# Large Language Models in Healthcare

arXiv: https://arxiv.org/abs/2503.04748 · HTML: https://mlsanity.com/paper/2503.04748/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2025.

## Built on by (most cited first, 6 of 6)

- [Second Opinion Matters: Towards Adaptive Clinical AI via the Consensus of Expert Model Ensemble](https://mlsanity.com/paper/2505.23075/) (2025)
- [To Adapt or not to Adapt, Rethinking the Value of Medical Knowledge-Aware Large Language Models](https://mlsanity.com/paper/2604.06854/) (2026)
- [Toward Workflow-Aware Benchmarking for Healthcare NLP Agents](https://mlsanity.com/paper/2609.00296/) (2026)
- [Haibu Mathematical-Medical Intelligent Agent:Enhancing Large Language Model Reliability in Medical Tasks via Verifiable Reasoning Chains](https://mlsanity.com/paper/2510.07748/) (2025)
- [Cognitive bias in LLM reasoning compromises interpretation of clinical oncology notes](https://mlsanity.com/paper/2511.20680/) (2025)
- [From Single Chatbots to Governed Agent Ecosystems: An Agentic AI Pattern Catalogue and Orchestration Framework for Mission-Critical Hospital Information Management Systems](https://mlsanity.com/paper/2608.07627/) (2026)

## Abstract (arXiv)

Large language models (LLMs) hold promise for transforming healthcare, from streamlining administrative and clinical workflows to enriching patient engagement and advancing clinical decision-making. However, their successful integration requires rigorous development, adaptation, and evaluation strategies tailored to clinical needs. In this Review, we highlight recent advancements, explore emerging opportunities for LLM-driven innovation, and propose a framework for their responsible implementation in healthcare settings. We examine strategies for adapting LLMs to domain-specific healthcare tasks, such as fine-tuning, prompt engineering, and multimodal integration with electronic health records. We also summarize various evaluation metrics tailored to healthcare, addressing clinical accuracy, fairness, robustness, and patient outcomes. Furthermore, we discuss the challenges associated with deploying LLMs in healthcare--including data privacy, bias mitigation, regulatory compliance, and computational sustainability--and underscore the need for interdisciplinary collaboration. Finally, these challenges present promising future research directions for advancing LLM implementation in clinical settings and healthcare.