mlsanityData to 7 Oct 2026

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

arXiv 2311.12882 ↗First posted 28 Oct 2023cs.CL cs.AIcited by 10

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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.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 10 of its 38 references that are in the map. First posted on arXiv 28 Oct 2023.

Builds on

10 of 38 references.

References · 10

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Large Language Models Encode Clinical Knowledgecited by 1,807
  3. 2021Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Surveycited by 457
  4. 2021Detecting cognitive decline using speech only: The ADReSSo Challengecited by 81
  5. 2023Understanding the Benefits and Challenges of Using Large Language Model-based Conversational Agents for Mental Well-being Supportcited by 75
  6. 2023Language Models are Few-shot Learners for Prognostic Predictioncited by 37
  7. 2023A Comparative Study of Open-Source Large Language Models, GPT-4 and Claude 2: Multiple-Choice Test Taking in Nephrologycited by 26
  8. 2023The Capability of Large Language Models to Measure Psychiatric Functioningcited by 25
  9. 2023Exploring Multimodal Approaches for Alzheimer's Disease Detection Using Patient Speech Transcript and Audio Datacited by 13
  10. 2023Large language models improve Alzheimer's disease diagnosis using multi-modality datacited by 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 6.

10 papers.

  1. 2025Med-R2: Crafting Trustworthy LLM Physicians via Retrieval and Reasoning of Evidence-Based Medicinecited by 5
  2. 2026Learning Clinical Representations Under Systematic Distribution Shiftcited by 2
  3. 2026Discriminative Representation Learning for Clinical Predictioncited by 2
  4. 2024A Survey for Large Language Models in Biomedicinecited by 1
  5. 2025Development of Application-Specific Large Language Models to Facilitate Research Ethics Reviewcited by 1
  6. 2025Optimizing Privacy-Preserving Primitives to Support LLM-Scale Applicationscited by 1
  7. 2026Uncertainty-Aware Foundation Models for Clinical Datacited by 1
  8. 2026First, Do No Harm (With LLMs): Mitigating Racial Bias via Agentic Workflowscited by 0
  9. 2026Can AI Guess What You Know? Performance Comparison of Large Language Models for Human Domain Knowledge Estimation From Communication Logscited by 0
  10. 2026M2G-LLM: Enhancing Clinical Prediction via Multimodal Graph Reasoning and LLM Context Injectioncited by 0