# LLMs-Healthcare : Current Applications and Challenges of Large Language Models in various Medical Specialties

arXiv: https://arxiv.org/abs/2311.12882 · HTML: https://mlsanity.com/paper/2311.12882/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 38 references that are in the map.
- First posted on arXiv 28 Oct 2023.

## Builds on (most cited first, 10 of 10)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://mlsanity.com/paper/2111.01243/) (2021)
- [Detecting cognitive decline using speech only: The ADReSSo Challenge](https://mlsanity.com/paper/2104.09356/) (2021)
- [Understanding the Benefits and Challenges of Using Large Language Model-based Conversational Agents for Mental Well-being Support](https://mlsanity.com/paper/2307.15810/) (2023)
- [Language Models are Few-shot Learners for Prognostic Prediction](https://mlsanity.com/paper/2302.12692/) (2023)
- [The Capability of Large Language Models to Measure Psychiatric Functioning](https://mlsanity.com/paper/2308.01834/) (2023)
- [A Comparative Study of Open-Source Large Language Models, GPT-4 and Claude 2: Multiple-Choice Test Taking in Nephrology](https://mlsanity.com/paper/2308.04709/) (2023)
- [Exploring Multimodal Approaches for Alzheimer's Disease Detection Using Patient Speech Transcript and Audio Data](https://mlsanity.com/paper/2307.02514/) (2023)
- [Large language models improve Alzheimer's disease diagnosis using multi-modality data](https://mlsanity.com/paper/2305.19280/) (2023)

## Built on by (most cited first, 10 of 10)

- [Med-R2: Crafting Trustworthy LLM Physicians via Retrieval and Reasoning of Evidence-Based Medicine](https://mlsanity.com/paper/2501.11885/) (2025)
- [Learning Clinical Representations Under Systematic Distribution Shift](https://mlsanity.com/paper/2603.07348/) (2026)
- [Discriminative Representation Learning for Clinical Prediction](https://mlsanity.com/paper/2603.20921/) (2026)
- [A Survey for Large Language Models in Biomedicine](https://mlsanity.com/paper/2409.00133/) (2024)
- [Development of Application-Specific Large Language Models to Facilitate Research Ethics Review](https://mlsanity.com/paper/2501.10741/) (2025)
- [Optimizing Privacy-Preserving Primitives to Support LLM-Scale Applications](https://mlsanity.com/paper/2509.25072/) (2025)
- [Uncertainty-Aware Foundation Models for Clinical Data](https://mlsanity.com/paper/2604.04175/) (2026)
- [First, Do No Harm (With LLMs): Mitigating Racial Bias via Agentic Workflows](https://mlsanity.com/paper/2604.18038/) (2026)
- [Can AI Guess What You Know? Performance Comparison of Large Language Models for Human Domain Knowledge Estimation From Communication Logs](https://mlsanity.com/paper/2605.22971/) (2026)
- [M2G-LLM: Enhancing Clinical Prediction via Multimodal Graph Reasoning and LLM Context Injection](https://mlsanity.com/paper/2609.21164/) (2026)

## Abstract (arXiv)

We aim to present a comprehensive overview of the latest advancements in utilizing Large Language Models (LLMs) within the healthcare sector, emphasizing their transformative impact across various medical domains. LLMs have become pivotal in supporting healthcare, including physicians, healthcare providers, and patients. Our review provides insight into the applications of Large Language Models (LLMs) in healthcare, specifically focusing on diagnostic and treatment-related functionalities. We shed light on how LLMs are applied in cancer care, dermatology, dental care, neurodegenerative disorders, and mental health, highlighting their innovative contributions to medical diagnostics and patient care. Throughout our analysis, we explore the challenges and opportunities associated with integrating LLMs in healthcare, recognizing their potential across various medical specialties despite existing limitations. Additionally, we offer an overview of handling diverse data types within the medical field.