant demand put on healthcare systems around the world with respect to an aging demographic and a shortage of healthcare professionals. Even though LLMs have already been integrated into medicine to assist both clinicians and patients, the integration of LLMs within healthcare robots has not yet been explored for clinical settings. In this perspective paper, we investigate the groundbreaking developments in robotics and LLMs to uniquely identify the needed system requirements for designing health specific LLM based robots in terms of multi modal communication through human robot interactions (HRIs), semantic reasoning, and task planning. Furthermore, we discuss the ethical issues, open challenges, and potential future research directions for this emerging innovative field.
References in corpus (12)
- Learning Transferable Visual Models From Natural Language Supervision
- Robust Speech Recognition via Large-Scale Weak Supervision
- Understanding Large-Language Model (LLM)-powered Human-Robot Interaction
- MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering
- LaMI: Large Language Models for Multi-Modal Human-Robot Interaction
- Prompt Design and Engineering: Introduction and Advanced Methods
- Fairness-guided Few-shot Prompting for Large Language Models
- Large Language Models for Robotics: Opportunities, Challenges, and Perspectives
- Understanding LLMs: A Comprehensive Overview from Training to Inference
- Global AI Governance in Healthcare: A Cross-Jurisdictional Regulatory Analysis
- Natural Language Robot Programming: NLP integrated with autonomous robotic grasping
- Self-Influence Guided Data Reweighting for Language Model Pre-training