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 (30)
- Learning Transferable Visual Models From Natural Language Supervision
- Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing
- PaLM: Scaling Language Modeling with Pathways
- Robust Speech Recognition via Large-Scale Weak Supervision
- A Comprehensive Overview of Large Language Models
- Measuring Massive Multitask Language Understanding
- Understanding Large-Language Model (LLM)-powered Human-Robot Interaction
- Large Language Models as Zero-Shot Human Models for Human-Robot Interaction
- MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering
- Large Language Models Can Be Strong Differentially Private Learners
- LaMI: Large Language Models for Multi-Modal Human-Robot Interaction
- Beyond Memorization: Violating Privacy Via Inference with Large Language Models
- Prompt Design and Engineering: Introduction and Advanced Methods
- Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets
- Efficient Streaming Language Models with Attention Sinks
- Fairness-guided Few-shot Prompting for Large Language Models
- On the Generalization Mystery in Deep Learning
- Large Language Models for Robotics: Opportunities, Challenges, and Perspectives
- MM-LLMs: Recent Advances in MultiModal Large Language Models
- Understanding LLMs: A Comprehensive Overview from Training to Inference
- Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory
- Grounding Complex Natural Language Commands for Temporal Tasks in Unseen Environments
- Advantages of Multimodal versus Verbal-Only Robot-to-Human Communication with an Anthropomorphic Robotic Mock Driver
- User-LLM: Efficient LLM Contextualization with User Embeddings
- Global AI Governance in Healthcare: A Cross-Jurisdictional Regulatory Analysis
- Privacy-Preserving Instructions for Aligning Large Language Models
- Natural Language Robot Programming: NLP integrated with autonomous robotic grasping
- Self-Influence Guided Data Reweighting for Language Model Pre-training
- Skill-Based Few-Shot Selection for In-Context Learning
- ADaPT: As-Needed Decomposition and Planning with Language Models