# Digital Twin Ecosystem for Oncology Clinical Operations

arXiv: https://arxiv.org/abs/2409.17650 · HTML: https://mlsanity.com/paper/2409.17650/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 43 references that are in the map.
- First posted on arXiv 26 Sep 2024.

## Builds on (most cited first, 11 of 11)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [A Survey on In-context Learning](https://mlsanity.com/paper/2301.00234/) (2022)
- [Automatic Prompt Optimization with "Gradient Descent" and Beam Search](https://mlsanity.com/paper/2305.03495/) (2023)
- [Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery](https://mlsanity.com/paper/2302.03668/) (2023)
- [Conversational Health Agents: A Personalized LLM-Powered Agent Framework](https://mlsanity.com/paper/2310.02374/) (2023)
- [Enhancing Clinical Efficiency through LLM: Discharge Note Generation for Cardiac Patients](https://mlsanity.com/paper/2404.05144/) (2024)
- [Multimodal Fusion of EHR in Structures and Semantics: Integrating Clinical Records and Notes with Hypergraph and LLM](https://mlsanity.com/paper/2403.08818/) (2024)
- [Advancing Healthcare Automation: Multi-Agent System for Medical Necessity Justification](https://mlsanity.com/paper/2404.17977/) (2024)
- [Retrieving Evidence from EHRs with LLMs: Possibilities and Challenges](https://mlsanity.com/paper/2309.04550/) (2023)
- [Automated Clinical Data Extraction with Knowledge Conditioned LLMs](https://mlsanity.com/paper/2406.18027/) (2024)

## Abstract (arXiv)

Artificial Intelligence (AI) and Large Language Models (LLMs) hold significant promise in revolutionizing healthcare, especially in clinical applications. Simultaneously, Digital Twin technology, which models and simulates complex systems, has gained traction in enhancing patient care. However, despite the advances in experimental clinical settings, the potential of AI and digital twins to streamline clinical operations remains largely untapped. This paper introduces a novel digital twin framework specifically designed to enhance oncology clinical operations. We propose the integration of multiple specialized digital twins, such as the Medical Necessity Twin, Care Navigator Twin, and Clinical History Twin, to enhance workflow efficiency and personalize care for each patient based on their unique data. Furthermore, by synthesizing multiple data sources and aligning them with the National Comprehensive Cancer Network (NCCN) guidelines, we create a dynamic Cancer Care Path, a continuously evolving knowledge base that enables these digital twins to provide precise, tailored clinical recommendations.