mlsanityData to 7 Oct 2026

Large language models in medicine: the potentials and pitfalls

arXiv 2309.00087 ↗First posted 31 Aug 2023cs.CL cs.AI cs.CYcited by 21

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Abstract (arXiv)

Large language models (LLMs) have been applied to tasks in healthcare, ranging from medical exam questions to responding to patient questions. With increasing institutional partnerships between companies producing LLMs and healthcare systems, real world clinical application is coming closer to reality. As these models gain traction, it is essential for healthcare practitioners to understand what LLMs are, their development, their current and potential applications, and the associated pitfalls when utilized in medicine. This review and accompanying tutorial aim to give an overview of these topics to aid healthcare practitioners in understanding the rapidly changing landscape of LLMs as applied to medicine.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 46 of its 99 references that are in the map. First posted on arXiv 31 Aug 2023.

Builds on

46 of 99 references.

References · 46

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 12; 2025: 4; 2026: 1.

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  1. 2023A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethicscited by 162
  2. 2023Large language models in healthcare and medical domain: A reviewcited by 126
  3. 2023A Survey of Large Language Models in Medicine: Progress, Application, and Challengecited by 123
  4. 2024A Comprehensive Survey of Large Language Models and Multimodal Large Language Models in Medicinecited by 45
  5. 2024Biomedical Visual Instruction Tuning with Clinician Preference Alignmentcited by 21
  6. 2025Exposing LLM User Privacy via Traffic Fingerprint Analysis: A Study of Privacy Risks in LLM Agent Interactionscited by 9
  7. 2023Large Language Models Illuminate a Progressive Pathway to Artificial Healthcare Assistant: A Reviewcited by 8
  8. 2024Generalist embedding models are better at short-context clinical semantic search than specialized embedding modelscited by 7
  9. 2024From Frege to chatGPT: Compositionality in language, cognition, and deep neural networkscited by 7
  10. 2025A Retrieval-Augmented Knowledge Mining Method with Deep Thinking LLMs for Biomedical Research and Clinical Supportcited by 7
  11. 2025Advancing the Scientific Method with Large Language Models: From Hypothesis to Discoverycited by 7
  12. 2024Best Practices for Large Language Models in Radiologycited by 5
9 more
  1. 2024High Throughput Phenotyping of Physician Notes with Large Language and Hybrid NLP Modelscited by 4
  2. 2025SpecPipe: Accelerating Pipeline Parallelism-based LLM Inference with Speculative Decodingcited by 4
  3. 2024Strategic Prompting for Conversational Tasks: A Comparative Analysis of Large Language Models Across Diverse Conversational Taskscited by 3
  4. 2024Enhancing Large Language Models with Domain-specific Retrieval Augment Generation: A Case Study on Long-form Consumer Health Question Answering in Ophthalmologycited by 2
  5. 2024Towards Transparency: Exploring LLM Trainings Datasets through Visual Topic Modeling and Semantic Framecited by 1
  6. 2024The Role of Language Models in Modern Healthcare: A Comprehensive Reviewcited by 1
  7. 2024Hybrid Student-Teacher Large Language Model Refinement for Cancer Toxicity Symptom Extractioncited by 0
  8. 2024Detecting Children with Autism Spectrum Disorder based on Script-Centric Behavior Understanding with Emotional Enhancementcited by 0
  9. 2026Perfecting Human-AI Interaction at Clinical Scale. Turning Production Signals into Safer, More Human Conversationscited by 0